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diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/__init__.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..56bfb14afdfba168ba2e230c41406799841f6a07
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/__init__.py
@@ -0,0 +1,133 @@
+from networkx.algorithms.assortativity import *
+from networkx.algorithms.asteroidal import *
+from networkx.algorithms.boundary import *
+from networkx.algorithms.broadcasting import *
+from networkx.algorithms.bridges import *
+from networkx.algorithms.chains import *
+from networkx.algorithms.centrality import *
+from networkx.algorithms.chordal import *
+from networkx.algorithms.cluster import *
+from networkx.algorithms.clique import *
+from networkx.algorithms.communicability_alg import *
+from networkx.algorithms.components import *
+from networkx.algorithms.coloring import *
+from networkx.algorithms.core import *
+from networkx.algorithms.covering import *
+from networkx.algorithms.cycles import *
+from networkx.algorithms.cuts import *
+from networkx.algorithms.d_separation import *
+from networkx.algorithms.dag import *
+from networkx.algorithms.distance_measures import *
+from networkx.algorithms.distance_regular import *
+from networkx.algorithms.dominance import *
+from networkx.algorithms.dominating import *
+from networkx.algorithms.efficiency_measures import *
+from networkx.algorithms.euler import *
+from networkx.algorithms.graphical import *
+from networkx.algorithms.hierarchy import *
+from networkx.algorithms.hybrid import *
+from networkx.algorithms.link_analysis import *
+from networkx.algorithms.link_prediction import *
+from networkx.algorithms.lowest_common_ancestors import *
+from networkx.algorithms.isolate import *
+from networkx.algorithms.matching import *
+from networkx.algorithms.minors import *
+from networkx.algorithms.mis import *
+from networkx.algorithms.moral import *
+from networkx.algorithms.non_randomness import *
+from networkx.algorithms.operators import *
+from networkx.algorithms.planarity import *
+from networkx.algorithms.planar_drawing import *
+from networkx.algorithms.polynomials import *
+from networkx.algorithms.reciprocity import *
+from networkx.algorithms.regular import *
+from networkx.algorithms.richclub import *
+from networkx.algorithms.shortest_paths import *
+from networkx.algorithms.similarity import *
+from networkx.algorithms.graph_hashing import *
+from networkx.algorithms.simple_paths import *
+from networkx.algorithms.smallworld import *
+from networkx.algorithms.smetric import *
+from networkx.algorithms.structuralholes import *
+from networkx.algorithms.sparsifiers import *
+from networkx.algorithms.summarization import *
+from networkx.algorithms.swap import *
+from networkx.algorithms.time_dependent import *
+from networkx.algorithms.traversal import *
+from networkx.algorithms.triads import *
+from networkx.algorithms.vitality import *
+from networkx.algorithms.voronoi import *
+from networkx.algorithms.walks import *
+from networkx.algorithms.wiener import *
+
+# Make certain subpackages available to the user as direct imports from
+# the `networkx` namespace.
+from networkx.algorithms import approximation
+from networkx.algorithms import assortativity
+from networkx.algorithms import bipartite
+from networkx.algorithms import node_classification
+from networkx.algorithms import centrality
+from networkx.algorithms import chordal
+from networkx.algorithms import cluster
+from networkx.algorithms import clique
+from networkx.algorithms import components
+from networkx.algorithms import connectivity
+from networkx.algorithms import community
+from networkx.algorithms import coloring
+from networkx.algorithms import flow
+from networkx.algorithms import isomorphism
+from networkx.algorithms import link_analysis
+from networkx.algorithms import lowest_common_ancestors
+from networkx.algorithms import operators
+from networkx.algorithms import shortest_paths
+from networkx.algorithms import tournament
+from networkx.algorithms import traversal
+from networkx.algorithms import tree
+
+# Make certain functions from some of the previous subpackages available
+# to the user as direct imports from the `networkx` namespace.
+from networkx.algorithms.bipartite import complete_bipartite_graph
+from networkx.algorithms.bipartite import is_bipartite
+from networkx.algorithms.bipartite import projected_graph
+from networkx.algorithms.connectivity import all_pairs_node_connectivity
+from networkx.algorithms.connectivity import all_node_cuts
+from networkx.algorithms.connectivity import average_node_connectivity
+from networkx.algorithms.connectivity import edge_connectivity
+from networkx.algorithms.connectivity import edge_disjoint_paths
+from networkx.algorithms.connectivity import k_components
+from networkx.algorithms.connectivity import k_edge_components
+from networkx.algorithms.connectivity import k_edge_subgraphs
+from networkx.algorithms.connectivity import k_edge_augmentation
+from networkx.algorithms.connectivity import is_k_edge_connected
+from networkx.algorithms.connectivity import minimum_edge_cut
+from networkx.algorithms.connectivity import minimum_node_cut
+from networkx.algorithms.connectivity import node_connectivity
+from networkx.algorithms.connectivity import node_disjoint_paths
+from networkx.algorithms.connectivity import stoer_wagner
+from networkx.algorithms.flow import capacity_scaling
+from networkx.algorithms.flow import cost_of_flow
+from networkx.algorithms.flow import gomory_hu_tree
+from networkx.algorithms.flow import max_flow_min_cost
+from networkx.algorithms.flow import maximum_flow
+from networkx.algorithms.flow import maximum_flow_value
+from networkx.algorithms.flow import min_cost_flow
+from networkx.algorithms.flow import min_cost_flow_cost
+from networkx.algorithms.flow import minimum_cut
+from networkx.algorithms.flow import minimum_cut_value
+from networkx.algorithms.flow import network_simplex
+from networkx.algorithms.isomorphism import could_be_isomorphic
+from networkx.algorithms.isomorphism import fast_could_be_isomorphic
+from networkx.algorithms.isomorphism import faster_could_be_isomorphic
+from networkx.algorithms.isomorphism import is_isomorphic
+from networkx.algorithms.isomorphism.vf2pp import *
+from networkx.algorithms.tree.branchings import maximum_branching
+from networkx.algorithms.tree.branchings import maximum_spanning_arborescence
+from networkx.algorithms.tree.branchings import minimum_branching
+from networkx.algorithms.tree.branchings import minimum_spanning_arborescence
+from networkx.algorithms.tree.branchings import ArborescenceIterator
+from networkx.algorithms.tree.coding import *
+from networkx.algorithms.tree.decomposition import *
+from networkx.algorithms.tree.mst import *
+from networkx.algorithms.tree.operations import *
+from networkx.algorithms.tree.recognition import *
+from networkx.algorithms.tournament import is_tournament
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/__init__.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0b401583eb2a31ca600efd9cfdf43839e098a28
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/__init__.py
@@ -0,0 +1,26 @@
+"""Approximations of graph properties and Heuristic methods for optimization.
+
+The functions in this class are not imported into the top-level ``networkx``
+namespace so the easiest way to use them is with::
+
+    >>> from networkx.algorithms import approximation
+
+Another option is to import the specific function with
+``from networkx.algorithms.approximation import function_name``.
+
+"""
+
+from networkx.algorithms.approximation.clustering_coefficient import *
+from networkx.algorithms.approximation.clique import *
+from networkx.algorithms.approximation.connectivity import *
+from networkx.algorithms.approximation.distance_measures import *
+from networkx.algorithms.approximation.dominating_set import *
+from networkx.algorithms.approximation.kcomponents import *
+from networkx.algorithms.approximation.matching import *
+from networkx.algorithms.approximation.ramsey import *
+from networkx.algorithms.approximation.steinertree import *
+from networkx.algorithms.approximation.traveling_salesman import *
+from networkx.algorithms.approximation.treewidth import *
+from networkx.algorithms.approximation.vertex_cover import *
+from networkx.algorithms.approximation.maxcut import *
+from networkx.algorithms.approximation.density import *
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/clique.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/clique.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed0f3506369046c749d118c4264afeb4f054f2cd
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/clique.py
@@ -0,0 +1,259 @@
+"""Functions for computing large cliques and maximum independent sets."""
+
+import networkx as nx
+from networkx.algorithms.approximation import ramsey
+from networkx.utils import not_implemented_for
+
+__all__ = [
+    "clique_removal",
+    "max_clique",
+    "large_clique_size",
+    "maximum_independent_set",
+]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def maximum_independent_set(G):
+    """Returns an approximate maximum independent set.
+
+    Independent set or stable set is a set of vertices in a graph, no two of
+    which are adjacent. That is, it is a set I of vertices such that for every
+    two vertices in I, there is no edge connecting the two. Equivalently, each
+    edge in the graph has at most one endpoint in I. The size of an independent
+    set is the number of vertices it contains [1]_.
+
+    A maximum independent set is a largest independent set for a given graph G
+    and its size is denoted $\\alpha(G)$. The problem of finding such a set is called
+    the maximum independent set problem and is an NP-hard optimization problem.
+    As such, it is unlikely that there exists an efficient algorithm for finding
+    a maximum independent set of a graph.
+
+    The Independent Set algorithm is based on [2]_.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    Returns
+    -------
+    iset : Set
+        The apx-maximum independent set
+
+    Examples
+    --------
+    >>> G = nx.path_graph(10)
+    >>> nx.approximation.maximum_independent_set(G)
+    {0, 2, 4, 6, 9}
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+
+    Notes
+    -----
+    Finds the $O(|V|/(log|V|)^2)$ apx of independent set in the worst case.
+
+    References
+    ----------
+    .. [1] `Wikipedia: Independent set
+        <https://en.wikipedia.org/wiki/Independent_set_(graph_theory)>`_
+    .. [2] Boppana, R., & Halldórsson, M. M. (1992).
+       Approximating maximum independent sets by excluding subgraphs.
+       BIT Numerical Mathematics, 32(2), 180–196. Springer.
+    """
+    iset, _ = clique_removal(G)
+    return iset
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def max_clique(G):
+    r"""Find the Maximum Clique
+
+    Finds the $O(|V|/(log|V|)^2)$ apx of maximum clique/independent set
+    in the worst case.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    Returns
+    -------
+    clique : set
+        The apx-maximum clique of the graph
+
+    Examples
+    --------
+    >>> G = nx.path_graph(10)
+    >>> nx.approximation.max_clique(G)
+    {8, 9}
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+
+    Notes
+    -----
+    A clique in an undirected graph G = (V, E) is a subset of the vertex set
+    `C \subseteq V` such that for every two vertices in C there exists an edge
+    connecting the two. This is equivalent to saying that the subgraph
+    induced by C is complete (in some cases, the term clique may also refer
+    to the subgraph).
+
+    A maximum clique is a clique of the largest possible size in a given graph.
+    The clique number `\omega(G)` of a graph G is the number of
+    vertices in a maximum clique in G. The intersection number of
+    G is the smallest number of cliques that together cover all edges of G.
+
+    https://en.wikipedia.org/wiki/Maximum_clique
+
+    References
+    ----------
+    .. [1] Boppana, R., & Halldórsson, M. M. (1992).
+        Approximating maximum independent sets by excluding subgraphs.
+        BIT Numerical Mathematics, 32(2), 180–196. Springer.
+        doi:10.1007/BF01994876
+    """
+    # finding the maximum clique in a graph is equivalent to finding
+    # the independent set in the complementary graph
+    cgraph = nx.complement(G)
+    iset, _ = clique_removal(cgraph)
+    return iset
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def clique_removal(G):
+    r"""Repeatedly remove cliques from the graph.
+
+    Results in a $O(|V|/(\log |V|)^2)$ approximation of maximum clique
+    and independent set. Returns the largest independent set found, along
+    with found maximal cliques.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    Returns
+    -------
+    max_ind_cliques : (set, list) tuple
+        2-tuple of Maximal Independent Set and list of maximal cliques (sets).
+
+    Examples
+    --------
+    >>> G = nx.path_graph(10)
+    >>> nx.approximation.clique_removal(G)
+    ({0, 2, 4, 6, 9}, [{0, 1}, {2, 3}, {4, 5}, {6, 7}, {8, 9}])
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+
+    References
+    ----------
+    .. [1] Boppana, R., & Halldórsson, M. M. (1992).
+        Approximating maximum independent sets by excluding subgraphs.
+        BIT Numerical Mathematics, 32(2), 180–196. Springer.
+    """
+    graph = G.copy()
+    c_i, i_i = ramsey.ramsey_R2(graph)
+    cliques = [c_i]
+    isets = [i_i]
+    while graph:
+        graph.remove_nodes_from(c_i)
+        c_i, i_i = ramsey.ramsey_R2(graph)
+        if c_i:
+            cliques.append(c_i)
+        if i_i:
+            isets.append(i_i)
+    # Determine the largest independent set as measured by cardinality.
+    maxiset = max(isets, key=len)
+    return maxiset, cliques
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def large_clique_size(G):
+    """Find the size of a large clique in a graph.
+
+    A *clique* is a subset of nodes in which each pair of nodes is
+    adjacent. This function is a heuristic for finding the size of a
+    large clique in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    k: integer
+       The size of a large clique in the graph.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(10)
+    >>> nx.approximation.large_clique_size(G)
+    2
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+
+    Notes
+    -----
+    This implementation is from [1]_. Its worst case time complexity is
+    :math:`O(n d^2)`, where *n* is the number of nodes in the graph and
+    *d* is the maximum degree.
+
+    This function is a heuristic, which means it may work well in
+    practice, but there is no rigorous mathematical guarantee on the
+    ratio between the returned number and the actual largest clique size
+    in the graph.
+
+    References
+    ----------
+    .. [1] Pattabiraman, Bharath, et al.
+       "Fast Algorithms for the Maximum Clique Problem on Massive Graphs
+       with Applications to Overlapping Community Detection."
+       *Internet Mathematics* 11.4-5 (2015): 421--448.
+       <https://doi.org/10.1080/15427951.2014.986778>
+
+    See also
+    --------
+
+    :func:`networkx.algorithms.approximation.clique.max_clique`
+        A function that returns an approximate maximum clique with a
+        guarantee on the approximation ratio.
+
+    :mod:`networkx.algorithms.clique`
+        Functions for finding the exact maximum clique in a graph.
+
+    """
+    degrees = G.degree
+
+    def _clique_heuristic(G, U, size, best_size):
+        if not U:
+            return max(best_size, size)
+        u = max(U, key=degrees)
+        U.remove(u)
+        N_prime = {v for v in G[u] if degrees[v] >= best_size}
+        return _clique_heuristic(G, U & N_prime, size + 1, best_size)
+
+    best_size = 0
+    nodes = (u for u in G if degrees[u] >= best_size)
+    for u in nodes:
+        neighbors = {v for v in G[u] if degrees[v] >= best_size}
+        best_size = _clique_heuristic(G, neighbors, 1, best_size)
+    return best_size
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/clustering_coefficient.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/clustering_coefficient.py
new file mode 100644
index 0000000000000000000000000000000000000000..545fc65533b8d8f44b35498aa7129c97efc0bc52
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/clustering_coefficient.py
@@ -0,0 +1,71 @@
+import networkx as nx
+from networkx.utils import not_implemented_for, py_random_state
+
+__all__ = ["average_clustering"]
+
+
+@not_implemented_for("directed")
+@py_random_state(2)
+@nx._dispatchable(name="approximate_average_clustering")
+def average_clustering(G, trials=1000, seed=None):
+    r"""Estimates the average clustering coefficient of G.
+
+    The local clustering of each node in `G` is the fraction of triangles
+    that actually exist over all possible triangles in its neighborhood.
+    The average clustering coefficient of a graph `G` is the mean of
+    local clusterings.
+
+    This function finds an approximate average clustering coefficient
+    for G by repeating `n` times (defined in `trials`) the following
+    experiment: choose a node at random, choose two of its neighbors
+    at random, and check if they are connected. The approximate
+    coefficient is the fraction of triangles found over the number
+    of trials [1]_.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    trials : integer
+        Number of trials to perform (default 1000).
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    c : float
+        Approximated average clustering coefficient.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import approximation
+    >>> G = nx.erdos_renyi_graph(10, 0.2, seed=10)
+    >>> approximation.average_clustering(G, trials=1000, seed=10)
+    0.214
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is directed.
+
+    References
+    ----------
+    .. [1] Schank, Thomas, and Dorothea Wagner. Approximating clustering
+       coefficient and transitivity. Universität Karlsruhe, Fakultät für
+       Informatik, 2004.
+       https://doi.org/10.5445/IR/1000001239
+
+    """
+    n = len(G)
+    triangles = 0
+    nodes = list(G)
+    for i in [int(seed.random() * n) for i in range(trials)]:
+        nbrs = list(G[nodes[i]])
+        if len(nbrs) < 2:
+            continue
+        u, v = seed.sample(nbrs, 2)
+        if u in G[v]:
+            triangles += 1
+    return triangles / trials
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/connectivity.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/connectivity.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b596fdf782bbb223e5203fc066bbac157a29b24
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/connectivity.py
@@ -0,0 +1,412 @@
+"""Fast approximation for node connectivity"""
+
+import itertools
+from operator import itemgetter
+
+import networkx as nx
+
+__all__ = [
+    "local_node_connectivity",
+    "node_connectivity",
+    "all_pairs_node_connectivity",
+]
+
+
+@nx._dispatchable(name="approximate_local_node_connectivity")
+def local_node_connectivity(G, source, target, cutoff=None):
+    """Compute node connectivity between source and target.
+
+    Pairwise or local node connectivity between two distinct and nonadjacent
+    nodes is the minimum number of nodes that must be removed (minimum
+    separating cutset) to disconnect them. By Menger's theorem, this is equal
+    to the number of node independent paths (paths that share no nodes other
+    than source and target). Which is what we compute in this function.
+
+    This algorithm is a fast approximation that gives an strict lower
+    bound on the actual number of node independent paths between two nodes [1]_.
+    It works for both directed and undirected graphs.
+
+    Parameters
+    ----------
+
+    G : NetworkX graph
+
+    source : node
+        Starting node for node connectivity
+
+    target : node
+        Ending node for node connectivity
+
+    cutoff : integer
+        Maximum node connectivity to consider. If None, the minimum degree
+        of source or target is used as a cutoff. Default value None.
+
+    Returns
+    -------
+    k: integer
+       pairwise node connectivity
+
+    Examples
+    --------
+    >>> # Platonic octahedral graph has node connectivity 4
+    >>> # for each non adjacent node pair
+    >>> from networkx.algorithms import approximation as approx
+    >>> G = nx.octahedral_graph()
+    >>> approx.local_node_connectivity(G, 0, 5)
+    4
+
+    Notes
+    -----
+    This algorithm [1]_ finds node independents paths between two nodes by
+    computing their shortest path using BFS, marking the nodes of the path
+    found as 'used' and then searching other shortest paths excluding the
+    nodes marked as used until no more paths exist. It is not exact because
+    a shortest path could use nodes that, if the path were longer, may belong
+    to two different node independent paths. Thus it only guarantees an
+    strict lower bound on node connectivity.
+
+    Note that the authors propose a further refinement, losing accuracy and
+    gaining speed, which is not implemented yet.
+
+    See also
+    --------
+    all_pairs_node_connectivity
+    node_connectivity
+
+    References
+    ----------
+    .. [1] White, Douglas R., and Mark Newman. 2001 A Fast Algorithm for
+        Node-Independent Paths. Santa Fe Institute Working Paper #01-07-035
+        http://eclectic.ss.uci.edu/~drwhite/working.pdf
+
+    """
+    if target == source:
+        raise nx.NetworkXError("source and target have to be different nodes.")
+
+    # Maximum possible node independent paths
+    if G.is_directed():
+        possible = min(G.out_degree(source), G.in_degree(target))
+    else:
+        possible = min(G.degree(source), G.degree(target))
+
+    K = 0
+    if not possible:
+        return K
+
+    if cutoff is None:
+        cutoff = float("inf")
+
+    exclude = set()
+    for i in range(min(possible, cutoff)):
+        try:
+            path = _bidirectional_shortest_path(G, source, target, exclude)
+            exclude.update(set(path))
+            K += 1
+        except nx.NetworkXNoPath:
+            break
+
+    return K
+
+
+@nx._dispatchable(name="approximate_node_connectivity")
+def node_connectivity(G, s=None, t=None):
+    r"""Returns an approximation for node connectivity for a graph or digraph G.
+
+    Node connectivity is equal to the minimum number of nodes that
+    must be removed to disconnect G or render it trivial. By Menger's theorem,
+    this is equal to the number of node independent paths (paths that
+    share no nodes other than source and target).
+
+    If source and target nodes are provided, this function returns the
+    local node connectivity: the minimum number of nodes that must be
+    removed to break all paths from source to target in G.
+
+    This algorithm is based on a fast approximation that gives an strict lower
+    bound on the actual number of node independent paths between two nodes [1]_.
+    It works for both directed and undirected graphs.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    s : node
+        Source node. Optional. Default value: None.
+
+    t : node
+        Target node. Optional. Default value: None.
+
+    Returns
+    -------
+    K : integer
+        Node connectivity of G, or local node connectivity if source
+        and target are provided.
+
+    Examples
+    --------
+    >>> # Platonic octahedral graph is 4-node-connected
+    >>> from networkx.algorithms import approximation as approx
+    >>> G = nx.octahedral_graph()
+    >>> approx.node_connectivity(G)
+    4
+
+    Notes
+    -----
+    This algorithm [1]_ finds node independents paths between two nodes by
+    computing their shortest path using BFS, marking the nodes of the path
+    found as 'used' and then searching other shortest paths excluding the
+    nodes marked as used until no more paths exist. It is not exact because
+    a shortest path could use nodes that, if the path were longer, may belong
+    to two different node independent paths. Thus it only guarantees an
+    strict lower bound on node connectivity.
+
+    See also
+    --------
+    all_pairs_node_connectivity
+    local_node_connectivity
+
+    References
+    ----------
+    .. [1] White, Douglas R., and Mark Newman. 2001 A Fast Algorithm for
+        Node-Independent Paths. Santa Fe Institute Working Paper #01-07-035
+        http://eclectic.ss.uci.edu/~drwhite/working.pdf
+
+    """
+    if (s is not None and t is None) or (s is None and t is not None):
+        raise nx.NetworkXError("Both source and target must be specified.")
+
+    # Local node connectivity
+    if s is not None and t is not None:
+        if s not in G:
+            raise nx.NetworkXError(f"node {s} not in graph")
+        if t not in G:
+            raise nx.NetworkXError(f"node {t} not in graph")
+        return local_node_connectivity(G, s, t)
+
+    # Global node connectivity
+    if G.is_directed():
+        connected_func = nx.is_weakly_connected
+        iter_func = itertools.permutations
+
+        def neighbors(v):
+            return itertools.chain(G.predecessors(v), G.successors(v))
+
+    else:
+        connected_func = nx.is_connected
+        iter_func = itertools.combinations
+        neighbors = G.neighbors
+
+    if not connected_func(G):
+        return 0
+
+    # Choose a node with minimum degree
+    v, minimum_degree = min(G.degree(), key=itemgetter(1))
+    # Node connectivity is bounded by minimum degree
+    K = minimum_degree
+    # compute local node connectivity with all non-neighbors nodes
+    # and store the minimum
+    for w in set(G) - set(neighbors(v)) - {v}:
+        K = min(K, local_node_connectivity(G, v, w, cutoff=K))
+    # Same for non adjacent pairs of neighbors of v
+    for x, y in iter_func(neighbors(v), 2):
+        if y not in G[x] and x != y:
+            K = min(K, local_node_connectivity(G, x, y, cutoff=K))
+    return K
+
+
+@nx._dispatchable(name="approximate_all_pairs_node_connectivity")
+def all_pairs_node_connectivity(G, nbunch=None, cutoff=None):
+    """Compute node connectivity between all pairs of nodes.
+
+    Pairwise or local node connectivity between two distinct and nonadjacent
+    nodes is the minimum number of nodes that must be removed (minimum
+    separating cutset) to disconnect them. By Menger's theorem, this is equal
+    to the number of node independent paths (paths that share no nodes other
+    than source and target). Which is what we compute in this function.
+
+    This algorithm is a fast approximation that gives an strict lower
+    bound on the actual number of node independent paths between two nodes [1]_.
+    It works for both directed and undirected graphs.
+
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    nbunch: container
+        Container of nodes. If provided node connectivity will be computed
+        only over pairs of nodes in nbunch.
+
+    cutoff : integer
+        Maximum node connectivity to consider. If None, the minimum degree
+        of source or target is used as a cutoff in each pair of nodes.
+        Default value None.
+
+    Returns
+    -------
+    K : dictionary
+        Dictionary, keyed by source and target, of pairwise node connectivity
+
+    Examples
+    --------
+    A 3 node cycle with one extra node attached has connectivity 2 between all
+    nodes in the cycle and connectivity 1 between the extra node and the rest:
+
+    >>> G = nx.cycle_graph(3)
+    >>> G.add_edge(2, 3)
+    >>> import pprint  # for nice dictionary formatting
+    >>> pprint.pprint(nx.all_pairs_node_connectivity(G))
+    {0: {1: 2, 2: 2, 3: 1},
+     1: {0: 2, 2: 2, 3: 1},
+     2: {0: 2, 1: 2, 3: 1},
+     3: {0: 1, 1: 1, 2: 1}}
+
+    See Also
+    --------
+    local_node_connectivity
+    node_connectivity
+
+    References
+    ----------
+    .. [1] White, Douglas R., and Mark Newman. 2001 A Fast Algorithm for
+        Node-Independent Paths. Santa Fe Institute Working Paper #01-07-035
+        http://eclectic.ss.uci.edu/~drwhite/working.pdf
+    """
+    if nbunch is None:
+        nbunch = G
+    else:
+        nbunch = set(nbunch)
+
+    directed = G.is_directed()
+    if directed:
+        iter_func = itertools.permutations
+    else:
+        iter_func = itertools.combinations
+
+    all_pairs = {n: {} for n in nbunch}
+
+    for u, v in iter_func(nbunch, 2):
+        k = local_node_connectivity(G, u, v, cutoff=cutoff)
+        all_pairs[u][v] = k
+        if not directed:
+            all_pairs[v][u] = k
+
+    return all_pairs
+
+
+def _bidirectional_shortest_path(G, source, target, exclude):
+    """Returns shortest path between source and target ignoring nodes in the
+    container 'exclude'.
+
+    Parameters
+    ----------
+
+    G : NetworkX graph
+
+    source : node
+        Starting node for path
+
+    target : node
+        Ending node for path
+
+    exclude: container
+        Container for nodes to exclude from the search for shortest paths
+
+    Returns
+    -------
+    path: list
+        Shortest path between source and target ignoring nodes in 'exclude'
+
+    Raises
+    ------
+    NetworkXNoPath
+        If there is no path or if nodes are adjacent and have only one path
+        between them
+
+    Notes
+    -----
+    This function and its helper are originally from
+    networkx.algorithms.shortest_paths.unweighted and are modified to
+    accept the extra parameter 'exclude', which is a container for nodes
+    already used in other paths that should be ignored.
+
+    References
+    ----------
+    .. [1] White, Douglas R., and Mark Newman. 2001 A Fast Algorithm for
+        Node-Independent Paths. Santa Fe Institute Working Paper #01-07-035
+        http://eclectic.ss.uci.edu/~drwhite/working.pdf
+
+    """
+    # call helper to do the real work
+    results = _bidirectional_pred_succ(G, source, target, exclude)
+    pred, succ, w = results
+
+    # build path from pred+w+succ
+    path = []
+    # from source to w
+    while w is not None:
+        path.append(w)
+        w = pred[w]
+    path.reverse()
+    # from w to target
+    w = succ[path[-1]]
+    while w is not None:
+        path.append(w)
+        w = succ[w]
+
+    return path
+
+
+def _bidirectional_pred_succ(G, source, target, exclude):
+    # does BFS from both source and target and meets in the middle
+    # excludes nodes in the container "exclude" from the search
+
+    # handle either directed or undirected
+    if G.is_directed():
+        Gpred = G.predecessors
+        Gsucc = G.successors
+    else:
+        Gpred = G.neighbors
+        Gsucc = G.neighbors
+
+    # predecessor and successors in search
+    pred = {source: None}
+    succ = {target: None}
+
+    # initialize fringes, start with forward
+    forward_fringe = [source]
+    reverse_fringe = [target]
+
+    level = 0
+
+    while forward_fringe and reverse_fringe:
+        # Make sure that we iterate one step forward and one step backwards
+        # thus source and target will only trigger "found path" when they are
+        # adjacent and then they can be safely included in the container 'exclude'
+        level += 1
+        if level % 2 != 0:
+            this_level = forward_fringe
+            forward_fringe = []
+            for v in this_level:
+                for w in Gsucc(v):
+                    if w in exclude:
+                        continue
+                    if w not in pred:
+                        forward_fringe.append(w)
+                        pred[w] = v
+                    if w in succ:
+                        return pred, succ, w  # found path
+        else:
+            this_level = reverse_fringe
+            reverse_fringe = []
+            for v in this_level:
+                for w in Gpred(v):
+                    if w in exclude:
+                        continue
+                    if w not in succ:
+                        succ[w] = v
+                        reverse_fringe.append(w)
+                    if w in pred:
+                        return pred, succ, w  # found path
+
+    raise nx.NetworkXNoPath(f"No path between {source} and {target}.")
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/density.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/density.py
new file mode 100644
index 0000000000000000000000000000000000000000..7eb744c439a4e1a78d0924bf55528b109b6dae0b
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/density.py
@@ -0,0 +1,396 @@
+"""Fast algorithms for the densest subgraph problem"""
+
+import math
+
+import networkx as nx
+
+__all__ = ["densest_subgraph"]
+
+
+def _greedy_plus_plus(G, iterations):
+    if G.number_of_edges() == 0:
+        return 0.0, set()
+    if iterations < 1:
+        raise ValueError(
+            f"The number of iterations must be an integer >= 1. Provided: {iterations}"
+        )
+
+    loads = dict.fromkeys(G.nodes, 0)  # Load vector for Greedy++.
+    best_density = 0.0  # Highest density encountered.
+    best_subgraph = set()  # Nodes of the best subgraph found.
+
+    for _ in range(iterations):
+        # Initialize heap for fast access to minimum weighted degree.
+        heap = nx.utils.BinaryHeap()
+
+        # Compute initial weighted degrees and add nodes to the heap.
+        for node, degree in G.degree:
+            heap.insert(node, loads[node] + degree)
+        # Set up tracking for current graph state.
+        remaining_nodes = set(G.nodes)
+        num_edges = G.number_of_edges()
+        current_degrees = dict(G.degree)
+
+        while remaining_nodes:
+            num_nodes = len(remaining_nodes)
+
+            # Current density of the (implicit) graph
+            current_density = num_edges / num_nodes
+
+            # Update the best density.
+            if current_density > best_density:
+                best_density = current_density
+                best_subgraph = set(remaining_nodes)
+
+            # Pop the node with the smallest weighted degree.
+            node, _ = heap.pop()
+            if node not in remaining_nodes:
+                continue  # Skip nodes already removed.
+
+            # Update the load of the popped node.
+            loads[node] += current_degrees[node]
+
+            # Update neighbors' degrees and the heap.
+            for neighbor in G.neighbors(node):
+                if neighbor in remaining_nodes:
+                    current_degrees[neighbor] -= 1
+                    num_edges -= 1
+                    heap.insert(neighbor, loads[neighbor] + current_degrees[neighbor])
+
+            # Remove the node from the remaining nodes.
+            remaining_nodes.remove(node)
+
+    return best_density, best_subgraph
+
+
+def _fractional_peeling(G, b, x, node_to_idx, edge_to_idx):
+    """
+    Optimized fractional peeling using NumPy arrays.
+
+    Parameters
+    ----------
+    G : networkx.Graph
+        The input graph.
+    b : numpy.ndarray
+        Induced load vector.
+    x : numpy.ndarray
+        Fractional edge values.
+    node_to_idx : dict
+        Mapping from node to index.
+    edge_to_idx : dict
+        Mapping from edge to index.
+
+    Returns
+    -------
+    best_density : float
+        The best density found.
+    best_subgraph : set
+        The subset of nodes defining the densest subgraph.
+    """
+    heap = nx.utils.BinaryHeap()
+
+    remaining_nodes = set(G.nodes)
+
+    # Initialize heap with b values
+    for idx in remaining_nodes:
+        heap.insert(idx, b[idx])
+
+    num_edges = G.number_of_edges()
+
+    best_density = 0.0
+    best_subgraph = set()
+
+    while remaining_nodes:
+        num_nodes = len(remaining_nodes)
+        current_density = num_edges / num_nodes
+
+        if current_density > best_density:
+            best_density = current_density
+            best_subgraph = set(remaining_nodes)
+
+        # Pop the node with the smallest b
+        node, _ = heap.pop()
+        while node not in remaining_nodes:
+            node, _ = heap.pop()  # Clean the heap from stale values
+
+        # Update neighbors b values by subtracting fractional x value
+        for neighbor in G.neighbors(node):
+            if neighbor in remaining_nodes:
+                neighbor_idx = node_to_idx[neighbor]
+                # Take off fractional value
+                b[neighbor_idx] -= x[edge_to_idx[(neighbor, node)]]
+                num_edges -= 1
+                heap.insert(neighbor, b[neighbor_idx])
+
+        remaining_nodes.remove(node)  # peel off node
+
+    return best_density, best_subgraph
+
+
+def _fista(G, iterations):
+    if G.number_of_edges() == 0:
+        return 0.0, set()
+    if iterations < 1:
+        raise ValueError(
+            f"The number of iterations must be an integer >= 1. Provided: {iterations}"
+        )
+    import numpy as np
+
+    # 1. Node Mapping: Assign a unique index to each node and edge
+    node_to_idx = {node: idx for idx, node in enumerate(G)}
+    num_nodes = G.number_of_nodes()
+    num_undirected_edges = G.number_of_edges()
+
+    # 2. Edge Mapping: Assign a unique index to each bidirectional edge
+    bidirectional_edges = [(u, v) for u, v in G.edges] + [(v, u) for u, v in G.edges]
+    edge_to_idx = {edge: idx for idx, edge in enumerate(bidirectional_edges)}
+
+    num_edges = len(bidirectional_edges)
+
+    # 3. Reverse Edge Mapping: Map each (bidirectional) edge to its reverse edge index
+    reverse_edge_idx = np.empty(num_edges, dtype=np.int32)
+    for idx in range(num_undirected_edges):
+        reverse_edge_idx[idx] = num_undirected_edges + idx
+    for idx in range(num_undirected_edges, 2 * num_undirected_edges):
+        reverse_edge_idx[idx] = idx - num_undirected_edges
+
+    # 4. Initialize Variables as NumPy Arrays
+    x = np.full(num_edges, 0.5, dtype=np.float32)
+    y = x.copy()
+    z = np.zeros(num_edges, dtype=np.float32)
+    b = np.zeros(num_nodes, dtype=np.float32)  # Induced load vector
+    tk = 1.0  # Momentum term
+
+    # 5. Precompute Edge Source Indices
+    edge_src_indices = np.array(
+        [node_to_idx[u] for u, _ in bidirectional_edges], dtype=np.int32
+    )
+
+    # 6. Compute Learning Rate
+    max_degree = max(deg for _, deg in G.degree)
+    # 0.9 for floating point errs when max_degree is very large
+    learning_rate = 0.9 / max_degree
+
+    # 7. Iterative Updates
+    for _ in range(iterations):
+        # 7a. Update b: sum y over outgoing edges for each node
+        b[:] = 0.0  # Reset b to zero
+        np.add.at(b, edge_src_indices, y)  # b_u = \sum_{v : (u,v) \in E(G)} y_{uv}
+
+        # 7b. Compute z, z_{uv} = y_{uv} - 2 * learning_rate * b_u
+        z = y - 2.0 * learning_rate * b[edge_src_indices]
+
+        # 7c. Update Momentum Term
+        tknew = (1.0 + math.sqrt(1 + 4.0 * tk**2)) / 2.0
+
+        # 7d. Update x in a vectorized manner, x_{uv} = (z_{uv} - z_{vu} + 1.0) / 2.0
+        new_xuv = (z - z[reverse_edge_idx] + 1.0) / 2.0
+        clamped_x = np.clip(new_xuv, 0.0, 1.0)  # Clamp x_{uv} between 0 and 1
+
+        # Update y using the FISTA update formula (similar to gradient descent)
+        y = (
+            clamped_x
+            + ((tk - 1.0) / tknew) * (clamped_x - x)
+            + (tk / tknew) * (clamped_x - y)
+        )
+
+        # Update x
+        x = clamped_x
+
+        # Update tk, the momemntum term
+        tk = tknew
+
+    # Rebalance the b values! Otherwise performance is a bit suboptimal.
+    b[:] = 0.0
+    np.add.at(b, edge_src_indices, x)  # b_u = \sum_{v : (u,v) \in E(G)} x_{uv}
+
+    # Extract the actual (approximate) dense subgraph.
+    return _fractional_peeling(G, b, x, node_to_idx, edge_to_idx)
+
+
+ALGORITHMS = {"greedy++": _greedy_plus_plus, "fista": _fista}
+
+
+@nx.utils.not_implemented_for("directed")
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable
+def densest_subgraph(G, iterations=1, *, method="fista"):
+    r"""Returns an approximate densest subgraph for a graph `G`.
+
+    This function runs an iterative algorithm to find the densest subgraph,
+    and returns both the density and the subgraph. For a discussion on the
+    notion of density used and the different algorithms available on
+    networkx, please see the Notes section below.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph.
+
+    iterations : int, optional (default=1)
+        Number of iterations to use for the iterative algorithm. Can be
+        specified positionally or as a keyword argument.
+
+    method : string, optional (default='fista')
+        The algorithm to use to approximate the densest subgraph. Supported
+        options: 'greedy++' by Boob et al. [2]_ and 'fista' by Harb et al. [3]_.
+        Must be specified as a keyword argument. Other inputs produce a
+        ValueError.
+
+    Returns
+    -------
+    d : float
+        The density of the approximate subgraph found.
+
+    S : set
+        The subset of nodes defining the approximate densest subgraph.
+
+    Examples
+    --------
+    >>> G = nx.star_graph(4)
+    >>> nx.approximation.densest_subgraph(G, iterations=1)
+    (0.8, {0, 1, 2, 3, 4})
+
+    Notes
+    -----
+    **Problem Definition:**
+    The densest subgraph problem (DSG) asks to find the subgraph
+    $S \subseteq V(G)$ with maximum density. For a subset of the nodes of
+    $G$, $S \subseteq V(G)$, define $E(S) = \{ (u,v) : (u,v)\in E(G),
+    u\in S, v\in S \}$ as the set of edges with both endpoints in $S$.
+    The density of $S$ is defined as $|E(S)|/|S|$, the ratio between the
+    edges in the subgraph $G[S]$ and the number of nodes in that subgraph.
+    Note that this is different from the standard graph theoretic definition
+    of density, defined as $\frac{2|E(S)|}{|S|(|S|-1)}$, for historical
+    reasons.
+
+    **Exact Algorithms:**
+    The densest subgraph problem is polynomial time solvable using maximum
+    flow, commonly referred to as Goldberg's algorithm. However, the
+    algorithm is quite involved. It first binary searches on the optimal
+    density, $d^\ast$. For a guess of the density $d$, it sets up a flow
+    network $G'$ with size $O(m)$. The maximum flow solution either
+    informs the algorithm that no subgraph with density $d$ exists, or it
+    provides a subgraph with density at least $d$. However, this is
+    inherently bottlenecked by the maximum flow algorithm. For example, [2]_
+    notes that Goldberg’s algorithm was not feasible on many large graphs
+    even though they used a highly optimized maximum flow library.
+
+    **Charikar's Greedy Peeling:**
+    While exact solution algorithms are quite involved, there are several
+    known approximation algorithms for the densest subgraph problem.
+
+    Charikar [1]_ described a very simple 1/2-approximation algorithm for DSG
+    known as the greedy "peeling" algorithm. The algorithm creates an
+    ordering of the nodes as follows. The first node $v_1$ is the one with
+    the smallest degree in $G$ (ties broken arbitrarily). It selects
+    $v_2$ to be the smallest degree node in $G \setminus v_1$. Letting
+    $G_i$ be the graph after removing $v_1, ..., v_i$ (with $G_0=G$),
+    the algorithm returns the graph among $G_0, ..., G_n$ with the highest
+    density.
+
+    **Greedy++:**
+    Boob et al. [2]_ generalized this algorithm into Greedy++, an iterative
+    algorithm that runs several rounds of "peeling". In fact, Greedy++ with 1
+    iteration is precisely Charikar's algorithm. The algorithm converges to a
+    $(1-\epsilon)$ approximate densest subgraph in $O(\Delta(G)\log
+    n/\epsilon^2)$ iterations, where $\Delta(G)$ is the maximum degree,
+    and $n$ is the number of nodes in $G$. The algorithm also has other
+    desirable properties as shown by [4]_ and [5]_.
+
+    **FISTA Algorithm:**
+    Harb et al. [3]_ gave a faster and more scalable algorithm using ideas
+    from quadratic programming for the densest subgraph, which is based on a
+    fast iterative shrinkage-thresholding algorithm (FISTA) algorithm. It is
+    known that computing the densest subgraph can be formulated as the
+    following convex optimization problem:
+
+    Minimize $\sum_{u \in V(G)} b_u^2$
+
+    Subject to:
+
+    $b_u = \sum_{v: \{u,v\} \in E(G)} x_{uv}$ for all $u \in V(G)$
+
+    $x_{uv} + x_{vu} = 1.0$ for all $\{u,v\} \in E(G)$
+
+    $x_{uv} \geq 0, x_{vu} \geq 0$ for all $\{u,v\} \in E(G)$
+
+    Here, $x_{uv}$ represents the fraction of edge $\{u,v\}$ assigned to
+    $u$, and $x_{vu}$ to $v$.
+
+    The FISTA algorithm efficiently solves this convex program using gradient
+    descent with projections. For a learning rate $\alpha$, the algorithm
+    does:
+
+    1. **Initialization**: Set $x^{(0)}_{uv} = x^{(0)}_{vu} = 0.5$ for all
+    edges as a feasible solution.
+
+    2. **Gradient Update**: For iteration $k\geq 1$, set
+    $x^{(k+1)}_{uv} = x^{(k)}_{uv} - 2 \alpha \sum_{v: \{u,v\} \in E(G)}
+    x^{(k)}_{uv}$. However, now $x^{(k+1)}_{uv}$ might be infeasible!
+    To ensure feasibility, we project $x^{(k+1)}_{uv}$.
+
+    3. **Projection to the Feasible Set**: Compute
+    $b^{(k+1)}_u = \sum_{v: \{u,v\} \in E(G)} x^{(k)}_{uv}$ for all
+    nodes $u$. Define $z^{(k+1)}_{uv} = x^{(k+1)}_{uv} - 2 \alpha
+    b^{(k+1)}_u$. Update $x^{(k+1)}_{uv} =
+    CLAMP((z^{(k+1)}_{uv} - z^{(k+1)}_{vu} + 1.0) / 2.0)$, where
+    $CLAMP(x) = \max(0, \min(1, x))$.
+
+    With a learning rate of $\alpha=1/\Delta(G)$, where $\Delta(G)$ is
+    the maximum degree, the algorithm converges to the optimum solution of
+    the convex program.
+
+    **Fractional Peeling:**
+    To obtain a **discrete** subgraph, we use fractional peeling, an
+    adaptation of the standard peeling algorithm which peels the minimum
+    degree vertex in each iteration, and returns the densest subgraph found
+    along the way. Here, we instead peel the vertex with the smallest
+    induced load $b_u$:
+
+    1. Compute $b_u$ and $x_{uv}$.
+
+    2. Iteratively remove the vertex with the smallest $b_u$, updating its
+    neighbors' load by $x_{vu}$.
+
+    Fractional peeling transforms the approximately optimal fractional
+    values $b_u, x_{uv}$ into a discrete subgraph. Unlike traditional
+    peeling, which removes the lowest-degree node, this method accounts for
+    fractional edge contributions from the convex program.
+
+    This approach is both scalable and theoretically sound, ensuring a quick
+    approximation of the densest subgraph while leveraging fractional load
+    balancing.
+
+    References
+    ----------
+    .. [1] Charikar, Moses. "Greedy approximation algorithms for finding dense
+       components in a graph." In International workshop on approximation
+       algorithms for combinatorial optimization, pp. 84-95. Berlin, Heidelberg:
+       Springer Berlin Heidelberg, 2000.
+
+    .. [2] Boob, Digvijay, Yu Gao, Richard Peng, Saurabh Sawlani, Charalampos
+       Tsourakakis, Di Wang, and Junxing Wang. "Flowless: Extracting densest
+       subgraphs without flow computations." In Proceedings of The Web Conference
+       2020, pp. 573-583. 2020.
+
+    .. [3] Harb, Elfarouk, Kent Quanrud, and Chandra Chekuri. "Faster and scalable
+       algorithms for densest subgraph and decomposition." Advances in Neural
+       Information Processing Systems 35 (2022): 26966-26979.
+
+    .. [4] Harb, Elfarouk, Kent Quanrud, and Chandra Chekuri. "Convergence to
+       lexicographically optimal base in a (contra) polymatroid and applications
+       to densest subgraph and tree packing." arXiv preprint arXiv:2305.02987
+       (2023).
+
+    .. [5] Chekuri, Chandra, Kent Quanrud, and Manuel R. Torres. "Densest
+       subgraph: Supermodularity, iterative peeling, and flow." In Proceedings of
+       the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp.
+       1531-1555. Society for Industrial and Applied Mathematics, 2022.
+    """
+    try:
+        algo = ALGORITHMS[method]
+    except KeyError as e:
+        raise ValueError(f"{method} is not a valid choice for an algorithm.") from e
+
+    return algo(G, iterations)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/distance_measures.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/distance_measures.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5847e65a2a401cd607436297fe4c1bbc81db3d9
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/distance_measures.py
@@ -0,0 +1,150 @@
+"""Distance measures approximated metrics."""
+
+import networkx as nx
+from networkx.utils.decorators import py_random_state
+
+__all__ = ["diameter"]
+
+
+@py_random_state(1)
+@nx._dispatchable(name="approximate_diameter")
+def diameter(G, seed=None):
+    """Returns a lower bound on the diameter of the graph G.
+
+    The function computes a lower bound on the diameter (i.e., the maximum eccentricity)
+    of a directed or undirected graph G. The procedure used varies depending on the graph
+    being directed or not.
+
+    If G is an `undirected` graph, then the function uses the `2-sweep` algorithm [1]_.
+    The main idea is to pick the farthest node from a random node and return its eccentricity.
+
+    Otherwise, if G is a `directed` graph, the function uses the `2-dSweep` algorithm [2]_,
+    The procedure starts by selecting a random source node $s$ from which it performs a
+    forward and a backward BFS. Let $a_1$ and $a_2$ be the farthest nodes in the forward and
+    backward cases, respectively. Then, it computes the backward eccentricity of $a_1$ using
+    a backward BFS and the forward eccentricity of $a_2$ using a forward BFS.
+    Finally, it returns the best lower bound between the two.
+
+    In both cases, the time complexity is linear with respect to the size of G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    d : integer
+       Lower Bound on the Diameter of G
+
+    Examples
+    --------
+    >>> G = nx.path_graph(10)  # undirected graph
+    >>> nx.diameter(G)
+    9
+    >>> G = nx.cycle_graph(3, create_using=nx.DiGraph)  # directed graph
+    >>> nx.diameter(G)
+    2
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is empty or
+        If the graph is undirected and not connected or
+        If the graph is directed and not strongly connected.
+
+    See Also
+    --------
+    networkx.algorithms.distance_measures.diameter
+
+    References
+    ----------
+    .. [1] Magnien, Clémence, Matthieu Latapy, and Michel Habib.
+       *Fast computation of empirically tight bounds for the diameter of massive graphs.*
+       Journal of Experimental Algorithmics (JEA), 2009.
+       https://arxiv.org/pdf/0904.2728.pdf
+    .. [2] Crescenzi, Pierluigi, Roberto Grossi, Leonardo Lanzi, and Andrea Marino.
+       *On computing the diameter of real-world directed (weighted) graphs.*
+       International Symposium on Experimental Algorithms. Springer, Berlin, Heidelberg, 2012.
+       https://courses.cs.ut.ee/MTAT.03.238/2014_fall/uploads/Main/diameter.pdf
+    """
+    # if G is empty
+    if not G:
+        raise nx.NetworkXError("Expected non-empty NetworkX graph!")
+    # if there's only a node
+    if G.number_of_nodes() == 1:
+        return 0
+    # if G is directed
+    if G.is_directed():
+        return _two_sweep_directed(G, seed)
+    # else if G is undirected
+    return _two_sweep_undirected(G, seed)
+
+
+def _two_sweep_undirected(G, seed):
+    """Helper function for finding a lower bound on the diameter
+        for undirected Graphs.
+
+        The idea is to pick the farthest node from a random node
+        and return its eccentricity.
+
+        ``G`` is a NetworkX undirected graph.
+
+    .. note::
+
+        ``seed`` is a random.Random or numpy.random.RandomState instance
+    """
+    # select a random source node
+    source = seed.choice(list(G))
+    # get the distances to the other nodes
+    distances = nx.shortest_path_length(G, source)
+    # if some nodes have not been visited, then the graph is not connected
+    if len(distances) != len(G):
+        raise nx.NetworkXError("Graph not connected.")
+    # take a node that is (one of) the farthest nodes from the source
+    *_, node = distances
+    # return the eccentricity of the node
+    return nx.eccentricity(G, node)
+
+
+def _two_sweep_directed(G, seed):
+    """Helper function for finding a lower bound on the diameter
+        for directed Graphs.
+
+        It implements 2-dSweep, the directed version of the 2-sweep algorithm.
+        The algorithm follows the following steps.
+        1. Select a source node $s$ at random.
+        2. Perform a forward BFS from $s$ to select a node $a_1$ at the maximum
+        distance from the source, and compute $LB_1$, the backward eccentricity of $a_1$.
+        3. Perform a backward BFS from $s$ to select a node $a_2$ at the maximum
+        distance from the source, and compute $LB_2$, the forward eccentricity of $a_2$.
+        4. Return the maximum between $LB_1$ and $LB_2$.
+
+        ``G`` is a NetworkX directed graph.
+
+    .. note::
+
+        ``seed`` is a random.Random or numpy.random.RandomState instance
+    """
+    # get a new digraph G' with the edges reversed in the opposite direction
+    G_reversed = G.reverse()
+    # select a random source node
+    source = seed.choice(list(G))
+    # compute forward distances from source
+    forward_distances = nx.shortest_path_length(G, source)
+    # compute backward distances  from source
+    backward_distances = nx.shortest_path_length(G_reversed, source)
+    # if either the source can't reach every node or not every node
+    # can reach the source, then the graph is not strongly connected
+    n = len(G)
+    if len(forward_distances) != n or len(backward_distances) != n:
+        raise nx.NetworkXError("DiGraph not strongly connected.")
+    # take a node a_1 at the maximum distance from the source in G
+    *_, a_1 = forward_distances
+    # take a node a_2 at the maximum distance from the source in G_reversed
+    *_, a_2 = backward_distances
+    # return the max between the backward eccentricity of a_1 and the forward eccentricity of a_2
+    return max(nx.eccentricity(G_reversed, a_1), nx.eccentricity(G, a_2))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/dominating_set.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/dominating_set.py
new file mode 100644
index 0000000000000000000000000000000000000000..e568a827ff99dad5390e8d7feb886cf92a3a6cad
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/dominating_set.py
@@ -0,0 +1,149 @@
+"""Functions for finding node and edge dominating sets.
+
+A `dominating set`_ for an undirected graph *G* with vertex set *V*
+and edge set *E* is a subset *D* of *V* such that every vertex not in
+*D* is adjacent to at least one member of *D*. An `edge dominating set`_
+is a subset *F* of *E* such that every edge not in *F* is
+incident to an endpoint of at least one edge in *F*.
+
+.. _dominating set: https://en.wikipedia.org/wiki/Dominating_set
+.. _edge dominating set: https://en.wikipedia.org/wiki/Edge_dominating_set
+
+"""
+
+import networkx as nx
+
+from ...utils import not_implemented_for
+from ..matching import maximal_matching
+
+__all__ = ["min_weighted_dominating_set", "min_edge_dominating_set"]
+
+
+# TODO Why doesn't this algorithm work for directed graphs?
+@not_implemented_for("directed")
+@nx._dispatchable(node_attrs="weight")
+def min_weighted_dominating_set(G, weight=None):
+    r"""Returns a dominating set that approximates the minimum weight node
+    dominating set.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph.
+
+    weight : string
+        The node attribute storing the weight of an node. If provided,
+        the node attribute with this key must be a number for each
+        node. If not provided, each node is assumed to have weight one.
+
+    Returns
+    -------
+    min_weight_dominating_set : set
+        A set of nodes, the sum of whose weights is no more than `(\log
+        w(V)) w(V^*)`, where `w(V)` denotes the sum of the weights of
+        each node in the graph and `w(V^*)` denotes the sum of the
+        weights of each node in the minimum weight dominating set.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 4), (1, 4), (1, 2), (2, 3), (3, 4), (2, 5)])
+    >>> nx.approximation.min_weighted_dominating_set(G)
+    {1, 2, 4}
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is directed.
+
+    Notes
+    -----
+    This algorithm computes an approximate minimum weighted dominating
+    set for the graph `G`. The returned solution has weight `(\log
+    w(V)) w(V^*)`, where `w(V)` denotes the sum of the weights of each
+    node in the graph and `w(V^*)` denotes the sum of the weights of
+    each node in the minimum weight dominating set for the graph.
+
+    This implementation of the algorithm runs in $O(m)$ time, where $m$
+    is the number of edges in the graph.
+
+    References
+    ----------
+    .. [1] Vazirani, Vijay V.
+           *Approximation Algorithms*.
+           Springer Science & Business Media, 2001.
+
+    """
+    # The unique dominating set for the null graph is the empty set.
+    if len(G) == 0:
+        return set()
+
+    # This is the dominating set that will eventually be returned.
+    dom_set = set()
+
+    def _cost(node_and_neighborhood):
+        """Returns the cost-effectiveness of greedily choosing the given
+        node.
+
+        `node_and_neighborhood` is a two-tuple comprising a node and its
+        closed neighborhood.
+
+        """
+        v, neighborhood = node_and_neighborhood
+        return G.nodes[v].get(weight, 1) / len(neighborhood - dom_set)
+
+    # This is a set of all vertices not already covered by the
+    # dominating set.
+    vertices = set(G)
+    # This is a dictionary mapping each node to the closed neighborhood
+    # of that node.
+    neighborhoods = {v: {v} | set(G[v]) for v in G}
+
+    # Continue until all vertices are adjacent to some node in the
+    # dominating set.
+    while vertices:
+        # Find the most cost-effective node to add, along with its
+        # closed neighborhood.
+        dom_node, min_set = min(neighborhoods.items(), key=_cost)
+        # Add the node to the dominating set and reduce the remaining
+        # set of nodes to cover.
+        dom_set.add(dom_node)
+        del neighborhoods[dom_node]
+        vertices -= min_set
+
+    return dom_set
+
+
+@nx._dispatchable
+def min_edge_dominating_set(G):
+    r"""Returns minimum cardinality edge dominating set.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+      Undirected graph
+
+    Returns
+    -------
+    min_edge_dominating_set : set
+      Returns a set of dominating edges whose size is no more than 2 * OPT.
+
+    Examples
+    --------
+    >>> G = nx.petersen_graph()
+    >>> nx.approximation.min_edge_dominating_set(G)
+    {(0, 1), (4, 9), (6, 8), (5, 7), (2, 3)}
+
+    Raises
+    ------
+    ValueError
+        If the input graph `G` is empty.
+
+    Notes
+    -----
+    The algorithm computes an approximate solution to the edge dominating set
+    problem. The result is no more than 2 * OPT in terms of size of the set.
+    Runtime of the algorithm is $O(|E|)$.
+    """
+    if not G:
+        raise ValueError("Expected non-empty NetworkX graph!")
+    return maximal_matching(G)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/kcomponents.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/kcomponents.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe52e39b587abb48ddd65d616521bd047ba82cbc
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/kcomponents.py
@@ -0,0 +1,367 @@
+"""Fast approximation for k-component structure"""
+
+import itertools
+from collections import defaultdict
+from collections.abc import Mapping
+from functools import cached_property
+
+import networkx as nx
+from networkx.algorithms.approximation import local_node_connectivity
+from networkx.exception import NetworkXError
+from networkx.utils import not_implemented_for
+
+__all__ = ["k_components"]
+
+
+@not_implemented_for("directed")
+@nx._dispatchable(name="approximate_k_components")
+def k_components(G, min_density=0.95):
+    r"""Returns the approximate k-component structure of a graph G.
+
+    A `k`-component is a maximal subgraph of a graph G that has, at least,
+    node connectivity `k`: we need to remove at least `k` nodes to break it
+    into more components. `k`-components have an inherent hierarchical
+    structure because they are nested in terms of connectivity: a connected
+    graph can contain several 2-components, each of which can contain
+    one or more 3-components, and so forth.
+
+    This implementation is based on the fast heuristics to approximate
+    the `k`-component structure of a graph [1]_. Which, in turn, it is based on
+    a fast approximation algorithm for finding good lower bounds of the number
+    of node independent paths between two nodes [2]_.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    min_density : Float
+        Density relaxation threshold. Default value 0.95
+
+    Returns
+    -------
+    k_components : dict
+        Dictionary with connectivity level `k` as key and a list of
+        sets of nodes that form a k-component of level `k` as values.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is directed.
+
+    Examples
+    --------
+    >>> # Petersen graph has 10 nodes and it is triconnected, thus all
+    >>> # nodes are in a single component on all three connectivity levels
+    >>> from networkx.algorithms import approximation as apxa
+    >>> G = nx.petersen_graph()
+    >>> k_components = apxa.k_components(G)
+
+    Notes
+    -----
+    The logic of the approximation algorithm for computing the `k`-component
+    structure [1]_ is based on repeatedly applying simple and fast algorithms
+    for `k`-cores and biconnected components in order to narrow down the
+    number of pairs of nodes over which we have to compute White and Newman's
+    approximation algorithm for finding node independent paths [2]_. More
+    formally, this algorithm is based on Whitney's theorem, which states
+    an inclusion relation among node connectivity, edge connectivity, and
+    minimum degree for any graph G. This theorem implies that every
+    `k`-component is nested inside a `k`-edge-component, which in turn,
+    is contained in a `k`-core. Thus, this algorithm computes node independent
+    paths among pairs of nodes in each biconnected part of each `k`-core,
+    and repeats this procedure for each `k` from 3 to the maximal core number
+    of a node in the input graph.
+
+    Because, in practice, many nodes of the core of level `k` inside a
+    bicomponent actually are part of a component of level k, the auxiliary
+    graph needed for the algorithm is likely to be very dense. Thus, we use
+    a complement graph data structure (see `AntiGraph`) to save memory.
+    AntiGraph only stores information of the edges that are *not* present
+    in the actual auxiliary graph. When applying algorithms to this
+    complement graph data structure, it behaves as if it were the dense
+    version.
+
+    See also
+    --------
+    k_components
+
+    References
+    ----------
+    .. [1]  Torrents, J. and F. Ferraro (2015) Structural Cohesion:
+            Visualization and Heuristics for Fast Computation.
+            https://arxiv.org/pdf/1503.04476v1
+
+    .. [2]  White, Douglas R., and Mark Newman (2001) A Fast Algorithm for
+            Node-Independent Paths. Santa Fe Institute Working Paper #01-07-035
+            https://www.santafe.edu/research/results/working-papers/fast-approximation-algorithms-for-finding-node-ind
+
+    .. [3]  Moody, J. and D. White (2003). Social cohesion and embeddedness:
+            A hierarchical conception of social groups.
+            American Sociological Review 68(1), 103--28.
+            https://doi.org/10.2307/3088904
+
+    """
+    # Dictionary with connectivity level (k) as keys and a list of
+    # sets of nodes that form a k-component as values
+    k_components = defaultdict(list)
+    # make a few functions local for speed
+    node_connectivity = local_node_connectivity
+    k_core = nx.k_core
+    core_number = nx.core_number
+    biconnected_components = nx.biconnected_components
+    combinations = itertools.combinations
+    # Exact solution for k = {1,2}
+    # There is a linear time algorithm for triconnectivity, if we had an
+    # implementation available we could start from k = 4.
+    for component in nx.connected_components(G):
+        # isolated nodes have connectivity 0
+        comp = set(component)
+        if len(comp) > 1:
+            k_components[1].append(comp)
+    for bicomponent in nx.biconnected_components(G):
+        # avoid considering dyads as bicomponents
+        bicomp = set(bicomponent)
+        if len(bicomp) > 2:
+            k_components[2].append(bicomp)
+    # There is no k-component of k > maximum core number
+    # \kappa(G) <= \lambda(G) <= \delta(G)
+    g_cnumber = core_number(G)
+    max_core = max(g_cnumber.values())
+    for k in range(3, max_core + 1):
+        C = k_core(G, k, core_number=g_cnumber)
+        for nodes in biconnected_components(C):
+            # Build a subgraph SG induced by the nodes that are part of
+            # each biconnected component of the k-core subgraph C.
+            if len(nodes) < k:
+                continue
+            SG = G.subgraph(nodes)
+            # Build auxiliary graph
+            H = _AntiGraph()
+            H.add_nodes_from(SG.nodes())
+            for u, v in combinations(SG, 2):
+                K = node_connectivity(SG, u, v, cutoff=k)
+                if k > K:
+                    H.add_edge(u, v)
+            for h_nodes in biconnected_components(H):
+                if len(h_nodes) <= k:
+                    continue
+                SH = H.subgraph(h_nodes)
+                for Gc in _cliques_heuristic(SG, SH, k, min_density):
+                    for k_nodes in biconnected_components(Gc):
+                        Gk = nx.k_core(SG.subgraph(k_nodes), k)
+                        if len(Gk) <= k:
+                            continue
+                        k_components[k].append(set(Gk))
+    return k_components
+
+
+def _cliques_heuristic(G, H, k, min_density):
+    h_cnumber = nx.core_number(H)
+    for i, c_value in enumerate(sorted(set(h_cnumber.values()), reverse=True)):
+        cands = {n for n, c in h_cnumber.items() if c == c_value}
+        # Skip checking for overlap for the highest core value
+        if i == 0:
+            overlap = False
+        else:
+            overlap = set.intersection(
+                *[{x for x in H[n] if x not in cands} for n in cands]
+            )
+        if overlap and len(overlap) < k:
+            SH = H.subgraph(cands | overlap)
+        else:
+            SH = H.subgraph(cands)
+        sh_cnumber = nx.core_number(SH)
+        SG = nx.k_core(G.subgraph(SH), k)
+        while not (_same(sh_cnumber) and nx.density(SH) >= min_density):
+            # This subgraph must be writable => .copy()
+            SH = H.subgraph(SG).copy()
+            if len(SH) <= k:
+                break
+            sh_cnumber = nx.core_number(SH)
+            sh_deg = dict(SH.degree())
+            min_deg = min(sh_deg.values())
+            SH.remove_nodes_from(n for n, d in sh_deg.items() if d == min_deg)
+            SG = nx.k_core(G.subgraph(SH), k)
+        else:
+            yield SG
+
+
+def _same(measure, tol=0):
+    vals = set(measure.values())
+    if (max(vals) - min(vals)) <= tol:
+        return True
+    return False
+
+
+class _AntiGraph(nx.Graph):
+    """
+    Class for complement graphs.
+
+    The main goal is to be able to work with big and dense graphs with
+    a low memory footprint.
+
+    In this class you add the edges that *do not exist* in the dense graph,
+    the report methods of the class return the neighbors, the edges and
+    the degree as if it was the dense graph. Thus it's possible to use
+    an instance of this class with some of NetworkX functions. In this
+    case we only use k-core, connected_components, and biconnected_components.
+    """
+
+    all_edge_dict = {"weight": 1}
+
+    def single_edge_dict(self):
+        return self.all_edge_dict
+
+    edge_attr_dict_factory = single_edge_dict  # type: ignore[assignment]
+
+    def __getitem__(self, n):
+        """Returns a dict of neighbors of node n in the dense graph.
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph.
+
+        Returns
+        -------
+        adj_dict : dictionary
+           The adjacency dictionary for nodes connected to n.
+
+        """
+        all_edge_dict = self.all_edge_dict
+        return dict.fromkeys(set(self._adj) - set(self._adj[n]) - {n}, all_edge_dict)
+
+    def neighbors(self, n):
+        """Returns an iterator over all neighbors of node n in the
+        dense graph.
+        """
+        try:
+            return iter(set(self._adj) - set(self._adj[n]) - {n})
+        except KeyError as err:
+            raise NetworkXError(f"The node {n} is not in the graph.") from err
+
+    class AntiAtlasView(Mapping):
+        """An adjacency inner dict for AntiGraph"""
+
+        def __init__(self, graph, node):
+            self._graph = graph
+            self._atlas = graph._adj[node]
+            self._node = node
+
+        def __len__(self):
+            return len(self._graph) - len(self._atlas) - 1
+
+        def __iter__(self):
+            return (n for n in self._graph if n not in self._atlas and n != self._node)
+
+        def __getitem__(self, nbr):
+            nbrs = set(self._graph._adj) - set(self._atlas) - {self._node}
+            if nbr in nbrs:
+                return self._graph.all_edge_dict
+            raise KeyError(nbr)
+
+    class AntiAdjacencyView(AntiAtlasView):
+        """An adjacency outer dict for AntiGraph"""
+
+        def __init__(self, graph):
+            self._graph = graph
+            self._atlas = graph._adj
+
+        def __len__(self):
+            return len(self._atlas)
+
+        def __iter__(self):
+            return iter(self._graph)
+
+        def __getitem__(self, node):
+            if node not in self._graph:
+                raise KeyError(node)
+            return self._graph.AntiAtlasView(self._graph, node)
+
+    @cached_property
+    def adj(self):
+        return self.AntiAdjacencyView(self)
+
+    def subgraph(self, nodes):
+        """This subgraph method returns a full AntiGraph. Not a View"""
+        nodes = set(nodes)
+        G = _AntiGraph()
+        G.add_nodes_from(nodes)
+        for n in G:
+            Gnbrs = G.adjlist_inner_dict_factory()
+            G._adj[n] = Gnbrs
+            for nbr, d in self._adj[n].items():
+                if nbr in G._adj:
+                    Gnbrs[nbr] = d
+                    G._adj[nbr][n] = d
+        G.graph = self.graph
+        return G
+
+    class AntiDegreeView(nx.reportviews.DegreeView):
+        def __iter__(self):
+            all_nodes = set(self._succ)
+            for n in self._nodes:
+                nbrs = all_nodes - set(self._succ[n]) - {n}
+                yield (n, len(nbrs))
+
+        def __getitem__(self, n):
+            nbrs = set(self._succ) - set(self._succ[n]) - {n}
+            # AntiGraph is a ThinGraph so all edges have weight 1
+            return len(nbrs) + (n in nbrs)
+
+    @cached_property
+    def degree(self):
+        """Returns an iterator for (node, degree) and degree for single node.
+
+        The node degree is the number of edges adjacent to the node.
+
+        Parameters
+        ----------
+        nbunch : iterable container, optional (default=all nodes)
+            A container of nodes.  The container will be iterated
+            through once.
+
+        weight : string or None, optional (default=None)
+           The edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        deg:
+            Degree of the node, if a single node is passed as argument.
+        nd_iter : an iterator
+            The iterator returns two-tuples of (node, degree).
+
+        See Also
+        --------
+        degree
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)
+        >>> G.degree(0)  # node 0 with degree 1
+        1
+        >>> list(G.degree([0, 1]))
+        [(0, 1), (1, 2)]
+
+        """
+        return self.AntiDegreeView(self)
+
+    def adjacency(self):
+        """Returns an iterator of (node, adjacency set) tuples for all nodes
+           in the dense graph.
+
+        This is the fastest way to look at every edge.
+        For directed graphs, only outgoing adjacencies are included.
+
+        Returns
+        -------
+        adj_iter : iterator
+           An iterator of (node, adjacency set) for all nodes in
+           the graph.
+
+        """
+        for n in self._adj:
+            yield (n, set(self._adj) - set(self._adj[n]) - {n})
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/matching.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/matching.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc0891947cb88c38930e9f8c5479d397e3aa923b
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/matching.py
@@ -0,0 +1,44 @@
+"""
+**************
+Graph Matching
+**************
+
+Given a graph G = (V,E), a matching M in G is a set of pairwise non-adjacent
+edges; that is, no two edges share a common vertex.
+
+`Wikipedia: Matching <https://en.wikipedia.org/wiki/Matching_(graph_theory)>`_
+"""
+
+import networkx as nx
+
+__all__ = ["min_maximal_matching"]
+
+
+@nx._dispatchable
+def min_maximal_matching(G):
+    r"""Returns the minimum maximal matching of G. That is, out of all maximal
+    matchings of the graph G, the smallest is returned.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+      Undirected graph
+
+    Returns
+    -------
+    min_maximal_matching : set
+      Returns a set of edges such that no two edges share a common endpoint
+      and every edge not in the set shares some common endpoint in the set.
+      Cardinality will be 2*OPT in the worst case.
+
+    Notes
+    -----
+    The algorithm computes an approximate solution for the minimum maximal
+    cardinality matching problem. The solution is no more than 2 * OPT in size.
+    Runtime is $O(|E|)$.
+
+    References
+    ----------
+    .. [1] Vazirani, Vijay Approximation Algorithms (2001)
+    """
+    return nx.maximal_matching(G)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/maxcut.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/maxcut.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4e1da87c35ab821f4b3d0851bba19d599d8fa6a
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/maxcut.py
@@ -0,0 +1,143 @@
+import networkx as nx
+from networkx.utils.decorators import not_implemented_for, py_random_state
+
+__all__ = ["randomized_partitioning", "one_exchange"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@py_random_state(1)
+@nx._dispatchable(edge_attrs="weight")
+def randomized_partitioning(G, seed=None, p=0.5, weight=None):
+    """Compute a random partitioning of the graph nodes and its cut value.
+
+    A partitioning is calculated by observing each node
+    and deciding to add it to the partition with probability `p`,
+    returning a random cut and its corresponding value (the
+    sum of weights of edges connecting different partitions).
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    p : scalar
+        Probability for each node to be part of the first partition.
+        Should be in [0,1]
+
+    weight : object
+        Edge attribute key to use as weight. If not specified, edges
+        have weight one.
+
+    Returns
+    -------
+    cut_size : scalar
+        Value of the minimum cut.
+
+    partition : pair of node sets
+        A partitioning of the nodes that defines a minimum cut.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> cut_size, partition = nx.approximation.randomized_partitioning(G, seed=1)
+    >>> cut_size
+    6
+    >>> partition
+    ({0, 3, 4}, {1, 2})
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+    """
+    cut = {node for node in G.nodes() if seed.random() < p}
+    cut_size = nx.algorithms.cut_size(G, cut, weight=weight)
+    partition = (cut, G.nodes - cut)
+    return cut_size, partition
+
+
+def _swap_node_partition(cut, node):
+    return cut - {node} if node in cut else cut.union({node})
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@py_random_state(2)
+@nx._dispatchable(edge_attrs="weight")
+def one_exchange(G, initial_cut=None, seed=None, weight=None):
+    """Compute a partitioning of the graphs nodes and the corresponding cut value.
+
+    Use a greedy one exchange strategy to find a locally maximal cut
+    and its value, it works by finding the best node (one that gives
+    the highest gain to the cut value) to add to the current cut
+    and repeats this process until no improvement can be made.
+
+    Parameters
+    ----------
+    G : networkx Graph
+        Graph to find a maximum cut for.
+
+    initial_cut : set
+        Cut to use as a starting point. If not supplied the algorithm
+        starts with an empty cut.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    weight : object
+        Edge attribute key to use as weight. If not specified, edges
+        have weight one.
+
+    Returns
+    -------
+    cut_value : scalar
+        Value of the maximum cut.
+
+    partition : pair of node sets
+        A partitioning of the nodes that defines a maximum cut.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> curr_cut_size, partition = nx.approximation.one_exchange(G, seed=1)
+    >>> curr_cut_size
+    6
+    >>> partition
+    ({0, 2}, {1, 3, 4})
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+    """
+    if initial_cut is None:
+        initial_cut = set()
+    cut = set(initial_cut)
+    current_cut_size = nx.algorithms.cut_size(G, cut, weight=weight)
+    while True:
+        nodes = list(G.nodes())
+        # Shuffling the nodes ensures random tie-breaks in the following call to max
+        seed.shuffle(nodes)
+        best_node_to_swap = max(
+            nodes,
+            key=lambda v: nx.algorithms.cut_size(
+                G, _swap_node_partition(cut, v), weight=weight
+            ),
+            default=None,
+        )
+        potential_cut = _swap_node_partition(cut, best_node_to_swap)
+        potential_cut_size = nx.algorithms.cut_size(G, potential_cut, weight=weight)
+
+        if potential_cut_size > current_cut_size:
+            cut = potential_cut
+            current_cut_size = potential_cut_size
+        else:
+            break
+
+    partition = (cut, G.nodes - cut)
+    return current_cut_size, partition
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/ramsey.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/ramsey.py
new file mode 100644
index 0000000000000000000000000000000000000000..0552e4a942c9c99fbf132300d7288595307052ff
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/ramsey.py
@@ -0,0 +1,53 @@
+"""
+Ramsey numbers.
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+from ...utils import arbitrary_element
+
+__all__ = ["ramsey_R2"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def ramsey_R2(G):
+    r"""Compute the largest clique and largest independent set in `G`.
+
+    This can be used to estimate bounds for the 2-color
+    Ramsey number `R(2;s,t)` for `G`.
+
+    This is a recursive implementation which could run into trouble
+    for large recursions. Note that self-loop edges are ignored.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    Returns
+    -------
+    max_pair : (set, set) tuple
+        Maximum clique, Maximum independent set.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+    """
+    if not G:
+        return set(), set()
+
+    node = arbitrary_element(G)
+    nbrs = (nbr for nbr in nx.all_neighbors(G, node) if nbr != node)
+    nnbrs = nx.non_neighbors(G, node)
+    c_1, i_1 = ramsey_R2(G.subgraph(nbrs).copy())
+    c_2, i_2 = ramsey_R2(G.subgraph(nnbrs).copy())
+
+    c_1.add(node)
+    i_2.add(node)
+    # Choose the larger of the two cliques and the larger of the two
+    # independent sets, according to cardinality.
+    return max(c_1, c_2, key=len), max(i_1, i_2, key=len)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/steinertree.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/steinertree.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bee11c56beca37943aabbdba4e560305c3fb1d0
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/steinertree.py
@@ -0,0 +1,248 @@
+from itertools import chain
+
+import networkx as nx
+from networkx.utils import not_implemented_for, pairwise
+
+__all__ = ["metric_closure", "steiner_tree"]
+
+
+@not_implemented_for("directed")
+@nx._dispatchable(edge_attrs="weight", returns_graph=True)
+def metric_closure(G, weight="weight"):
+    """Return the metric closure of a graph.
+
+    The metric closure of a graph *G* is the complete graph in which each edge
+    is weighted by the shortest path distance between the nodes in *G* .
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    NetworkX graph
+        Metric closure of the graph `G`.
+
+    """
+    M = nx.Graph()
+
+    Gnodes = set(G)
+
+    # check for connected graph while processing first node
+    all_paths_iter = nx.all_pairs_dijkstra(G, weight=weight)
+    u, (distance, path) = next(all_paths_iter)
+    if len(G) != len(distance):
+        msg = "G is not a connected graph. metric_closure is not defined."
+        raise nx.NetworkXError(msg)
+    Gnodes.remove(u)
+    for v in Gnodes:
+        M.add_edge(u, v, distance=distance[v], path=path[v])
+
+    # first node done -- now process the rest
+    for u, (distance, path) in all_paths_iter:
+        Gnodes.remove(u)
+        for v in Gnodes:
+            M.add_edge(u, v, distance=distance[v], path=path[v])
+
+    return M
+
+
+def _mehlhorn_steiner_tree(G, terminal_nodes, weight):
+    paths = nx.multi_source_dijkstra_path(G, terminal_nodes)
+
+    d_1 = {}
+    s = {}
+    for v in G.nodes():
+        s[v] = paths[v][0]
+        d_1[(v, s[v])] = len(paths[v]) - 1
+
+    # G1-G4 names match those from the Mehlhorn 1988 paper.
+    G_1_prime = nx.Graph()
+    for u, v, data in G.edges(data=True):
+        su, sv = s[u], s[v]
+        weight_here = d_1[(u, su)] + data.get(weight, 1) + d_1[(v, sv)]
+        if not G_1_prime.has_edge(su, sv):
+            G_1_prime.add_edge(su, sv, weight=weight_here)
+        else:
+            new_weight = min(weight_here, G_1_prime[su][sv]["weight"])
+            G_1_prime.add_edge(su, sv, weight=new_weight)
+
+    G_2 = nx.minimum_spanning_edges(G_1_prime, data=True)
+
+    G_3 = nx.Graph()
+    for u, v, d in G_2:
+        path = nx.shortest_path(G, u, v, weight)
+        for n1, n2 in pairwise(path):
+            G_3.add_edge(n1, n2)
+
+    G_3_mst = list(nx.minimum_spanning_edges(G_3, data=False))
+    if G.is_multigraph():
+        G_3_mst = (
+            (u, v, min(G[u][v], key=lambda k: G[u][v][k][weight])) for u, v in G_3_mst
+        )
+    G_4 = G.edge_subgraph(G_3_mst).copy()
+    _remove_nonterminal_leaves(G_4, terminal_nodes)
+    return G_4.edges()
+
+
+def _kou_steiner_tree(G, terminal_nodes, weight):
+    # Compute the metric closure only for terminal nodes
+    # Create a complete graph H from the metric edges
+    H = nx.Graph()
+    unvisited_terminals = set(terminal_nodes)
+
+    # check for connected graph while processing first node
+    u = unvisited_terminals.pop()
+    distances, paths = nx.single_source_dijkstra(G, source=u, weight=weight)
+    if len(G) != len(distances):
+        msg = "G is not a connected graph."
+        raise nx.NetworkXError(msg)
+    for v in unvisited_terminals:
+        H.add_edge(u, v, distance=distances[v], path=paths[v])
+
+    # first node done -- now process the rest
+    for u in unvisited_terminals.copy():
+        distances, paths = nx.single_source_dijkstra(G, source=u, weight=weight)
+        unvisited_terminals.remove(u)
+        for v in unvisited_terminals:
+            H.add_edge(u, v, distance=distances[v], path=paths[v])
+
+    # Use the 'distance' attribute of each edge provided by H.
+    mst_edges = nx.minimum_spanning_edges(H, weight="distance", data=True)
+
+    # Create an iterator over each edge in each shortest path; repeats are okay
+    mst_all_edges = chain.from_iterable(pairwise(d["path"]) for u, v, d in mst_edges)
+    if G.is_multigraph():
+        mst_all_edges = (
+            (u, v, min(G[u][v], key=lambda k: G[u][v][k][weight]))
+            for u, v in mst_all_edges
+        )
+
+    # Find the MST again, over this new set of edges
+    G_S = G.edge_subgraph(mst_all_edges)
+    T_S = nx.minimum_spanning_edges(G_S, weight="weight", data=False)
+
+    # Leaf nodes that are not terminal might still remain; remove them here
+    T_H = G.edge_subgraph(T_S).copy()
+    _remove_nonterminal_leaves(T_H, terminal_nodes)
+
+    return T_H.edges()
+
+
+def _remove_nonterminal_leaves(G, terminals):
+    terminal_set = set(terminals)
+    leaves = {n for n in G if len(set(G[n]) - {n}) == 1}
+    nonterminal_leaves = leaves - terminal_set
+
+    while nonterminal_leaves:
+        # Removing a node may create new non-terminal leaves, so we limit
+        # search for candidate non-terminal nodes to neighbors of current
+        # non-terminal nodes
+        candidate_leaves = set.union(*(set(G[n]) for n in nonterminal_leaves))
+        candidate_leaves -= nonterminal_leaves | terminal_set
+        # Remove current set of non-terminal nodes
+        G.remove_nodes_from(nonterminal_leaves)
+        # Find any new non-terminal nodes from the set of candidates
+        leaves = {n for n in candidate_leaves if len(set(G[n]) - {n}) == 1}
+        nonterminal_leaves = leaves - terminal_set
+
+
+ALGORITHMS = {
+    "kou": _kou_steiner_tree,
+    "mehlhorn": _mehlhorn_steiner_tree,
+}
+
+
+@not_implemented_for("directed")
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def steiner_tree(G, terminal_nodes, weight="weight", method=None):
+    r"""Return an approximation to the minimum Steiner tree of a graph.
+
+    The minimum Steiner tree of `G` w.r.t a set of `terminal_nodes` (also *S*)
+    is a tree within `G` that spans those nodes and has minimum size (sum of
+    edge weights) among all such trees.
+
+    The approximation algorithm is specified with the `method` keyword
+    argument. All three available algorithms produce a tree whose weight is
+    within a ``(2 - (2 / l))`` factor of the weight of the optimal Steiner tree,
+    where ``l`` is the minimum number of leaf nodes across all possible Steiner
+    trees.
+
+    * ``"kou"`` [2]_ (runtime $O(|S| |V|^2)$) computes the minimum spanning tree of
+      the subgraph of the metric closure of *G* induced by the terminal nodes,
+      where the metric closure of *G* is the complete graph in which each edge is
+      weighted by the shortest path distance between the nodes in *G*.
+
+    * ``"mehlhorn"`` [3]_ (runtime $O(|E|+|V|\log|V|)$) modifies Kou et al.'s
+      algorithm, beginning by finding the closest terminal node for each
+      non-terminal. This data is used to create a complete graph containing only
+      the terminal nodes, in which edge is weighted with the shortest path
+      distance between them. The algorithm then proceeds in the same way as Kou
+      et al..
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    terminal_nodes : list
+         A list of terminal nodes for which minimum steiner tree is
+         to be found.
+
+    weight : string (default = 'weight')
+        Use the edge attribute specified by this string as the edge weight.
+        Any edge attribute not present defaults to 1.
+
+    method : string, optional (default = 'mehlhorn')
+        The algorithm to use to approximate the Steiner tree.
+        Supported options: 'kou', 'mehlhorn'.
+        Other inputs produce a ValueError.
+
+    Returns
+    -------
+    NetworkX graph
+        Approximation to the minimum steiner tree of `G` induced by
+        `terminal_nodes` .
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is directed.
+
+    ValueError
+        If the specified `method` is not supported.
+
+    Notes
+    -----
+    For multigraphs, the edge between two nodes with minimum weight is the
+    edge put into the Steiner tree.
+
+
+    References
+    ----------
+    .. [1] Steiner_tree_problem on Wikipedia.
+           https://en.wikipedia.org/wiki/Steiner_tree_problem
+    .. [2] Kou, L., G. Markowsky, and L. Berman. 1981.
+           ‘A Fast Algorithm for Steiner Trees’.
+           Acta Informatica 15 (2): 141–45.
+           https://doi.org/10.1007/BF00288961.
+    .. [3] Mehlhorn, Kurt. 1988.
+           ‘A Faster Approximation Algorithm for the Steiner Problem in Graphs’.
+           Information Processing Letters 27 (3): 125–28.
+           https://doi.org/10.1016/0020-0190(88)90066-X.
+    """
+    if method is None:
+        method = "mehlhorn"
+
+    try:
+        algo = ALGORITHMS[method]
+    except KeyError as e:
+        raise ValueError(f"{method} is not a valid choice for an algorithm.") from e
+
+    edges = algo(G, terminal_nodes, weight)
+    # For multigraph we should add the minimal weight edge keys
+    if G.is_multigraph():
+        edges = (
+            (u, v, min(G[u][v], key=lambda k: G[u][v][k][weight])) for u, v in edges
+        )
+    T = G.edge_subgraph(edges)
+    return T
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/traveling_salesman.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/traveling_salesman.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c6dd52281bc90b7eafbc0fee694771b59a5bfaa
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/traveling_salesman.py
@@ -0,0 +1,1508 @@
+"""
+=================================
+Travelling Salesman Problem (TSP)
+=================================
+
+Implementation of approximate algorithms
+for solving and approximating the TSP problem.
+
+Categories of algorithms which are implemented:
+
+- Christofides (provides a 3/2-approximation of TSP)
+- Greedy
+- Simulated Annealing (SA)
+- Threshold Accepting (TA)
+- Asadpour Asymmetric Traveling Salesman Algorithm
+
+The Travelling Salesman Problem tries to find, given the weight
+(distance) between all points where a salesman has to visit, the
+route so that:
+
+- The total distance (cost) which the salesman travels is minimized.
+- The salesman returns to the starting point.
+- Note that for a complete graph, the salesman visits each point once.
+
+The function `travelling_salesman_problem` allows for incomplete
+graphs by finding all-pairs shortest paths, effectively converting
+the problem to a complete graph problem. It calls one of the
+approximate methods on that problem and then converts the result
+back to the original graph using the previously found shortest paths.
+
+TSP is an NP-hard problem in combinatorial optimization,
+important in operations research and theoretical computer science.
+
+http://en.wikipedia.org/wiki/Travelling_salesman_problem
+"""
+
+import math
+
+import networkx as nx
+from networkx.algorithms.tree.mst import random_spanning_tree
+from networkx.utils import not_implemented_for, pairwise, py_random_state
+
+__all__ = [
+    "traveling_salesman_problem",
+    "christofides",
+    "asadpour_atsp",
+    "greedy_tsp",
+    "simulated_annealing_tsp",
+    "threshold_accepting_tsp",
+]
+
+
+def swap_two_nodes(soln, seed):
+    """Swap two nodes in `soln` to give a neighbor solution.
+
+    Parameters
+    ----------
+    soln : list of nodes
+        Current cycle of nodes
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    list
+        The solution after move is applied. (A neighbor solution.)
+
+    Notes
+    -----
+        This function assumes that the incoming list `soln` is a cycle
+        (that the first and last element are the same) and also that
+        we don't want any move to change the first node in the list
+        (and thus not the last node either).
+
+        The input list is changed as well as returned. Make a copy if needed.
+
+    See Also
+    --------
+        move_one_node
+    """
+    a, b = seed.sample(range(1, len(soln) - 1), k=2)
+    soln[a], soln[b] = soln[b], soln[a]
+    return soln
+
+
+def move_one_node(soln, seed):
+    """Move one node to another position to give a neighbor solution.
+
+    The node to move and the position to move to are chosen randomly.
+    The first and last nodes are left untouched as soln must be a cycle
+    starting at that node.
+
+    Parameters
+    ----------
+    soln : list of nodes
+        Current cycle of nodes
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    list
+        The solution after move is applied. (A neighbor solution.)
+
+    Notes
+    -----
+        This function assumes that the incoming list `soln` is a cycle
+        (that the first and last element are the same) and also that
+        we don't want any move to change the first node in the list
+        (and thus not the last node either).
+
+        The input list is changed as well as returned. Make a copy if needed.
+
+    See Also
+    --------
+        swap_two_nodes
+    """
+    a, b = seed.sample(range(1, len(soln) - 1), k=2)
+    soln.insert(b, soln.pop(a))
+    return soln
+
+
+@not_implemented_for("directed")
+@nx._dispatchable(edge_attrs="weight")
+def christofides(G, weight="weight", tree=None):
+    """Approximate a solution of the traveling salesman problem
+
+    Compute a 3/2-approximation of the traveling salesman problem
+    in a complete undirected graph using Christofides [1]_ algorithm.
+
+    Parameters
+    ----------
+    G : Graph
+        `G` should be a complete weighted undirected graph.
+        The distance between all pairs of nodes should be included.
+
+    weight : string, optional (default="weight")
+        Edge data key corresponding to the edge weight.
+        If any edge does not have this attribute the weight is set to 1.
+
+    tree : NetworkX graph or None (default: None)
+        A minimum spanning tree of G. Or, if None, the minimum spanning
+        tree is computed using :func:`networkx.minimum_spanning_tree`
+
+    Returns
+    -------
+    list
+        List of nodes in `G` along a cycle with a 3/2-approximation of
+        the minimal Hamiltonian cycle.
+
+    References
+    ----------
+    .. [1] Christofides, Nicos. "Worst-case analysis of a new heuristic for
+       the travelling salesman problem." No. RR-388. Carnegie-Mellon Univ
+       Pittsburgh Pa Management Sciences Research Group, 1976.
+    """
+    # Remove selfloops if necessary
+    loop_nodes = nx.nodes_with_selfloops(G)
+    try:
+        node = next(loop_nodes)
+    except StopIteration:
+        pass
+    else:
+        G = G.copy()
+        G.remove_edge(node, node)
+        G.remove_edges_from((n, n) for n in loop_nodes)
+    # Check that G is a complete graph
+    N = len(G) - 1
+    # This check ignores selfloops which is what we want here.
+    if any(len(nbrdict) != N for n, nbrdict in G.adj.items()):
+        raise nx.NetworkXError("G must be a complete graph.")
+
+    if tree is None:
+        tree = nx.minimum_spanning_tree(G, weight=weight)
+    L = G.copy()
+    L.remove_nodes_from([v for v, degree in tree.degree if not (degree % 2)])
+    MG = nx.MultiGraph()
+    MG.add_edges_from(tree.edges)
+    edges = nx.min_weight_matching(L, weight=weight)
+    MG.add_edges_from(edges)
+    return _shortcutting(nx.eulerian_circuit(MG))
+
+
+def _shortcutting(circuit):
+    """Remove duplicate nodes in the path"""
+    nodes = []
+    for u, v in circuit:
+        if v in nodes:
+            continue
+        if not nodes:
+            nodes.append(u)
+        nodes.append(v)
+    nodes.append(nodes[0])
+    return nodes
+
+
+@nx._dispatchable(edge_attrs="weight")
+def traveling_salesman_problem(
+    G, weight="weight", nodes=None, cycle=True, method=None, **kwargs
+):
+    """Find the shortest path in `G` connecting specified nodes
+
+    This function allows approximate solution to the traveling salesman
+    problem on networks that are not complete graphs and/or where the
+    salesman does not need to visit all nodes.
+
+    This function proceeds in two steps. First, it creates a complete
+    graph using the all-pairs shortest_paths between nodes in `nodes`.
+    Edge weights in the new graph are the lengths of the paths
+    between each pair of nodes in the original graph.
+    Second, an algorithm (default: `christofides` for undirected and
+    `asadpour_atsp` for directed) is used to approximate the minimal Hamiltonian
+    cycle on this new graph. The available algorithms are:
+
+     - christofides
+     - greedy_tsp
+     - simulated_annealing_tsp
+     - threshold_accepting_tsp
+     - asadpour_atsp
+
+    Once the Hamiltonian Cycle is found, this function post-processes to
+    accommodate the structure of the original graph. If `cycle` is ``False``,
+    the biggest weight edge is removed to make a Hamiltonian path.
+    Then each edge on the new complete graph used for that analysis is
+    replaced by the shortest_path between those nodes on the original graph.
+    If the input graph `G` includes edges with weights that do not adhere to
+    the triangle inequality, such as when `G` is not a complete graph (i.e
+    length of non-existent edges is infinity), then the returned path may
+    contain some repeating nodes (other than the starting node).
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A possibly weighted graph
+
+    nodes : collection of nodes (default=G.nodes)
+        collection (list, set, etc.) of nodes to visit
+
+    weight : string, optional (default="weight")
+        Edge data key corresponding to the edge weight.
+        If any edge does not have this attribute the weight is set to 1.
+
+    cycle : bool (default: True)
+        Indicates whether a cycle should be returned, or a path.
+        Note: the cycle is the approximate minimal cycle.
+        The path simply removes the biggest edge in that cycle.
+
+    method : function (default: None)
+        A function that returns a cycle on all nodes and approximates
+        the solution to the traveling salesman problem on a complete
+        graph. The returned cycle is then used to find a corresponding
+        solution on `G`. `method` should be callable; take inputs
+        `G`, and `weight`; and return a list of nodes along the cycle.
+
+        Provided options include :func:`christofides`, :func:`greedy_tsp`,
+        :func:`simulated_annealing_tsp` and :func:`threshold_accepting_tsp`.
+
+        If `method is None`: use :func:`christofides` for undirected `G` and
+        :func:`asadpour_atsp` for directed `G`.
+
+    **kwargs : dict
+        Other keyword arguments to be passed to the `method` function passed in.
+
+    Returns
+    -------
+    list
+        List of nodes in `G` along a path with an approximation of the minimal
+        path through `nodes`.
+
+    Raises
+    ------
+    NetworkXError
+        If `G` is a directed graph it has to be strongly connected or the
+        complete version cannot be generated.
+
+    Examples
+    --------
+    >>> tsp = nx.approximation.traveling_salesman_problem
+    >>> G = nx.cycle_graph(9)
+    >>> G[4][5]["weight"] = 5  # all other weights are 1
+    >>> tsp(G, nodes=[3, 6])
+    [3, 2, 1, 0, 8, 7, 6, 7, 8, 0, 1, 2, 3]
+    >>> path = tsp(G, cycle=False)
+    >>> path in ([4, 3, 2, 1, 0, 8, 7, 6, 5], [5, 6, 7, 8, 0, 1, 2, 3, 4])
+    True
+
+    While no longer required, you can still build (curry) your own function
+    to provide parameter values to the methods.
+
+    >>> SA_tsp = nx.approximation.simulated_annealing_tsp
+    >>> method = lambda G, weight: SA_tsp(G, "greedy", weight=weight, temp=500)
+    >>> path = tsp(G, cycle=False, method=method)
+    >>> path in ([4, 3, 2, 1, 0, 8, 7, 6, 5], [5, 6, 7, 8, 0, 1, 2, 3, 4])
+    True
+
+    Otherwise, pass other keyword arguments directly into the tsp function.
+
+    >>> path = tsp(
+    ...     G,
+    ...     cycle=False,
+    ...     method=nx.approximation.simulated_annealing_tsp,
+    ...     init_cycle="greedy",
+    ...     temp=500,
+    ... )
+    >>> path in ([4, 3, 2, 1, 0, 8, 7, 6, 5], [5, 6, 7, 8, 0, 1, 2, 3, 4])
+    True
+    """
+    if method is None:
+        if G.is_directed():
+            method = asadpour_atsp
+        else:
+            method = christofides
+    if nodes is None:
+        nodes = list(G.nodes)
+
+    dist = {}
+    path = {}
+    for n, (d, p) in nx.all_pairs_dijkstra(G, weight=weight):
+        dist[n] = d
+        path[n] = p
+
+    if G.is_directed():
+        # If the graph is not strongly connected, raise an exception
+        if not nx.is_strongly_connected(G):
+            raise nx.NetworkXError("G is not strongly connected")
+        GG = nx.DiGraph()
+    else:
+        GG = nx.Graph()
+    for u in nodes:
+        for v in nodes:
+            if u == v:
+                continue
+            # Ensure that the weight attribute on GG has the
+            # same name as the input graph
+            GG.add_edge(u, v, **{weight: dist[u][v]})
+
+    best_GG = method(GG, weight=weight, **kwargs)
+
+    if not cycle:
+        # find and remove the biggest edge
+        (u, v) = max(pairwise(best_GG), key=lambda x: dist[x[0]][x[1]])
+        pos = best_GG.index(u) + 1
+        while best_GG[pos] != v:
+            pos = best_GG[pos:].index(u) + 1
+        best_GG = best_GG[pos:-1] + best_GG[:pos]
+
+    best_path = []
+    for u, v in pairwise(best_GG):
+        best_path.extend(path[u][v][:-1])
+    best_path.append(v)
+    return best_path
+
+
+@not_implemented_for("undirected")
+@py_random_state(2)
+@nx._dispatchable(edge_attrs="weight", mutates_input=True)
+def asadpour_atsp(G, weight="weight", seed=None, source=None):
+    """
+    Returns an approximate solution to the traveling salesman problem.
+
+    This approximate solution is one of the best known approximations for the
+    asymmetric traveling salesman problem developed by Asadpour et al,
+    [1]_. The algorithm first solves the Held-Karp relaxation to find a lower
+    bound for the weight of the cycle. Next, it constructs an exponential
+    distribution of undirected spanning trees where the probability of an
+    edge being in the tree corresponds to the weight of that edge using a
+    maximum entropy rounding scheme. Next we sample that distribution
+    $2 \\lceil \\ln n \\rceil$ times and save the minimum sampled tree once the
+    direction of the arcs is added back to the edges. Finally, we augment
+    then short circuit that graph to find the approximate tour for the
+    salesman.
+
+    Parameters
+    ----------
+    G : nx.DiGraph
+        The graph should be a complete weighted directed graph. The
+        distance between all paris of nodes should be included and the triangle
+        inequality should hold. That is, the direct edge between any two nodes
+        should be the path of least cost.
+
+    weight : string, optional (default="weight")
+        Edge data key corresponding to the edge weight.
+        If any edge does not have this attribute the weight is set to 1.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    source : node label (default=`None`)
+        If given, return the cycle starting and ending at the given node.
+
+    Returns
+    -------
+    cycle : list of nodes
+        Returns the cycle (list of nodes) that a salesman can follow to minimize
+        the total weight of the trip.
+
+    Raises
+    ------
+    NetworkXError
+        If `G` is not complete or has less than two nodes, the algorithm raises
+        an exception.
+
+    NetworkXError
+        If `source` is not `None` and is not a node in `G`, the algorithm raises
+        an exception.
+
+    NetworkXNotImplemented
+        If `G` is an undirected graph.
+
+    References
+    ----------
+    .. [1] A. Asadpour, M. X. Goemans, A. Madry, S. O. Gharan, and A. Saberi,
+       An o(log n/log log n)-approximation algorithm for the asymmetric
+       traveling salesman problem, Operations research, 65 (2017),
+       pp. 1043–1061
+
+    Examples
+    --------
+    >>> import networkx as nx
+    >>> import networkx.algorithms.approximation as approx
+    >>> G = nx.complete_graph(3, create_using=nx.DiGraph)
+    >>> nx.set_edge_attributes(
+    ...     G,
+    ...     {(0, 1): 2, (1, 2): 2, (2, 0): 2, (0, 2): 1, (2, 1): 1, (1, 0): 1},
+    ...     "weight",
+    ... )
+    >>> tour = approx.asadpour_atsp(G, source=0)
+    >>> tour
+    [0, 2, 1, 0]
+    """
+    from math import ceil, exp
+    from math import log as ln
+
+    # Check that G is a complete graph
+    N = len(G) - 1
+    if N < 1:
+        raise nx.NetworkXError("G must have at least two nodes")
+    # This check ignores selfloops which is what we want here.
+    if any(len(nbrdict) - (n in nbrdict) != N for n, nbrdict in G.adj.items()):
+        raise nx.NetworkXError("G is not a complete DiGraph")
+    # Check that the source vertex, if given, is in the graph
+    if source is not None and source not in G.nodes:
+        raise nx.NetworkXError("Given source node not in G.")
+    # handle 2 node case
+    if N == 1:
+        if source is None:
+            return list(G)
+        return [source, next(n for n in G if n != source)]
+
+    opt_hk, z_star = held_karp_ascent(G, weight)
+
+    # Test to see if the ascent method found an integer solution or a fractional
+    # solution. If it is integral then z_star is a nx.Graph, otherwise it is
+    # a dict
+    if not isinstance(z_star, dict):
+        # Here we are using the shortcutting method to go from the list of edges
+        # returned from eulerian_circuit to a list of nodes
+        return _shortcutting(nx.eulerian_circuit(z_star, source=source))
+
+    # Create the undirected support of z_star
+    z_support = nx.MultiGraph()
+    for u, v in z_star:
+        if (u, v) not in z_support.edges:
+            edge_weight = min(G[u][v][weight], G[v][u][weight])
+            z_support.add_edge(u, v, **{weight: edge_weight})
+
+    # Create the exponential distribution of spanning trees
+    gamma = spanning_tree_distribution(z_support, z_star)
+
+    # Write the lambda values to the edges of z_support
+    z_support = nx.Graph(z_support)
+    lambda_dict = {(u, v): exp(gamma[(u, v)]) for u, v in z_support.edges()}
+    nx.set_edge_attributes(z_support, lambda_dict, "weight")
+    del gamma, lambda_dict
+
+    # Sample 2 * ceil( ln(n) ) spanning trees and record the minimum one
+    minimum_sampled_tree = None
+    minimum_sampled_tree_weight = math.inf
+    for _ in range(2 * ceil(ln(G.number_of_nodes()))):
+        sampled_tree = random_spanning_tree(z_support, "weight", seed=seed)
+        sampled_tree_weight = sampled_tree.size(weight)
+        if sampled_tree_weight < minimum_sampled_tree_weight:
+            minimum_sampled_tree = sampled_tree.copy()
+            minimum_sampled_tree_weight = sampled_tree_weight
+
+    # Orient the edges in that tree to keep the cost of the tree the same.
+    t_star = nx.MultiDiGraph()
+    for u, v, d in minimum_sampled_tree.edges(data=weight):
+        if d == G[u][v][weight]:
+            t_star.add_edge(u, v, **{weight: d})
+        else:
+            t_star.add_edge(v, u, **{weight: d})
+
+    # Find the node demands needed to neutralize the flow of t_star in G
+    node_demands = {n: t_star.out_degree(n) - t_star.in_degree(n) for n in t_star}
+    nx.set_node_attributes(G, node_demands, "demand")
+
+    # Find the min_cost_flow
+    flow_dict = nx.min_cost_flow(G, "demand")
+
+    # Build the flow into t_star
+    for source, values in flow_dict.items():
+        for target in values:
+            if (source, target) not in t_star.edges and values[target] > 0:
+                # IF values[target] > 0 we have to add that many edges
+                for _ in range(values[target]):
+                    t_star.add_edge(source, target)
+
+    # Return the shortcut eulerian circuit
+    circuit = nx.eulerian_circuit(t_star, source=source)
+    return _shortcutting(circuit)
+
+
+@nx._dispatchable(edge_attrs="weight", mutates_input=True, returns_graph=True)
+def held_karp_ascent(G, weight="weight"):
+    """
+    Minimizes the Held-Karp relaxation of the TSP for `G`
+
+    Solves the Held-Karp relaxation of the input complete digraph and scales
+    the output solution for use in the Asadpour [1]_ ASTP algorithm.
+
+    The Held-Karp relaxation defines the lower bound for solutions to the
+    ATSP, although it does return a fractional solution. This is used in the
+    Asadpour algorithm as an initial solution which is later rounded to a
+    integral tree within the spanning tree polytopes. This function solves
+    the relaxation with the branch and bound method in [2]_.
+
+    Parameters
+    ----------
+    G : nx.DiGraph
+        The graph should be a complete weighted directed graph.
+        The distance between all paris of nodes should be included.
+
+    weight : string, optional (default="weight")
+        Edge data key corresponding to the edge weight.
+        If any edge does not have this attribute the weight is set to 1.
+
+    Returns
+    -------
+    OPT : float
+        The cost for the optimal solution to the Held-Karp relaxation
+    z : dict or nx.Graph
+        A symmetrized and scaled version of the optimal solution to the
+        Held-Karp relaxation for use in the Asadpour algorithm.
+
+        If an integral solution is found, then that is an optimal solution for
+        the ATSP problem and that is returned instead.
+
+    References
+    ----------
+    .. [1] A. Asadpour, M. X. Goemans, A. Madry, S. O. Gharan, and A. Saberi,
+       An o(log n/log log n)-approximation algorithm for the asymmetric
+       traveling salesman problem, Operations research, 65 (2017),
+       pp. 1043–1061
+
+    .. [2] M. Held, R. M. Karp, The traveling-salesman problem and minimum
+           spanning trees, Operations Research, 1970-11-01, Vol. 18 (6),
+           pp.1138-1162
+    """
+    import numpy as np
+    import scipy as sp
+
+    def k_pi():
+        """
+        Find the set of minimum 1-Arborescences for G at point pi.
+
+        Returns
+        -------
+        Set
+            The set of minimum 1-Arborescences
+        """
+        # Create a copy of G without vertex 1.
+        G_1 = G.copy()
+        minimum_1_arborescences = set()
+        minimum_1_arborescence_weight = math.inf
+
+        # node is node '1' in the Held and Karp paper
+        n = next(G.__iter__())
+        G_1.remove_node(n)
+
+        # Iterate over the spanning arborescences of the graph until we know
+        # that we have found the minimum 1-arborescences. My proposed strategy
+        # is to find the most extensive root to connect to from 'node 1' and
+        # the least expensive one. We then iterate over arborescences until
+        # the cost of the basic arborescence is the cost of the minimum one
+        # plus the difference between the most and least expensive roots,
+        # that way the cost of connecting 'node 1' will by definition not by
+        # minimum
+        min_root = {"node": None, weight: math.inf}
+        max_root = {"node": None, weight: -math.inf}
+        for u, v, d in G.edges(n, data=True):
+            if d[weight] < min_root[weight]:
+                min_root = {"node": v, weight: d[weight]}
+            if d[weight] > max_root[weight]:
+                max_root = {"node": v, weight: d[weight]}
+
+        min_in_edge = min(G.in_edges(n, data=True), key=lambda x: x[2][weight])
+        min_root[weight] = min_root[weight] + min_in_edge[2][weight]
+        max_root[weight] = max_root[weight] + min_in_edge[2][weight]
+
+        min_arb_weight = math.inf
+        for arb in nx.ArborescenceIterator(G_1):
+            arb_weight = arb.size(weight)
+            if min_arb_weight == math.inf:
+                min_arb_weight = arb_weight
+            elif arb_weight > min_arb_weight + max_root[weight] - min_root[weight]:
+                break
+            # We have to pick the root node of the arborescence for the out
+            # edge of the first vertex as that is the only node without an
+            # edge directed into it.
+            for N, deg in arb.in_degree:
+                if deg == 0:
+                    # root found
+                    arb.add_edge(n, N, **{weight: G[n][N][weight]})
+                    arb_weight += G[n][N][weight]
+                    break
+
+            # We can pick the minimum weight in-edge for the vertex with
+            # a cycle. If there are multiple edges with the same, minimum
+            # weight, We need to add all of them.
+            #
+            # Delete the edge (N, v) so that we cannot pick it.
+            edge_data = G[N][n]
+            G.remove_edge(N, n)
+            min_weight = min(G.in_edges(n, data=weight), key=lambda x: x[2])[2]
+            min_edges = [
+                (u, v, d) for u, v, d in G.in_edges(n, data=weight) if d == min_weight
+            ]
+            for u, v, d in min_edges:
+                new_arb = arb.copy()
+                new_arb.add_edge(u, v, **{weight: d})
+                new_arb_weight = arb_weight + d
+                # Check to see the weight of the arborescence, if it is a
+                # new minimum, clear all of the old potential minimum
+                # 1-arborescences and add this is the only one. If its
+                # weight is above the known minimum, do not add it.
+                if new_arb_weight < minimum_1_arborescence_weight:
+                    minimum_1_arborescences.clear()
+                    minimum_1_arborescence_weight = new_arb_weight
+                # We have a 1-arborescence, add it to the set
+                if new_arb_weight == minimum_1_arborescence_weight:
+                    minimum_1_arborescences.add(new_arb)
+            G.add_edge(N, n, **edge_data)
+
+        return minimum_1_arborescences
+
+    def direction_of_ascent():
+        """
+        Find the direction of ascent at point pi.
+
+        See [1]_ for more information.
+
+        Returns
+        -------
+        dict
+            A mapping from the nodes of the graph which represents the direction
+            of ascent.
+
+        References
+        ----------
+        .. [1] M. Held, R. M. Karp, The traveling-salesman problem and minimum
+           spanning trees, Operations Research, 1970-11-01, Vol. 18 (6),
+           pp.1138-1162
+        """
+        # 1. Set d equal to the zero n-vector.
+        d = {}
+        for n in G:
+            d[n] = 0
+        del n
+        # 2. Find a 1-Arborescence T^k such that k is in K(pi, d).
+        minimum_1_arborescences = k_pi()
+        while True:
+            # Reduce K(pi) to K(pi, d)
+            # Find the arborescence in K(pi) which increases the lest in
+            # direction d
+            min_k_d_weight = math.inf
+            min_k_d = None
+            for arborescence in minimum_1_arborescences:
+                weighted_cost = 0
+                for n, deg in arborescence.degree:
+                    weighted_cost += d[n] * (deg - 2)
+                if weighted_cost < min_k_d_weight:
+                    min_k_d_weight = weighted_cost
+                    min_k_d = arborescence
+
+            # 3. If sum of d_i * v_{i, k} is greater than zero, terminate
+            if min_k_d_weight > 0:
+                return d, min_k_d
+            # 4. d_i = d_i + v_{i, k}
+            for n, deg in min_k_d.degree:
+                d[n] += deg - 2
+            # Check that we do not need to terminate because the direction
+            # of ascent does not exist. This is done with linear
+            # programming.
+            c = np.full(len(minimum_1_arborescences), -1, dtype=int)
+            a_eq = np.empty((len(G) + 1, len(minimum_1_arborescences)), dtype=int)
+            b_eq = np.zeros(len(G) + 1, dtype=int)
+            b_eq[len(G)] = 1
+            for arb_count, arborescence in enumerate(minimum_1_arborescences):
+                n_count = len(G) - 1
+                for n, deg in arborescence.degree:
+                    a_eq[n_count][arb_count] = deg - 2
+                    n_count -= 1
+                a_eq[len(G)][arb_count] = 1
+            program_result = sp.optimize.linprog(
+                c, A_eq=a_eq, b_eq=b_eq, method="highs-ipm"
+            )
+            # If the constants exist, then the direction of ascent doesn't
+            if program_result.success:
+                # There is no direction of ascent
+                return None, minimum_1_arborescences
+
+            # 5. GO TO 2
+
+    def find_epsilon(k, d):
+        """
+        Given the direction of ascent at pi, find the maximum distance we can go
+        in that direction.
+
+        Parameters
+        ----------
+        k_xy : set
+            The set of 1-arborescences which have the minimum rate of increase
+            in the direction of ascent
+
+        d : dict
+            The direction of ascent
+
+        Returns
+        -------
+        float
+            The distance we can travel in direction `d`
+        """
+        min_epsilon = math.inf
+        for e_u, e_v, e_w in G.edges(data=weight):
+            if (e_u, e_v) in k.edges:
+                continue
+            # Now, I have found a condition which MUST be true for the edges to
+            # be a valid substitute. The edge in the graph which is the
+            # substitute is the one with the same terminated end. This can be
+            # checked rather simply.
+            #
+            # Find the edge within k which is the substitute. Because k is a
+            # 1-arborescence, we know that they is only one such edges
+            # leading into every vertex.
+            if len(k.in_edges(e_v, data=weight)) > 1:
+                raise Exception
+            sub_u, sub_v, sub_w = next(k.in_edges(e_v, data=weight).__iter__())
+            k.add_edge(e_u, e_v, **{weight: e_w})
+            k.remove_edge(sub_u, sub_v)
+            if (
+                max(d for n, d in k.in_degree()) <= 1
+                and len(G) == k.number_of_edges()
+                and nx.is_weakly_connected(k)
+            ):
+                # Ascent method calculation
+                if d[sub_u] == d[e_u] or sub_w == e_w:
+                    # Revert to the original graph
+                    k.remove_edge(e_u, e_v)
+                    k.add_edge(sub_u, sub_v, **{weight: sub_w})
+                    continue
+                epsilon = (sub_w - e_w) / (d[e_u] - d[sub_u])
+                if 0 < epsilon < min_epsilon:
+                    min_epsilon = epsilon
+            # Revert to the original graph
+            k.remove_edge(e_u, e_v)
+            k.add_edge(sub_u, sub_v, **{weight: sub_w})
+
+        return min_epsilon
+
+    # I have to know that the elements in pi correspond to the correct elements
+    # in the direction of ascent, even if the node labels are not integers.
+    # Thus, I will use dictionaries to made that mapping.
+    pi_dict = {}
+    for n in G:
+        pi_dict[n] = 0
+    del n
+    original_edge_weights = {}
+    for u, v, d in G.edges(data=True):
+        original_edge_weights[(u, v)] = d[weight]
+    dir_ascent, k_d = direction_of_ascent()
+    while dir_ascent is not None:
+        max_distance = find_epsilon(k_d, dir_ascent)
+        for n, v in dir_ascent.items():
+            pi_dict[n] += max_distance * v
+        for u, v, d in G.edges(data=True):
+            d[weight] = original_edge_weights[(u, v)] + pi_dict[u]
+        dir_ascent, k_d = direction_of_ascent()
+    nx._clear_cache(G)
+    # k_d is no longer an individual 1-arborescence but rather a set of
+    # minimal 1-arborescences at the maximum point of the polytope and should
+    # be reflected as such
+    k_max = k_d
+
+    # Search for a cycle within k_max. If a cycle exists, return it as the
+    # solution
+    for k in k_max:
+        if len([n for n in k if k.degree(n) == 2]) == G.order():
+            # Tour found
+            # TODO: this branch does not restore original_edge_weights of G!
+            return k.size(weight), k
+
+    # Write the original edge weights back to G and every member of k_max at
+    # the maximum point. Also average the number of times that edge appears in
+    # the set of minimal 1-arborescences.
+    x_star = {}
+    size_k_max = len(k_max)
+    for u, v, d in G.edges(data=True):
+        edge_count = 0
+        d[weight] = original_edge_weights[(u, v)]
+        for k in k_max:
+            if (u, v) in k.edges():
+                edge_count += 1
+                k[u][v][weight] = original_edge_weights[(u, v)]
+        x_star[(u, v)] = edge_count / size_k_max
+    # Now symmetrize the edges in x_star and scale them according to (5) in
+    # reference [1]
+    z_star = {}
+    scale_factor = (G.order() - 1) / G.order()
+    for u, v in x_star:
+        frequency = x_star[(u, v)] + x_star[(v, u)]
+        if frequency > 0:
+            z_star[(u, v)] = scale_factor * frequency
+    del x_star
+    # Return the optimal weight and the z dict
+    return next(k_max.__iter__()).size(weight), z_star
+
+
+@nx._dispatchable
+def spanning_tree_distribution(G, z):
+    """
+    Find the asadpour exponential distribution of spanning trees.
+
+    Solves the Maximum Entropy Convex Program in the Asadpour algorithm [1]_
+    using the approach in section 7 to build an exponential distribution of
+    undirected spanning trees.
+
+    This algorithm ensures that the probability of any edge in a spanning
+    tree is proportional to the sum of the probabilities of the tress
+    containing that edge over the sum of the probabilities of all spanning
+    trees of the graph.
+
+    Parameters
+    ----------
+    G : nx.MultiGraph
+        The undirected support graph for the Held Karp relaxation
+
+    z : dict
+        The output of `held_karp_ascent()`, a scaled version of the Held-Karp
+        solution.
+
+    Returns
+    -------
+    gamma : dict
+        The probability distribution which approximately preserves the marginal
+        probabilities of `z`.
+    """
+    from math import exp
+    from math import log as ln
+
+    def q(e):
+        """
+        The value of q(e) is described in the Asadpour paper is "the
+        probability that edge e will be included in a spanning tree T that is
+        chosen with probability proportional to exp(gamma(T))" which
+        basically means that it is the total probability of the edge appearing
+        across the whole distribution.
+
+        Parameters
+        ----------
+        e : tuple
+            The `(u, v)` tuple describing the edge we are interested in
+
+        Returns
+        -------
+        float
+            The probability that a spanning tree chosen according to the
+            current values of gamma will include edge `e`.
+        """
+        # Create the laplacian matrices
+        for u, v, d in G.edges(data=True):
+            d[lambda_key] = exp(gamma[(u, v)])
+        G_Kirchhoff = nx.number_of_spanning_trees(G, weight=lambda_key)
+        G_e = nx.contracted_edge(G, e, self_loops=False)
+        G_e_Kirchhoff = nx.number_of_spanning_trees(G_e, weight=lambda_key)
+
+        # Multiply by the weight of the contracted edge since it is not included
+        # in the total weight of the contracted graph.
+        return exp(gamma[(e[0], e[1])]) * G_e_Kirchhoff / G_Kirchhoff
+
+    # initialize gamma to the zero dict
+    gamma = {}
+    for u, v, _ in G.edges:
+        gamma[(u, v)] = 0
+
+    # set epsilon
+    EPSILON = 0.2
+
+    # pick an edge attribute name that is unlikely to be in the graph
+    lambda_key = "spanning_tree_distribution's secret attribute name for lambda"
+
+    while True:
+        # We need to know that know that no values of q_e are greater than
+        # (1 + epsilon) * z_e, however changing one gamma value can increase the
+        # value of a different q_e, so we have to complete the for loop without
+        # changing anything for the condition to be meet
+        in_range_count = 0
+        # Search for an edge with q_e > (1 + epsilon) * z_e
+        for u, v in gamma:
+            e = (u, v)
+            q_e = q(e)
+            z_e = z[e]
+            if q_e > (1 + EPSILON) * z_e:
+                delta = ln(
+                    (q_e * (1 - (1 + EPSILON / 2) * z_e))
+                    / ((1 - q_e) * (1 + EPSILON / 2) * z_e)
+                )
+                gamma[e] -= delta
+                # Check that delta had the desired effect
+                new_q_e = q(e)
+                desired_q_e = (1 + EPSILON / 2) * z_e
+                if round(new_q_e, 8) != round(desired_q_e, 8):
+                    raise nx.NetworkXError(
+                        f"Unable to modify probability for edge ({u}, {v})"
+                    )
+            else:
+                in_range_count += 1
+        # Check if the for loop terminated without changing any gamma
+        if in_range_count == len(gamma):
+            break
+
+    # Remove the new edge attributes
+    for _, _, d in G.edges(data=True):
+        if lambda_key in d:
+            del d[lambda_key]
+
+    return gamma
+
+
+@nx._dispatchable(edge_attrs="weight")
+def greedy_tsp(G, weight="weight", source=None):
+    """Return a low cost cycle starting at `source` and its cost.
+
+    This approximates a solution to the traveling salesman problem.
+    It finds a cycle of all the nodes that a salesman can visit in order
+    to visit many nodes while minimizing total distance.
+    It uses a simple greedy algorithm.
+    In essence, this function returns a large cycle given a source point
+    for which the total cost of the cycle is minimized.
+
+    Parameters
+    ----------
+    G : Graph
+        The Graph should be a complete weighted undirected graph.
+        The distance between all pairs of nodes should be included.
+
+    weight : string, optional (default="weight")
+        Edge data key corresponding to the edge weight.
+        If any edge does not have this attribute the weight is set to 1.
+
+    source : node, optional (default: first node in list(G))
+        Starting node.  If None, defaults to ``next(iter(G))``
+
+    Returns
+    -------
+    cycle : list of nodes
+        Returns the cycle (list of nodes) that a salesman
+        can follow to minimize total weight of the trip.
+
+    Raises
+    ------
+    NetworkXError
+        If `G` is not complete, the algorithm raises an exception.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import approximation as approx
+    >>> G = nx.DiGraph()
+    >>> G.add_weighted_edges_from(
+    ...     {
+    ...         ("A", "B", 3),
+    ...         ("A", "C", 17),
+    ...         ("A", "D", 14),
+    ...         ("B", "A", 3),
+    ...         ("B", "C", 12),
+    ...         ("B", "D", 16),
+    ...         ("C", "A", 13),
+    ...         ("C", "B", 12),
+    ...         ("C", "D", 4),
+    ...         ("D", "A", 14),
+    ...         ("D", "B", 15),
+    ...         ("D", "C", 2),
+    ...     }
+    ... )
+    >>> cycle = approx.greedy_tsp(G, source="D")
+    >>> cost = sum(G[n][nbr]["weight"] for n, nbr in nx.utils.pairwise(cycle))
+    >>> cycle
+    ['D', 'C', 'B', 'A', 'D']
+    >>> cost
+    31
+
+    Notes
+    -----
+    This implementation of a greedy algorithm is based on the following:
+
+    - The algorithm adds a node to the solution at every iteration.
+    - The algorithm selects a node not already in the cycle whose connection
+      to the previous node adds the least cost to the cycle.
+
+    A greedy algorithm does not always give the best solution.
+    However, it can construct a first feasible solution which can
+    be passed as a parameter to an iterative improvement algorithm such
+    as Simulated Annealing, or Threshold Accepting.
+
+    Time complexity: It has a running time $O(|V|^2)$
+    """
+    # Check that G is a complete graph
+    N = len(G) - 1
+    # This check ignores selfloops which is what we want here.
+    if any(len(nbrdict) - (n in nbrdict) != N for n, nbrdict in G.adj.items()):
+        raise nx.NetworkXError("G must be a complete graph.")
+
+    if source is None:
+        source = nx.utils.arbitrary_element(G)
+
+    if G.number_of_nodes() == 2:
+        neighbor = next(G.neighbors(source))
+        return [source, neighbor, source]
+
+    nodeset = set(G)
+    nodeset.remove(source)
+    cycle = [source]
+    next_node = source
+    while nodeset:
+        nbrdict = G[next_node]
+        next_node = min(nodeset, key=lambda n: nbrdict[n].get(weight, 1))
+        cycle.append(next_node)
+        nodeset.remove(next_node)
+    cycle.append(cycle[0])
+    return cycle
+
+
+@py_random_state(9)
+@nx._dispatchable(edge_attrs="weight")
+def simulated_annealing_tsp(
+    G,
+    init_cycle,
+    weight="weight",
+    source=None,
+    temp=100,
+    move="1-1",
+    max_iterations=10,
+    N_inner=100,
+    alpha=0.01,
+    seed=None,
+):
+    """Returns an approximate solution to the traveling salesman problem.
+
+    This function uses simulated annealing to approximate the minimal cost
+    cycle through the nodes. Starting from a suboptimal solution, simulated
+    annealing perturbs that solution, occasionally accepting changes that make
+    the solution worse to escape from a locally optimal solution. The chance
+    of accepting such changes decreases over the iterations to encourage
+    an optimal result.  In summary, the function returns a cycle starting
+    at `source` for which the total cost is minimized. It also returns the cost.
+
+    The chance of accepting a proposed change is related to a parameter called
+    the temperature (annealing has a physical analogue of steel hardening
+    as it cools). As the temperature is reduced, the chance of moves that
+    increase cost goes down.
+
+    Parameters
+    ----------
+    G : Graph
+        `G` should be a complete weighted graph.
+        The distance between all pairs of nodes should be included.
+
+    init_cycle : list of all nodes or "greedy"
+        The initial solution (a cycle through all nodes returning to the start).
+        This argument has no default to make you think about it.
+        If "greedy", use `greedy_tsp(G, weight)`.
+        Other common starting cycles are `list(G) + [next(iter(G))]` or the final
+        result of `simulated_annealing_tsp` when doing `threshold_accepting_tsp`.
+
+    weight : string, optional (default="weight")
+        Edge data key corresponding to the edge weight.
+        If any edge does not have this attribute the weight is set to 1.
+
+    source : node, optional (default: first node in list(G))
+        Starting node.  If None, defaults to ``next(iter(G))``
+
+    temp : int, optional (default=100)
+        The algorithm's temperature parameter. It represents the initial
+        value of temperature
+
+    move : "1-1" or "1-0" or function, optional (default="1-1")
+        Indicator of what move to use when finding new trial solutions.
+        Strings indicate two special built-in moves:
+
+        - "1-1": 1-1 exchange which transposes the position
+          of two elements of the current solution.
+          The function called is :func:`swap_two_nodes`.
+          For example if we apply 1-1 exchange in the solution
+          ``A = [3, 2, 1, 4, 3]``
+          we can get the following by the transposition of 1 and 4 elements:
+          ``A' = [3, 2, 4, 1, 3]``
+        - "1-0": 1-0 exchange which moves an node in the solution
+          to a new position.
+          The function called is :func:`move_one_node`.
+          For example if we apply 1-0 exchange in the solution
+          ``A = [3, 2, 1, 4, 3]``
+          we can transfer the fourth element to the second position:
+          ``A' = [3, 4, 2, 1, 3]``
+
+        You may provide your own functions to enact a move from
+        one solution to a neighbor solution. The function must take
+        the solution as input along with a `seed` input to control
+        random number generation (see the `seed` input here).
+        Your function should maintain the solution as a cycle with
+        equal first and last node and all others appearing once.
+        Your function should return the new solution.
+
+    max_iterations : int, optional (default=10)
+        Declared done when this number of consecutive iterations of
+        the outer loop occurs without any change in the best cost solution.
+
+    N_inner : int, optional (default=100)
+        The number of iterations of the inner loop.
+
+    alpha : float between (0, 1), optional (default=0.01)
+        Percentage of temperature decrease in each iteration
+        of outer loop
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    cycle : list of nodes
+        Returns the cycle (list of nodes) that a salesman
+        can follow to minimize total weight of the trip.
+
+    Raises
+    ------
+    NetworkXError
+        If `G` is not complete the algorithm raises an exception.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import approximation as approx
+    >>> G = nx.DiGraph()
+    >>> G.add_weighted_edges_from(
+    ...     {
+    ...         ("A", "B", 3),
+    ...         ("A", "C", 17),
+    ...         ("A", "D", 14),
+    ...         ("B", "A", 3),
+    ...         ("B", "C", 12),
+    ...         ("B", "D", 16),
+    ...         ("C", "A", 13),
+    ...         ("C", "B", 12),
+    ...         ("C", "D", 4),
+    ...         ("D", "A", 14),
+    ...         ("D", "B", 15),
+    ...         ("D", "C", 2),
+    ...     }
+    ... )
+    >>> cycle = approx.simulated_annealing_tsp(G, "greedy", source="D")
+    >>> cost = sum(G[n][nbr]["weight"] for n, nbr in nx.utils.pairwise(cycle))
+    >>> cycle
+    ['D', 'C', 'B', 'A', 'D']
+    >>> cost
+    31
+    >>> incycle = ["D", "B", "A", "C", "D"]
+    >>> cycle = approx.simulated_annealing_tsp(G, incycle, source="D")
+    >>> cost = sum(G[n][nbr]["weight"] for n, nbr in nx.utils.pairwise(cycle))
+    >>> cycle
+    ['D', 'C', 'B', 'A', 'D']
+    >>> cost
+    31
+
+    Notes
+    -----
+    Simulated Annealing is a metaheuristic local search algorithm.
+    The main characteristic of this algorithm is that it accepts
+    even solutions which lead to the increase of the cost in order
+    to escape from low quality local optimal solutions.
+
+    This algorithm needs an initial solution. If not provided, it is
+    constructed by a simple greedy algorithm. At every iteration, the
+    algorithm selects thoughtfully a neighbor solution.
+    Consider $c(x)$ cost of current solution and $c(x')$ cost of a
+    neighbor solution.
+    If $c(x') - c(x) <= 0$ then the neighbor solution becomes the current
+    solution for the next iteration. Otherwise, the algorithm accepts
+    the neighbor solution with probability $p = exp - ([c(x') - c(x)] / temp)$.
+    Otherwise the current solution is retained.
+
+    `temp` is a parameter of the algorithm and represents temperature.
+
+    Time complexity:
+    For $N_i$ iterations of the inner loop and $N_o$ iterations of the
+    outer loop, this algorithm has running time $O(N_i * N_o * |V|)$.
+
+    For more information and how the algorithm is inspired see:
+    http://en.wikipedia.org/wiki/Simulated_annealing
+    """
+    if move == "1-1":
+        move = swap_two_nodes
+    elif move == "1-0":
+        move = move_one_node
+    if init_cycle == "greedy":
+        # Construct an initial solution using a greedy algorithm.
+        cycle = greedy_tsp(G, weight=weight, source=source)
+        if G.number_of_nodes() == 2:
+            return cycle
+
+    else:
+        cycle = list(init_cycle)
+        if source is None:
+            source = cycle[0]
+        elif source != cycle[0]:
+            raise nx.NetworkXError("source must be first node in init_cycle")
+        if cycle[0] != cycle[-1]:
+            raise nx.NetworkXError("init_cycle must be a cycle. (return to start)")
+
+        if len(cycle) - 1 != len(G) or len(set(G.nbunch_iter(cycle))) != len(G):
+            raise nx.NetworkXError("init_cycle should be a cycle over all nodes in G.")
+
+        # Check that G is a complete graph
+        N = len(G) - 1
+        # This check ignores selfloops which is what we want here.
+        if any(len(nbrdict) - (n in nbrdict) != N for n, nbrdict in G.adj.items()):
+            raise nx.NetworkXError("G must be a complete graph.")
+
+        if G.number_of_nodes() == 2:
+            neighbor = next(G.neighbors(source))
+            return [source, neighbor, source]
+
+    # Find the cost of initial solution
+    cost = sum(G[u][v].get(weight, 1) for u, v in pairwise(cycle))
+
+    count = 0
+    best_cycle = cycle.copy()
+    best_cost = cost
+    while count <= max_iterations and temp > 0:
+        count += 1
+        for i in range(N_inner):
+            adj_sol = move(cycle, seed)
+            adj_cost = sum(G[u][v].get(weight, 1) for u, v in pairwise(adj_sol))
+            delta = adj_cost - cost
+            if delta <= 0:
+                # Set current solution the adjacent solution.
+                cycle = adj_sol
+                cost = adj_cost
+
+                if cost < best_cost:
+                    count = 0
+                    best_cycle = cycle.copy()
+                    best_cost = cost
+            else:
+                # Accept even a worse solution with probability p.
+                p = math.exp(-delta / temp)
+                if p >= seed.random():
+                    cycle = adj_sol
+                    cost = adj_cost
+        temp -= temp * alpha
+
+    return best_cycle
+
+
+@py_random_state(9)
+@nx._dispatchable(edge_attrs="weight")
+def threshold_accepting_tsp(
+    G,
+    init_cycle,
+    weight="weight",
+    source=None,
+    threshold=1,
+    move="1-1",
+    max_iterations=10,
+    N_inner=100,
+    alpha=0.1,
+    seed=None,
+):
+    """Returns an approximate solution to the traveling salesman problem.
+
+    This function uses threshold accepting methods to approximate the minimal cost
+    cycle through the nodes. Starting from a suboptimal solution, threshold
+    accepting methods perturb that solution, accepting any changes that make
+    the solution no worse than increasing by a threshold amount. Improvements
+    in cost are accepted, but so are changes leading to small increases in cost.
+    This allows the solution to leave suboptimal local minima in solution space.
+    The threshold is decreased slowly as iterations proceed helping to ensure
+    an optimum. In summary, the function returns a cycle starting at `source`
+    for which the total cost is minimized.
+
+    Parameters
+    ----------
+    G : Graph
+        `G` should be a complete weighted graph.
+        The distance between all pairs of nodes should be included.
+
+    init_cycle : list or "greedy"
+        The initial solution (a cycle through all nodes returning to the start).
+        This argument has no default to make you think about it.
+        If "greedy", use `greedy_tsp(G, weight)`.
+        Other common starting cycles are `list(G) + [next(iter(G))]` or the final
+        result of `simulated_annealing_tsp` when doing `threshold_accepting_tsp`.
+
+    weight : string, optional (default="weight")
+        Edge data key corresponding to the edge weight.
+        If any edge does not have this attribute the weight is set to 1.
+
+    source : node, optional (default: first node in list(G))
+        Starting node.  If None, defaults to ``next(iter(G))``
+
+    threshold : int, optional (default=1)
+        The algorithm's threshold parameter. It represents the initial
+        threshold's value
+
+    move : "1-1" or "1-0" or function, optional (default="1-1")
+        Indicator of what move to use when finding new trial solutions.
+        Strings indicate two special built-in moves:
+
+        - "1-1": 1-1 exchange which transposes the position
+          of two elements of the current solution.
+          The function called is :func:`swap_two_nodes`.
+          For example if we apply 1-1 exchange in the solution
+          ``A = [3, 2, 1, 4, 3]``
+          we can get the following by the transposition of 1 and 4 elements:
+          ``A' = [3, 2, 4, 1, 3]``
+        - "1-0": 1-0 exchange which moves an node in the solution
+          to a new position.
+          The function called is :func:`move_one_node`.
+          For example if we apply 1-0 exchange in the solution
+          ``A = [3, 2, 1, 4, 3]``
+          we can transfer the fourth element to the second position:
+          ``A' = [3, 4, 2, 1, 3]``
+
+        You may provide your own functions to enact a move from
+        one solution to a neighbor solution. The function must take
+        the solution as input along with a `seed` input to control
+        random number generation (see the `seed` input here).
+        Your function should maintain the solution as a cycle with
+        equal first and last node and all others appearing once.
+        Your function should return the new solution.
+
+    max_iterations : int, optional (default=10)
+        Declared done when this number of consecutive iterations of
+        the outer loop occurs without any change in the best cost solution.
+
+    N_inner : int, optional (default=100)
+        The number of iterations of the inner loop.
+
+    alpha : float between (0, 1), optional (default=0.1)
+        Percentage of threshold decrease when there is at
+        least one acceptance of a neighbor solution.
+        If no inner loop moves are accepted the threshold remains unchanged.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    cycle : list of nodes
+        Returns the cycle (list of nodes) that a salesman
+        can follow to minimize total weight of the trip.
+
+    Raises
+    ------
+    NetworkXError
+        If `G` is not complete the algorithm raises an exception.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import approximation as approx
+    >>> G = nx.DiGraph()
+    >>> G.add_weighted_edges_from(
+    ...     {
+    ...         ("A", "B", 3),
+    ...         ("A", "C", 17),
+    ...         ("A", "D", 14),
+    ...         ("B", "A", 3),
+    ...         ("B", "C", 12),
+    ...         ("B", "D", 16),
+    ...         ("C", "A", 13),
+    ...         ("C", "B", 12),
+    ...         ("C", "D", 4),
+    ...         ("D", "A", 14),
+    ...         ("D", "B", 15),
+    ...         ("D", "C", 2),
+    ...     }
+    ... )
+    >>> cycle = approx.threshold_accepting_tsp(G, "greedy", source="D")
+    >>> cost = sum(G[n][nbr]["weight"] for n, nbr in nx.utils.pairwise(cycle))
+    >>> cycle
+    ['D', 'C', 'B', 'A', 'D']
+    >>> cost
+    31
+    >>> incycle = ["D", "B", "A", "C", "D"]
+    >>> cycle = approx.threshold_accepting_tsp(G, incycle, source="D")
+    >>> cost = sum(G[n][nbr]["weight"] for n, nbr in nx.utils.pairwise(cycle))
+    >>> cycle
+    ['D', 'C', 'B', 'A', 'D']
+    >>> cost
+    31
+
+    Notes
+    -----
+    Threshold Accepting is a metaheuristic local search algorithm.
+    The main characteristic of this algorithm is that it accepts
+    even solutions which lead to the increase of the cost in order
+    to escape from low quality local optimal solutions.
+
+    This algorithm needs an initial solution. This solution can be
+    constructed by a simple greedy algorithm. At every iteration, it
+    selects thoughtfully a neighbor solution.
+    Consider $c(x)$ cost of current solution and $c(x')$ cost of
+    neighbor solution.
+    If $c(x') - c(x) <= threshold$ then the neighbor solution becomes the current
+    solution for the next iteration, where the threshold is named threshold.
+
+    In comparison to the Simulated Annealing algorithm, the Threshold
+    Accepting algorithm does not accept very low quality solutions
+    (due to the presence of the threshold value). In the case of
+    Simulated Annealing, even a very low quality solution can
+    be accepted with probability $p$.
+
+    Time complexity:
+    It has a running time $O(m * n * |V|)$ where $m$ and $n$ are the number
+    of times the outer and inner loop run respectively.
+
+    For more information and how algorithm is inspired see:
+    https://doi.org/10.1016/0021-9991(90)90201-B
+
+    See Also
+    --------
+    simulated_annealing_tsp
+
+    """
+    if move == "1-1":
+        move = swap_two_nodes
+    elif move == "1-0":
+        move = move_one_node
+    if init_cycle == "greedy":
+        # Construct an initial solution using a greedy algorithm.
+        cycle = greedy_tsp(G, weight=weight, source=source)
+        if G.number_of_nodes() == 2:
+            return cycle
+
+    else:
+        cycle = list(init_cycle)
+        if source is None:
+            source = cycle[0]
+        elif source != cycle[0]:
+            raise nx.NetworkXError("source must be first node in init_cycle")
+        if cycle[0] != cycle[-1]:
+            raise nx.NetworkXError("init_cycle must be a cycle. (return to start)")
+
+        if len(cycle) - 1 != len(G) or len(set(G.nbunch_iter(cycle))) != len(G):
+            raise nx.NetworkXError("init_cycle is not all and only nodes.")
+
+        # Check that G is a complete graph
+        N = len(G) - 1
+        # This check ignores selfloops which is what we want here.
+        if any(len(nbrdict) - (n in nbrdict) != N for n, nbrdict in G.adj.items()):
+            raise nx.NetworkXError("G must be a complete graph.")
+
+        if G.number_of_nodes() == 2:
+            neighbor = list(G.neighbors(source))[0]
+            return [source, neighbor, source]
+
+    # Find the cost of initial solution
+    cost = sum(G[u][v].get(weight, 1) for u, v in pairwise(cycle))
+
+    count = 0
+    best_cycle = cycle.copy()
+    best_cost = cost
+    while count <= max_iterations:
+        count += 1
+        accepted = False
+        for i in range(N_inner):
+            adj_sol = move(cycle, seed)
+            adj_cost = sum(G[u][v].get(weight, 1) for u, v in pairwise(adj_sol))
+            delta = adj_cost - cost
+            if delta <= threshold:
+                accepted = True
+
+                # Set current solution the adjacent solution.
+                cycle = adj_sol
+                cost = adj_cost
+
+                if cost < best_cost:
+                    count = 0
+                    best_cycle = cycle.copy()
+                    best_cost = cost
+        if accepted:
+            threshold -= threshold * alpha
+
+    return best_cycle
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/treewidth.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/treewidth.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef1b2f7351bdb1d7815b8218ac1f041acdd55512
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/treewidth.py
@@ -0,0 +1,255 @@
+"""Functions for computing treewidth decomposition.
+
+Treewidth of an undirected graph is a number associated with the graph.
+It can be defined as the size of the largest vertex set (bag) in a tree
+decomposition of the graph minus one.
+
+`Wikipedia: Treewidth <https://en.wikipedia.org/wiki/Treewidth>`_
+
+The notions of treewidth and tree decomposition have gained their
+attractiveness partly because many graph and network problems that are
+intractable (e.g., NP-hard) on arbitrary graphs become efficiently
+solvable (e.g., with a linear time algorithm) when the treewidth of the
+input graphs is bounded by a constant [1]_ [2]_.
+
+There are two different functions for computing a tree decomposition:
+:func:`treewidth_min_degree` and :func:`treewidth_min_fill_in`.
+
+.. [1] Hans L. Bodlaender and Arie M. C. A. Koster. 2010. "Treewidth
+      computations I.Upper bounds". Inf. Comput. 208, 3 (March 2010),259-275.
+      http://dx.doi.org/10.1016/j.ic.2009.03.008
+
+.. [2] Hans L. Bodlaender. "Discovering Treewidth". Institute of Information
+      and Computing Sciences, Utrecht University.
+      Technical Report UU-CS-2005-018.
+      http://www.cs.uu.nl
+
+.. [3] K. Wang, Z. Lu, and J. Hicks *Treewidth*.
+      https://web.archive.org/web/20210507025929/http://web.eecs.utk.edu/~cphill25/cs594_spring2015_projects/treewidth.pdf
+
+"""
+
+import itertools
+import sys
+from heapq import heapify, heappop, heappush
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["treewidth_min_degree", "treewidth_min_fill_in"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(returns_graph=True)
+def treewidth_min_degree(G):
+    """Returns a treewidth decomposition using the Minimum Degree heuristic.
+
+    The heuristic chooses the nodes according to their degree, i.e., first
+    the node with the lowest degree is chosen, then the graph is updated
+    and the corresponding node is removed. Next, a new node with the lowest
+    degree is chosen, and so on.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    Treewidth decomposition : (int, Graph) tuple
+          2-tuple with treewidth and the corresponding decomposed tree.
+    """
+    deg_heuristic = MinDegreeHeuristic(G)
+    return treewidth_decomp(G, lambda graph: deg_heuristic.best_node(graph))
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(returns_graph=True)
+def treewidth_min_fill_in(G):
+    """Returns a treewidth decomposition using the Minimum Fill-in heuristic.
+
+    The heuristic chooses a node from the graph, where the number of edges
+    added turning the neighborhood of the chosen node into clique is as
+    small as possible.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    Treewidth decomposition : (int, Graph) tuple
+        2-tuple with treewidth and the corresponding decomposed tree.
+    """
+    return treewidth_decomp(G, min_fill_in_heuristic)
+
+
+class MinDegreeHeuristic:
+    """Implements the Minimum Degree heuristic.
+
+    The heuristic chooses the nodes according to their degree
+    (number of neighbors), i.e., first the node with the lowest degree is
+    chosen, then the graph is updated and the corresponding node is
+    removed. Next, a new node with the lowest degree is chosen, and so on.
+    """
+
+    def __init__(self, graph):
+        self._graph = graph
+
+        # nodes that have to be updated in the heap before each iteration
+        self._update_nodes = []
+
+        self._degreeq = []  # a heapq with 3-tuples (degree,unique_id,node)
+        self.count = itertools.count()
+
+        # build heap with initial degrees
+        for n in graph:
+            self._degreeq.append((len(graph[n]), next(self.count), n))
+        heapify(self._degreeq)
+
+    def best_node(self, graph):
+        # update nodes in self._update_nodes
+        for n in self._update_nodes:
+            # insert changed degrees into degreeq
+            heappush(self._degreeq, (len(graph[n]), next(self.count), n))
+
+        # get the next valid (minimum degree) node
+        while self._degreeq:
+            (min_degree, _, elim_node) = heappop(self._degreeq)
+            if elim_node not in graph or len(graph[elim_node]) != min_degree:
+                # outdated entry in degreeq
+                continue
+            elif min_degree == len(graph) - 1:
+                # fully connected: abort condition
+                return None
+
+            # remember to update nodes in the heap before getting the next node
+            self._update_nodes = graph[elim_node]
+            return elim_node
+
+        # the heap is empty: abort
+        return None
+
+
+def min_fill_in_heuristic(graph_dict):
+    """Implements the Minimum Degree heuristic.
+
+    graph_dict: dict keyed by node to sets of neighbors (no self-loops)
+
+    Returns the node from the graph, where the number of edges added when
+    turning the neighborhood of the chosen node into clique is as small as
+    possible. This algorithm chooses the nodes using the Minimum Fill-In
+    heuristic. The running time of the algorithm is :math:`O(V^3)` and it uses
+    additional constant memory.
+    """
+
+    if len(graph_dict) == 0:
+        return None
+
+    min_fill_in_node = None
+
+    min_fill_in = sys.maxsize
+
+    # sort nodes by degree
+    nodes_by_degree = sorted(graph_dict, key=lambda x: len(graph_dict[x]))
+    min_degree = len(graph_dict[nodes_by_degree[0]])
+
+    # abort condition (handle complete graph)
+    if min_degree == len(graph_dict) - 1:
+        return None
+
+    for node in nodes_by_degree:
+        num_fill_in = 0
+        nbrs = graph_dict[node]
+        for nbr in nbrs:
+            # count how many nodes in nbrs current nbr is not connected to
+            # subtract 1 for the node itself
+            num_fill_in += len(nbrs - graph_dict[nbr]) - 1
+            if num_fill_in >= 2 * min_fill_in:
+                break
+
+        num_fill_in /= 2  # divide by 2 because of double counting
+
+        if num_fill_in < min_fill_in:  # update min-fill-in node
+            if num_fill_in == 0:
+                return node
+            min_fill_in = num_fill_in
+            min_fill_in_node = node
+
+    return min_fill_in_node
+
+
+@nx._dispatchable(returns_graph=True)
+def treewidth_decomp(G, heuristic=min_fill_in_heuristic):
+    """Returns a treewidth decomposition using the passed heuristic.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+    heuristic : heuristic function
+
+    Returns
+    -------
+    Treewidth decomposition : (int, Graph) tuple
+        2-tuple with treewidth and the corresponding decomposed tree.
+    """
+
+    # make dict-of-sets structure
+    graph_dict = {n: set(G[n]) - {n} for n in G}
+
+    # stack containing nodes and neighbors in the order from the heuristic
+    node_stack = []
+
+    # get first node from heuristic
+    elim_node = heuristic(graph_dict)
+    while elim_node is not None:
+        # connect all neighbors with each other
+        nbrs = graph_dict[elim_node]
+        for u, v in itertools.permutations(nbrs, 2):
+            if v not in graph_dict[u]:
+                graph_dict[u].add(v)
+
+        # push node and its current neighbors on stack
+        node_stack.append((elim_node, nbrs))
+
+        # remove node from graph_dict
+        for u in graph_dict[elim_node]:
+            graph_dict[u].remove(elim_node)
+
+        del graph_dict[elim_node]
+        elim_node = heuristic(graph_dict)
+
+    # the abort condition is met; put all remaining nodes into one bag
+    decomp = nx.Graph()
+    first_bag = frozenset(graph_dict.keys())
+    decomp.add_node(first_bag)
+
+    treewidth = len(first_bag) - 1
+
+    while node_stack:
+        # get node and its neighbors from the stack
+        (curr_node, nbrs) = node_stack.pop()
+
+        # find a bag all neighbors are in
+        old_bag = None
+        for bag in decomp.nodes:
+            if nbrs <= bag:
+                old_bag = bag
+                break
+
+        if old_bag is None:
+            # no old_bag was found: just connect to the first_bag
+            old_bag = first_bag
+
+        # create new node for decomposition
+        nbrs.add(curr_node)
+        new_bag = frozenset(nbrs)
+
+        # update treewidth
+        treewidth = max(treewidth, len(new_bag) - 1)
+
+        # add edge to decomposition (implicitly also adds the new node)
+        decomp.add_edge(old_bag, new_bag)
+
+    return treewidth, decomp
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/vertex_cover.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/vertex_cover.py
new file mode 100644
index 0000000000000000000000000000000000000000..13d7167cfc1e4494cbbb2ee8c774e9ffbc3ee495
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/vertex_cover.py
@@ -0,0 +1,83 @@
+"""Functions for computing an approximate minimum weight vertex cover.
+
+A |vertex cover|_ is a subset of nodes such that each edge in the graph
+is incident to at least one node in the subset.
+
+.. _vertex cover: https://en.wikipedia.org/wiki/Vertex_cover
+.. |vertex cover| replace:: *vertex cover*
+
+"""
+
+import networkx as nx
+
+__all__ = ["min_weighted_vertex_cover"]
+
+
+@nx._dispatchable(node_attrs="weight")
+def min_weighted_vertex_cover(G, weight=None):
+    r"""Returns an approximate minimum weighted vertex cover.
+
+    The set of nodes returned by this function is guaranteed to be a
+    vertex cover, and the total weight of the set is guaranteed to be at
+    most twice the total weight of the minimum weight vertex cover. In
+    other words,
+
+    .. math::
+
+       w(S) \leq 2 * w(S^*),
+
+    where $S$ is the vertex cover returned by this function,
+    $S^*$ is the vertex cover of minimum weight out of all vertex
+    covers of the graph, and $w$ is the function that computes the
+    sum of the weights of each node in that given set.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight : string, optional (default = None)
+        If None, every node has weight 1. If a string, use this node
+        attribute as the node weight. A node without this attribute is
+        assumed to have weight 1.
+
+    Returns
+    -------
+    min_weighted_cover : set
+        Returns a set of nodes whose weight sum is no more than twice
+        the weight sum of the minimum weight vertex cover.
+
+    Notes
+    -----
+    For a directed graph, a vertex cover has the same definition: a set
+    of nodes such that each edge in the graph is incident to at least
+    one node in the set. Whether the node is the head or tail of the
+    directed edge is ignored.
+
+    This is the local-ratio algorithm for computing an approximate
+    vertex cover. The algorithm greedily reduces the costs over edges,
+    iteratively building a cover. The worst-case runtime of this
+    implementation is $O(m \log n)$, where $n$ is the number
+    of nodes and $m$ the number of edges in the graph.
+
+    References
+    ----------
+    .. [1] Bar-Yehuda, R., and Even, S. (1985). "A local-ratio theorem for
+       approximating the weighted vertex cover problem."
+       *Annals of Discrete Mathematics*, 25, 27–46
+       <http://www.cs.technion.ac.il/~reuven/PDF/vc_lr.pdf>
+
+    """
+    cost = dict(G.nodes(data=weight, default=1))
+    # While there are uncovered edges, choose an uncovered and update
+    # the cost of the remaining edges.
+    cover = set()
+    for u, v in G.edges():
+        if u in cover or v in cover:
+            continue
+        if cost[u] <= cost[v]:
+            cover.add(u)
+            cost[v] -= cost[u]
+        else:
+            cover.add(v)
+            cost[u] -= cost[v]
+    return cover
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/__init__.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d9888609cbc43d4ba2121fcd0feda0985d1aebd
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/__init__.py
@@ -0,0 +1,5 @@
+from networkx.algorithms.assortativity.connectivity import *
+from networkx.algorithms.assortativity.correlation import *
+from networkx.algorithms.assortativity.mixing import *
+from networkx.algorithms.assortativity.neighbor_degree import *
+from networkx.algorithms.assortativity.pairs import *
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/connectivity.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/connectivity.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3fde0da68a1990da29ced6996620d709c52c13d
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/connectivity.py
@@ -0,0 +1,122 @@
+from collections import defaultdict
+
+import networkx as nx
+
+__all__ = ["average_degree_connectivity"]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def average_degree_connectivity(
+    G, source="in+out", target="in+out", nodes=None, weight=None
+):
+    r"""Compute the average degree connectivity of graph.
+
+    The average degree connectivity is the average nearest neighbor degree of
+    nodes with degree k. For weighted graphs, an analogous measure can
+    be computed using the weighted average neighbors degree defined in
+    [1]_, for a node `i`, as
+
+    .. math::
+
+        k_{nn,i}^{w} = \frac{1}{s_i} \sum_{j \in N(i)} w_{ij} k_j
+
+    where `s_i` is the weighted degree of node `i`,
+    `w_{ij}` is the weight of the edge that links `i` and `j`,
+    and `N(i)` are the neighbors of node `i`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source :  "in"|"out"|"in+out" (default:"in+out")
+       Directed graphs only. Use "in"- or "out"-degree for source node.
+
+    target : "in"|"out"|"in+out" (default:"in+out"
+       Directed graphs only. Use "in"- or "out"-degree for target node.
+
+    nodes : list or iterable (optional)
+        Compute neighbor connectivity for these nodes. The default is all
+        nodes.
+
+    weight : string or None, optional (default=None)
+       The edge attribute that holds the numerical value used as a weight.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    d : dict
+       A dictionary keyed by degree k with the value of average connectivity.
+
+    Raises
+    ------
+    NetworkXError
+        If either `source` or `target` are not one of 'in',
+        'out', or 'in+out'.
+        If either `source` or `target` is passed for an undirected graph.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> G.edges[1, 2]["weight"] = 3
+    >>> nx.average_degree_connectivity(G)
+    {1: 2.0, 2: 1.5}
+    >>> nx.average_degree_connectivity(G, weight="weight")
+    {1: 2.0, 2: 1.75}
+
+    See Also
+    --------
+    average_neighbor_degree
+
+    References
+    ----------
+    .. [1] A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani,
+       "The architecture of complex weighted networks".
+       PNAS 101 (11): 3747–3752 (2004).
+    """
+    # First, determine the type of neighbors and the type of degree to use.
+    if G.is_directed():
+        if source not in ("in", "out", "in+out"):
+            raise nx.NetworkXError('source must be one of "in", "out", or "in+out"')
+        if target not in ("in", "out", "in+out"):
+            raise nx.NetworkXError('target must be one of "in", "out", or "in+out"')
+        direction = {"out": G.out_degree, "in": G.in_degree, "in+out": G.degree}
+        neighbor_funcs = {
+            "out": G.successors,
+            "in": G.predecessors,
+            "in+out": G.neighbors,
+        }
+        source_degree = direction[source]
+        target_degree = direction[target]
+        neighbors = neighbor_funcs[source]
+        # `reverse` indicates whether to look at the in-edge when
+        # computing the weight of an edge.
+        reverse = source == "in"
+    else:
+        if source != "in+out" or target != "in+out":
+            raise nx.NetworkXError(
+                f"source and target arguments are only supported for directed graphs"
+            )
+        source_degree = G.degree
+        target_degree = G.degree
+        neighbors = G.neighbors
+        reverse = False
+    dsum = defaultdict(int)
+    dnorm = defaultdict(int)
+    # Check if `source_nodes` is actually a single node in the graph.
+    source_nodes = source_degree(nodes)
+    if nodes in G:
+        source_nodes = [(nodes, source_degree(nodes))]
+    for n, k in source_nodes:
+        nbrdeg = target_degree(neighbors(n))
+        if weight is None:
+            s = sum(d for n, d in nbrdeg)
+        else:  # weight nbr degree by weight of (n,nbr) edge
+            if reverse:
+                s = sum(G[nbr][n].get(weight, 1) * d for nbr, d in nbrdeg)
+            else:
+                s = sum(G[n][nbr].get(weight, 1) * d for nbr, d in nbrdeg)
+        dnorm[k] += source_degree(n, weight=weight)
+        dsum[k] += s
+
+    # normalize
+    return {k: avg if dnorm[k] == 0 else avg / dnorm[k] for k, avg in dsum.items()}
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/correlation.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/correlation.py
new file mode 100644
index 0000000000000000000000000000000000000000..52ae7a12fa9de5705412538fc6bbe873755d9b7a
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/correlation.py
@@ -0,0 +1,302 @@
+"""Node assortativity coefficients and correlation measures."""
+
+import networkx as nx
+from networkx.algorithms.assortativity.mixing import (
+    attribute_mixing_matrix,
+    degree_mixing_matrix,
+)
+from networkx.algorithms.assortativity.pairs import node_degree_xy
+
+__all__ = [
+    "degree_pearson_correlation_coefficient",
+    "degree_assortativity_coefficient",
+    "attribute_assortativity_coefficient",
+    "numeric_assortativity_coefficient",
+]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def degree_assortativity_coefficient(G, x="out", y="in", weight=None, nodes=None):
+    """Compute degree assortativity of graph.
+
+    Assortativity measures the similarity of connections
+    in the graph with respect to the node degree.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    x: string ('in','out')
+       The degree type for source node (directed graphs only).
+
+    y: string ('in','out')
+       The degree type for target node (directed graphs only).
+
+    weight: string or None, optional (default=None)
+       The edge attribute that holds the numerical value used
+       as a weight.  If None, then each edge has weight 1.
+       The degree is the sum of the edge weights adjacent to the node.
+
+    nodes: list or iterable (optional)
+        Compute degree assortativity only for nodes in container.
+        The default is all nodes.
+
+    Returns
+    -------
+    r : float
+       Assortativity of graph by degree.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> r = nx.degree_assortativity_coefficient(G)
+    >>> print(f"{r:3.1f}")
+    -0.5
+
+    See Also
+    --------
+    attribute_assortativity_coefficient
+    numeric_assortativity_coefficient
+    degree_mixing_dict
+    degree_mixing_matrix
+
+    Notes
+    -----
+    This computes Eq. (21) in Ref. [1]_ , where e is the joint
+    probability distribution (mixing matrix) of the degrees.  If G is
+    directed than the matrix e is the joint probability of the
+    user-specified degree type for the source and target.
+
+    References
+    ----------
+    .. [1] M. E. J. Newman, Mixing patterns in networks,
+       Physical Review E, 67 026126, 2003
+    .. [2] Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M.
+       Edge direction and the structure of networks, PNAS 107, 10815-20 (2010).
+    """
+    if nodes is None:
+        nodes = G.nodes
+
+    degrees = None
+
+    if G.is_directed():
+        indeg = (
+            {d for _, d in G.in_degree(nodes, weight=weight)}
+            if "in" in (x, y)
+            else set()
+        )
+        outdeg = (
+            {d for _, d in G.out_degree(nodes, weight=weight)}
+            if "out" in (x, y)
+            else set()
+        )
+        degrees = set.union(indeg, outdeg)
+    else:
+        degrees = {d for _, d in G.degree(nodes, weight=weight)}
+
+    mapping = {d: i for i, d in enumerate(degrees)}
+    M = degree_mixing_matrix(G, x=x, y=y, nodes=nodes, weight=weight, mapping=mapping)
+
+    return _numeric_ac(M, mapping=mapping)
+
+
+@nx._dispatchable(edge_attrs="weight")
+def degree_pearson_correlation_coefficient(G, x="out", y="in", weight=None, nodes=None):
+    """Compute degree assortativity of graph.
+
+    Assortativity measures the similarity of connections
+    in the graph with respect to the node degree.
+
+    This is the same as degree_assortativity_coefficient but uses the
+    potentially faster scipy.stats.pearsonr function.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    x: string ('in','out')
+       The degree type for source node (directed graphs only).
+
+    y: string ('in','out')
+       The degree type for target node (directed graphs only).
+
+    weight: string or None, optional (default=None)
+       The edge attribute that holds the numerical value used
+       as a weight.  If None, then each edge has weight 1.
+       The degree is the sum of the edge weights adjacent to the node.
+
+    nodes: list or iterable (optional)
+        Compute pearson correlation of degrees only for specified nodes.
+        The default is all nodes.
+
+    Returns
+    -------
+    r : float
+       Assortativity of graph by degree.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> r = nx.degree_pearson_correlation_coefficient(G)
+    >>> print(f"{r:3.1f}")
+    -0.5
+
+    Notes
+    -----
+    This calls scipy.stats.pearsonr.
+
+    References
+    ----------
+    .. [1] M. E. J. Newman, Mixing patterns in networks
+           Physical Review E, 67 026126, 2003
+    .. [2] Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M.
+       Edge direction and the structure of networks, PNAS 107, 10815-20 (2010).
+    """
+    import scipy as sp
+
+    xy = node_degree_xy(G, x=x, y=y, nodes=nodes, weight=weight)
+    x, y = zip(*xy)
+    return float(sp.stats.pearsonr(x, y)[0])
+
+
+@nx._dispatchable(node_attrs="attribute")
+def attribute_assortativity_coefficient(G, attribute, nodes=None):
+    """Compute assortativity for node attributes.
+
+    Assortativity measures the similarity of connections
+    in the graph with respect to the given attribute.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    attribute : string
+        Node attribute key
+
+    nodes: list or iterable (optional)
+        Compute attribute assortativity for nodes in container.
+        The default is all nodes.
+
+    Returns
+    -------
+    r: float
+       Assortativity of graph for given attribute
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from([0, 1], color="red")
+    >>> G.add_nodes_from([2, 3], color="blue")
+    >>> G.add_edges_from([(0, 1), (2, 3)])
+    >>> print(nx.attribute_assortativity_coefficient(G, "color"))
+    1.0
+
+    Notes
+    -----
+    This computes Eq. (2) in Ref. [1]_ , (trace(M)-sum(M^2))/(1-sum(M^2)),
+    where M is the joint probability distribution (mixing matrix)
+    of the specified attribute.
+
+    References
+    ----------
+    .. [1] M. E. J. Newman, Mixing patterns in networks,
+       Physical Review E, 67 026126, 2003
+    """
+    M = attribute_mixing_matrix(G, attribute, nodes)
+    return attribute_ac(M)
+
+
+@nx._dispatchable(node_attrs="attribute")
+def numeric_assortativity_coefficient(G, attribute, nodes=None):
+    """Compute assortativity for numerical node attributes.
+
+    Assortativity measures the similarity of connections
+    in the graph with respect to the given numeric attribute.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    attribute : string
+        Node attribute key.
+
+    nodes: list or iterable (optional)
+        Compute numeric assortativity only for attributes of nodes in
+        container. The default is all nodes.
+
+    Returns
+    -------
+    r: float
+       Assortativity of graph for given attribute
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from([0, 1], size=2)
+    >>> G.add_nodes_from([2, 3], size=3)
+    >>> G.add_edges_from([(0, 1), (2, 3)])
+    >>> print(nx.numeric_assortativity_coefficient(G, "size"))
+    1.0
+
+    Notes
+    -----
+    This computes Eq. (21) in Ref. [1]_ , which is the Pearson correlation
+    coefficient of the specified (scalar valued) attribute across edges.
+
+    References
+    ----------
+    .. [1] M. E. J. Newman, Mixing patterns in networks
+           Physical Review E, 67 026126, 2003
+    """
+    if nodes is None:
+        nodes = G.nodes
+    vals = {G.nodes[n][attribute] for n in nodes}
+    mapping = {d: i for i, d in enumerate(vals)}
+    M = attribute_mixing_matrix(G, attribute, nodes, mapping)
+    return _numeric_ac(M, mapping)
+
+
+def attribute_ac(M):
+    """Compute assortativity for attribute matrix M.
+
+    Parameters
+    ----------
+    M : numpy.ndarray
+        2D ndarray representing the attribute mixing matrix.
+
+    Notes
+    -----
+    This computes Eq. (2) in Ref. [1]_ , (trace(e)-sum(e^2))/(1-sum(e^2)),
+    where e is the joint probability distribution (mixing matrix)
+    of the specified attribute.
+
+    References
+    ----------
+    .. [1] M. E. J. Newman, Mixing patterns in networks,
+       Physical Review E, 67 026126, 2003
+    """
+    if M.sum() != 1.0:
+        M = M / M.sum()
+    s = (M @ M).sum()
+    t = M.trace()
+    r = (t - s) / (1 - s)
+    return float(r)
+
+
+def _numeric_ac(M, mapping):
+    # M is a 2D numpy array
+    # numeric assortativity coefficient, pearsonr
+    import numpy as np
+
+    if M.sum() != 1.0:
+        M = M / M.sum()
+    x = np.array(list(mapping.keys()))
+    y = x  # x and y have the same support
+    idx = list(mapping.values())
+    a = M.sum(axis=0)
+    b = M.sum(axis=1)
+    vara = (a[idx] * x**2).sum() - ((a[idx] * x).sum()) ** 2
+    varb = (b[idx] * y**2).sum() - ((b[idx] * y).sum()) ** 2
+    xy = np.outer(x, y)
+    ab = np.outer(a[idx], b[idx])
+    return float((xy * (M - ab)).sum() / np.sqrt(vara * varb))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/mixing.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/mixing.py
new file mode 100644
index 0000000000000000000000000000000000000000..1762d4e56c96624ecb4cccf1f2247f46159a12e4
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/mixing.py
@@ -0,0 +1,255 @@
+"""
+Mixing matrices for node attributes and degree.
+"""
+
+import networkx as nx
+from networkx.algorithms.assortativity.pairs import node_attribute_xy, node_degree_xy
+from networkx.utils import dict_to_numpy_array
+
+__all__ = [
+    "attribute_mixing_matrix",
+    "attribute_mixing_dict",
+    "degree_mixing_matrix",
+    "degree_mixing_dict",
+    "mixing_dict",
+]
+
+
+@nx._dispatchable(node_attrs="attribute")
+def attribute_mixing_dict(G, attribute, nodes=None, normalized=False):
+    """Returns dictionary representation of mixing matrix for attribute.
+
+    Parameters
+    ----------
+    G : graph
+       NetworkX graph object.
+
+    attribute : string
+       Node attribute key.
+
+    nodes: list or iterable (optional)
+        Unse nodes in container to build the dict. The default is all nodes.
+
+    normalized : bool (default=False)
+       Return counts if False or probabilities if True.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from([0, 1], color="red")
+    >>> G.add_nodes_from([2, 3], color="blue")
+    >>> G.add_edge(1, 3)
+    >>> d = nx.attribute_mixing_dict(G, "color")
+    >>> print(d["red"]["blue"])
+    1
+    >>> print(d["blue"]["red"])  # d symmetric for undirected graphs
+    1
+
+    Returns
+    -------
+    d : dictionary
+       Counts or joint probability of occurrence of attribute pairs.
+    """
+    xy_iter = node_attribute_xy(G, attribute, nodes)
+    return mixing_dict(xy_iter, normalized=normalized)
+
+
+@nx._dispatchable(node_attrs="attribute")
+def attribute_mixing_matrix(G, attribute, nodes=None, mapping=None, normalized=True):
+    """Returns mixing matrix for attribute.
+
+    Parameters
+    ----------
+    G : graph
+       NetworkX graph object.
+
+    attribute : string
+       Node attribute key.
+
+    nodes: list or iterable (optional)
+        Use only nodes in container to build the matrix. The default is
+        all nodes.
+
+    mapping : dictionary, optional
+       Mapping from node attribute to integer index in matrix.
+       If not specified, an arbitrary ordering will be used.
+
+    normalized : bool (default=True)
+       Return counts if False or probabilities if True.
+
+    Returns
+    -------
+    m: numpy array
+       Counts or joint probability of occurrence of attribute pairs.
+
+    Notes
+    -----
+    If each node has a unique attribute value, the unnormalized mixing matrix
+    will be equal to the adjacency matrix. To get a denser mixing matrix,
+    the rounding can be performed to form groups of nodes with equal values.
+    For example, the exact height of persons in cm (180.79155222, 163.9080892,
+    163.30095355, 167.99016217, 168.21590163, ...) can be rounded to (180, 163,
+    163, 168, 168, ...).
+
+    Definitions of attribute mixing matrix vary on whether the matrix
+    should include rows for attribute values that don't arise. Here we
+    do not include such empty-rows. But you can force them to appear
+    by inputting a `mapping` that includes those values.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> gender = {0: "male", 1: "female", 2: "female"}
+    >>> nx.set_node_attributes(G, gender, "gender")
+    >>> mapping = {"male": 0, "female": 1}
+    >>> mix_mat = nx.attribute_mixing_matrix(G, "gender", mapping=mapping)
+    >>> mix_mat
+    array([[0.  , 0.25],
+           [0.25, 0.5 ]])
+    """
+    d = attribute_mixing_dict(G, attribute, nodes)
+    a = dict_to_numpy_array(d, mapping=mapping)
+    if normalized:
+        a = a / a.sum()
+    return a
+
+
+@nx._dispatchable(edge_attrs="weight")
+def degree_mixing_dict(G, x="out", y="in", weight=None, nodes=None, normalized=False):
+    """Returns dictionary representation of mixing matrix for degree.
+
+    Parameters
+    ----------
+    G : graph
+        NetworkX graph object.
+
+    x: string ('in','out')
+       The degree type for source node (directed graphs only).
+
+    y: string ('in','out')
+       The degree type for target node (directed graphs only).
+
+    weight: string or None, optional (default=None)
+       The edge attribute that holds the numerical value used
+       as a weight.  If None, then each edge has weight 1.
+       The degree is the sum of the edge weights adjacent to the node.
+
+    normalized : bool (default=False)
+        Return counts if False or probabilities if True.
+
+    Returns
+    -------
+    d: dictionary
+       Counts or joint probability of occurrence of degree pairs.
+    """
+    xy_iter = node_degree_xy(G, x=x, y=y, nodes=nodes, weight=weight)
+    return mixing_dict(xy_iter, normalized=normalized)
+
+
+@nx._dispatchable(edge_attrs="weight")
+def degree_mixing_matrix(
+    G, x="out", y="in", weight=None, nodes=None, normalized=True, mapping=None
+):
+    """Returns mixing matrix for attribute.
+
+    Parameters
+    ----------
+    G : graph
+       NetworkX graph object.
+
+    x: string ('in','out')
+       The degree type for source node (directed graphs only).
+
+    y: string ('in','out')
+       The degree type for target node (directed graphs only).
+
+    nodes: list or iterable (optional)
+        Build the matrix using only nodes in container.
+        The default is all nodes.
+
+    weight: string or None, optional (default=None)
+       The edge attribute that holds the numerical value used
+       as a weight.  If None, then each edge has weight 1.
+       The degree is the sum of the edge weights adjacent to the node.
+
+    normalized : bool (default=True)
+       Return counts if False or probabilities if True.
+
+    mapping : dictionary, optional
+       Mapping from node degree to integer index in matrix.
+       If not specified, an arbitrary ordering will be used.
+
+    Returns
+    -------
+    m: numpy array
+       Counts, or joint probability, of occurrence of node degree.
+
+    Notes
+    -----
+    Definitions of degree mixing matrix vary on whether the matrix
+    should include rows for degree values that don't arise. Here we
+    do not include such empty-rows. But you can force them to appear
+    by inputting a `mapping` that includes those values. See examples.
+
+    Examples
+    --------
+    >>> G = nx.star_graph(3)
+    >>> mix_mat = nx.degree_mixing_matrix(G)
+    >>> mix_mat
+    array([[0. , 0.5],
+           [0.5, 0. ]])
+
+    If you want every possible degree to appear as a row, even if no nodes
+    have that degree, use `mapping` as follows,
+
+    >>> max_degree = max(deg for n, deg in G.degree)
+    >>> mapping = {x: x for x in range(max_degree + 1)}  # identity mapping
+    >>> mix_mat = nx.degree_mixing_matrix(G, mapping=mapping)
+    >>> mix_mat
+    array([[0. , 0. , 0. , 0. ],
+           [0. , 0. , 0. , 0.5],
+           [0. , 0. , 0. , 0. ],
+           [0. , 0.5, 0. , 0. ]])
+    """
+    d = degree_mixing_dict(G, x=x, y=y, nodes=nodes, weight=weight)
+    a = dict_to_numpy_array(d, mapping=mapping)
+    if normalized:
+        a = a / a.sum()
+    return a
+
+
+def mixing_dict(xy, normalized=False):
+    """Returns a dictionary representation of mixing matrix.
+
+    Parameters
+    ----------
+    xy : list or container of two-tuples
+       Pairs of (x,y) items.
+
+    attribute : string
+       Node attribute key
+
+    normalized : bool (default=False)
+       Return counts if False or probabilities if True.
+
+    Returns
+    -------
+    d: dictionary
+       Counts or Joint probability of occurrence of values in xy.
+    """
+    d = {}
+    psum = 0.0
+    for x, y in xy:
+        if x not in d:
+            d[x] = {}
+        if y not in d:
+            d[y] = {}
+        v = d[x].get(y, 0)
+        d[x][y] = v + 1
+        psum += 1
+
+    if normalized:
+        for _, jdict in d.items():
+            for j in jdict:
+                jdict[j] /= psum
+    return d
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/neighbor_degree.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/neighbor_degree.py
new file mode 100644
index 0000000000000000000000000000000000000000..6488d041a8bdc93ef3591283781b81bcf7f47dab
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/neighbor_degree.py
@@ -0,0 +1,160 @@
+import networkx as nx
+
+__all__ = ["average_neighbor_degree"]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def average_neighbor_degree(G, source="out", target="out", nodes=None, weight=None):
+    r"""Returns the average degree of the neighborhood of each node.
+
+    In an undirected graph, the neighborhood `N(i)` of node `i` contains the
+    nodes that are connected to `i` by an edge.
+
+    For directed graphs, `N(i)` is defined according to the parameter `source`:
+
+        - if source is 'in', then `N(i)` consists of predecessors of node `i`.
+        - if source is 'out', then `N(i)` consists of successors of node `i`.
+        - if source is 'in+out', then `N(i)` is both predecessors and successors.
+
+    The average neighborhood degree of a node `i` is
+
+    .. math::
+
+        k_{nn,i} = \frac{1}{|N(i)|} \sum_{j \in N(i)} k_j
+
+    where `N(i)` are the neighbors of node `i` and `k_j` is
+    the degree of node `j` which belongs to `N(i)`. For weighted
+    graphs, an analogous measure can be defined [1]_,
+
+    .. math::
+
+        k_{nn,i}^{w} = \frac{1}{s_i} \sum_{j \in N(i)} w_{ij} k_j
+
+    where `s_i` is the weighted degree of node `i`, `w_{ij}`
+    is the weight of the edge that links `i` and `j` and
+    `N(i)` are the neighbors of node `i`.
+
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : string ("in"|"out"|"in+out"), optional (default="out")
+       Directed graphs only.
+       Use "in"- or "out"-neighbors of source node.
+
+    target : string ("in"|"out"|"in+out"), optional (default="out")
+       Directed graphs only.
+       Use "in"- or "out"-degree for target node.
+
+    nodes : list or iterable, optional (default=G.nodes)
+        Compute neighbor degree only for specified nodes.
+
+    weight : string or None, optional (default=None)
+       The edge attribute that holds the numerical value used as a weight.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    d: dict
+       A dictionary keyed by node to the average degree of its neighbors.
+
+    Raises
+    ------
+    NetworkXError
+        If either `source` or `target` are not one of 'in', 'out', or 'in+out'.
+        If either `source` or `target` is passed for an undirected graph.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> G.edges[0, 1]["weight"] = 5
+    >>> G.edges[2, 3]["weight"] = 3
+
+    >>> nx.average_neighbor_degree(G)
+    {0: 2.0, 1: 1.5, 2: 1.5, 3: 2.0}
+    >>> nx.average_neighbor_degree(G, weight="weight")
+    {0: 2.0, 1: 1.1666666666666667, 2: 1.25, 3: 2.0}
+
+    >>> G = nx.DiGraph()
+    >>> nx.add_path(G, [0, 1, 2, 3])
+    >>> nx.average_neighbor_degree(G, source="in", target="in")
+    {0: 0.0, 1: 0.0, 2: 1.0, 3: 1.0}
+
+    >>> nx.average_neighbor_degree(G, source="out", target="out")
+    {0: 1.0, 1: 1.0, 2: 0.0, 3: 0.0}
+
+    See Also
+    --------
+    average_degree_connectivity
+
+    References
+    ----------
+    .. [1] A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani,
+       "The architecture of complex weighted networks".
+       PNAS 101 (11): 3747–3752 (2004).
+    """
+    if G.is_directed():
+        if source == "in":
+            source_degree = G.in_degree
+        elif source == "out":
+            source_degree = G.out_degree
+        elif source == "in+out":
+            source_degree = G.degree
+        else:
+            raise nx.NetworkXError(
+                f"source argument {source} must be 'in', 'out' or 'in+out'"
+            )
+
+        if target == "in":
+            target_degree = G.in_degree
+        elif target == "out":
+            target_degree = G.out_degree
+        elif target == "in+out":
+            target_degree = G.degree
+        else:
+            raise nx.NetworkXError(
+                f"target argument {target} must be 'in', 'out' or 'in+out'"
+            )
+    else:
+        if source != "out" or target != "out":
+            raise nx.NetworkXError(
+                f"source and target arguments are only supported for directed graphs"
+            )
+        source_degree = target_degree = G.degree
+
+    # precompute target degrees -- should *not* be weighted degree
+    t_deg = dict(target_degree())
+
+    # Set up both predecessor and successor neighbor dicts leaving empty if not needed
+    G_P = G_S = {n: {} for n in G}
+    if G.is_directed():
+        # "in" or "in+out" cases: G_P contains predecessors
+        if "in" in source:
+            G_P = G.pred
+        # "out" or "in+out" cases: G_S contains successors
+        if "out" in source:
+            G_S = G.succ
+    else:
+        # undirected leave G_P empty but G_S is the adjacency
+        G_S = G.adj
+
+    # Main loop: Compute average degree of neighbors
+    avg = {}
+    for n, deg in source_degree(nodes, weight=weight):
+        # handle degree zero average
+        if deg == 0:
+            avg[n] = 0.0
+            continue
+
+        # we sum over both G_P and G_S, but one of the two is usually empty.
+        if weight is None:
+            avg[n] = (
+                sum(t_deg[nbr] for nbr in G_S[n]) + sum(t_deg[nbr] for nbr in G_P[n])
+            ) / deg
+        else:
+            avg[n] = (
+                sum(dd.get(weight, 1) * t_deg[nbr] for nbr, dd in G_S[n].items())
+                + sum(dd.get(weight, 1) * t_deg[nbr] for nbr, dd in G_P[n].items())
+            ) / deg
+    return avg
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/pairs.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/pairs.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea5fd287545c80dd2ebbb2b253d5ab0ab7480743
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/assortativity/pairs.py
@@ -0,0 +1,127 @@
+"""Generators of x-y pairs of node data."""
+
+import networkx as nx
+
+__all__ = ["node_attribute_xy", "node_degree_xy"]
+
+
+@nx._dispatchable(node_attrs="attribute")
+def node_attribute_xy(G, attribute, nodes=None):
+    """Yields 2-tuples of node attribute values for all edges in `G`.
+
+    This generator yields, for each edge in `G` incident to a node in `nodes`,
+    a 2-tuple of form ``(attribute value,  attribute value)`` for the parameter
+    specified node-attribute.
+
+    Parameters
+    ----------
+    G: NetworkX graph
+
+    attribute: key
+        The node attribute key.
+
+    nodes: list or iterable (optional)
+        Use only edges that are incident to specified nodes.
+        The default is all nodes.
+
+    Yields
+    ------
+    (x, y): 2-tuple
+        Generates 2-tuple of (attribute, attribute) values.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph()
+    >>> G.add_node(1, color="red")
+    >>> G.add_node(2, color="blue")
+    >>> G.add_node(3, color="green")
+    >>> G.add_edge(1, 2)
+    >>> list(nx.node_attribute_xy(G, "color"))
+    [('red', 'blue')]
+
+    Notes
+    -----
+    For undirected graphs, each edge is produced twice, once for each edge
+    representation (u, v) and (v, u), with the exception of self-loop edges
+    which only appear once.
+    """
+    if nodes is None:
+        nodes = set(G)
+    else:
+        nodes = set(nodes)
+    Gnodes = G.nodes
+    for u, nbrsdict in G.adjacency():
+        if u not in nodes:
+            continue
+        uattr = Gnodes[u].get(attribute, None)
+        if G.is_multigraph():
+            for v, keys in nbrsdict.items():
+                vattr = Gnodes[v].get(attribute, None)
+                for _ in keys:
+                    yield (uattr, vattr)
+        else:
+            for v in nbrsdict:
+                vattr = Gnodes[v].get(attribute, None)
+                yield (uattr, vattr)
+
+
+@nx._dispatchable(edge_attrs="weight")
+def node_degree_xy(G, x="out", y="in", weight=None, nodes=None):
+    """Yields 2-tuples of ``(degree, degree)`` values for edges in `G`.
+
+    This generator yields, for each edge in `G` incident to a node in `nodes`,
+    a 2-tuple of form ``(degree, degree)``. The node degrees are weighted
+    when a `weight` attribute is specified.
+
+    Parameters
+    ----------
+    G: NetworkX graph
+
+    x: string ('in','out')
+       The degree type for source node (directed graphs only).
+
+    y: string ('in','out')
+       The degree type for target node (directed graphs only).
+
+    weight: string or None, optional (default=None)
+       The edge attribute that holds the numerical value used
+       as a weight.  If None, then each edge has weight 1.
+       The degree is the sum of the edge weights adjacent to the node.
+
+    nodes: list or iterable (optional)
+        Use only edges that are adjacency to specified nodes.
+        The default is all nodes.
+
+    Yields
+    ------
+    (x, y): 2-tuple
+        Generates 2-tuple of (degree, degree) values.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph()
+    >>> G.add_edge(1, 2)
+    >>> list(nx.node_degree_xy(G, x="out", y="in"))
+    [(1, 1)]
+    >>> list(nx.node_degree_xy(G, x="in", y="out"))
+    [(0, 0)]
+
+    Notes
+    -----
+    For undirected graphs, each edge is produced twice, once for each edge
+    representation (u, v) and (v, u), with the exception of self-loop edges
+    which only appear once.
+    """
+    nodes = set(G) if nodes is None else set(nodes)
+    if G.is_directed():
+        direction = {"out": G.out_degree, "in": G.in_degree}
+        xdeg = direction[x]
+        ydeg = direction[y]
+    else:
+        xdeg = ydeg = G.degree
+
+    for u, degu in xdeg(nodes, weight=weight):
+        # use G.edges to treat multigraphs correctly
+        neighbors = (nbr for _, nbr in G.edges(u) if nbr in nodes)
+        for _, degv in ydeg(neighbors, weight=weight):
+            yield degu, degv
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/asteroidal.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/asteroidal.py
new file mode 100644
index 0000000000000000000000000000000000000000..b308392a48626ade3b964aa544373e29d8a3a22b
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/asteroidal.py
@@ -0,0 +1,164 @@
+"""
+Algorithms for asteroidal triples and asteroidal numbers in graphs.
+
+An asteroidal triple in a graph G is a set of three non-adjacent vertices
+u, v and w such that there exist a path between any two of them that avoids
+closed neighborhood of the third. More formally, v_j, v_k belongs to the same
+connected component of G - N[v_i], where N[v_i] denotes the closed neighborhood
+of v_i. A graph which does not contain any asteroidal triples is called
+an AT-free graph. The class of AT-free graphs is a graph class for which
+many NP-complete problems are solvable in polynomial time. Amongst them,
+independent set and coloring.
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["is_at_free", "find_asteroidal_triple"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def find_asteroidal_triple(G):
+    r"""Find an asteroidal triple in the given graph.
+
+    An asteroidal triple is a triple of non-adjacent vertices such that
+    there exists a path between any two of them which avoids the closed
+    neighborhood of the third. It checks all independent triples of vertices
+    and whether they are an asteroidal triple or not. This is done with the
+    help of a data structure called a component structure.
+    A component structure encodes information about which vertices belongs to
+    the same connected component when the closed neighborhood of a given vertex
+    is removed from the graph. The algorithm used to check is the trivial
+    one, outlined in [1]_, which has a runtime of
+    :math:`O(|V||\overline{E} + |V||E|)`, where the second term is the
+    creation of the component structure.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        The graph to check whether is AT-free or not
+
+    Returns
+    -------
+    list or None
+        An asteroidal triple is returned as a list of nodes. If no asteroidal
+        triple exists, i.e. the graph is AT-free, then None is returned.
+
+    Notes
+    -----
+    The component structure and the algorithm is described in [1]_. The current
+    implementation implements the trivial algorithm for simple graphs.
+
+    References
+    ----------
+    .. [1] Ekkehard Köhler,
+       "Recognizing Graphs without asteroidal triples",
+       Journal of Discrete Algorithms 2, pages 439-452, 2004.
+       https://www.sciencedirect.com/science/article/pii/S157086670400019X
+    """
+    V = set(G.nodes)
+
+    if len(V) < 6:
+        # An asteroidal triple cannot exist in a graph with 5 or less vertices.
+        return None
+
+    component_structure = create_component_structure(G)
+
+    for u, v in nx.non_edges(G):
+        u_neighborhood = set(G[u]).union([u])
+        v_neighborhood = set(G[v]).union([v])
+        union_of_neighborhoods = u_neighborhood.union(v_neighborhood)
+        for w in V - union_of_neighborhoods:
+            # Check for each pair of vertices whether they belong to the
+            # same connected component when the closed neighborhood of the
+            # third is removed.
+            if (
+                component_structure[u][v] == component_structure[u][w]
+                and component_structure[v][u] == component_structure[v][w]
+                and component_structure[w][u] == component_structure[w][v]
+            ):
+                return [u, v, w]
+    return None
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def is_at_free(G):
+    """Check if a graph is AT-free.
+
+    The method uses the `find_asteroidal_triple` method to recognize
+    an AT-free graph. If no asteroidal triple is found the graph is
+    AT-free and True is returned. If at least one asteroidal triple is
+    found the graph is not AT-free and False is returned.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        The graph to check whether is AT-free or not.
+
+    Returns
+    -------
+    bool
+        True if G is AT-free and False otherwise.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2), (1, 2), (1, 3), (1, 4), (4, 5)])
+    >>> nx.is_at_free(G)
+    True
+
+    >>> G = nx.cycle_graph(6)
+    >>> nx.is_at_free(G)
+    False
+    """
+    return find_asteroidal_triple(G) is None
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def create_component_structure(G):
+    r"""Create component structure for G.
+
+    A *component structure* is an `nxn` array, denoted `c`, where `n` is
+    the number of vertices,  where each row and column corresponds to a vertex.
+
+    .. math::
+        c_{uv} = \begin{cases} 0, if v \in N[u] \\
+            k, if v \in component k of G \setminus N[u] \end{cases}
+
+    Where `k` is an arbitrary label for each component. The structure is used
+    to simplify the detection of asteroidal triples.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        Undirected, simple graph.
+
+    Returns
+    -------
+    component_structure : dictionary
+        A dictionary of dictionaries, keyed by pairs of vertices.
+
+    """
+    V = set(G.nodes)
+    component_structure = {}
+    for v in V:
+        label = 0
+        closed_neighborhood = set(G[v]).union({v})
+        row_dict = {}
+        for u in closed_neighborhood:
+            row_dict[u] = 0
+
+        G_reduced = G.subgraph(set(G.nodes) - closed_neighborhood)
+        for cc in nx.connected_components(G_reduced):
+            label += 1
+            for u in cc:
+                row_dict[u] = label
+
+        component_structure[v] = row_dict
+
+    return component_structure
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/__init__.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..edc66b47efa70f9813db54ee3bdc32847aaeff65
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/__init__.py
@@ -0,0 +1,88 @@
+r"""This module provides functions and operations for bipartite
+graphs.  Bipartite graphs `B = (U, V, E)` have two node sets `U,V` and edges in
+`E` that only connect nodes from opposite sets. It is common in the literature
+to use an spatial analogy referring to the two node sets as top and bottom nodes.
+
+The bipartite algorithms are not imported into the networkx namespace
+at the top level so the easiest way to use them is with:
+
+>>> from networkx.algorithms import bipartite
+
+NetworkX does not have a custom bipartite graph class but the Graph()
+or DiGraph() classes can be used to represent bipartite graphs. However,
+you have to keep track of which set each node belongs to, and make
+sure that there is no edge between nodes of the same set. The convention used
+in NetworkX is to use a node attribute named `bipartite` with values 0 or 1 to
+identify the sets each node belongs to. This convention is not enforced in
+the source code of bipartite functions, it's only a recommendation.
+
+For example:
+
+>>> B = nx.Graph()
+>>> # Add nodes with the node attribute "bipartite"
+>>> B.add_nodes_from([1, 2, 3, 4], bipartite=0)
+>>> B.add_nodes_from(["a", "b", "c"], bipartite=1)
+>>> # Add edges only between nodes of opposite node sets
+>>> B.add_edges_from([(1, "a"), (1, "b"), (2, "b"), (2, "c"), (3, "c"), (4, "a")])
+
+Many algorithms of the bipartite module of NetworkX require, as an argument, a
+container with all the nodes that belong to one set, in addition to the bipartite
+graph `B`. The functions in the bipartite package do not check that the node set
+is actually correct nor that the input graph is actually bipartite.
+If `B` is connected, you can find the two node sets using a two-coloring
+algorithm:
+
+>>> nx.is_connected(B)
+True
+>>> bottom_nodes, top_nodes = bipartite.sets(B)
+
+However, if the input graph is not connected, there are more than one possible
+colorations. This is the reason why we require the user to pass a container
+with all nodes of one bipartite node set as an argument to most bipartite
+functions. In the face of ambiguity, we refuse the temptation to guess and
+raise an :exc:`AmbiguousSolution <networkx.AmbiguousSolution>`
+Exception if the input graph for
+:func:`bipartite.sets <networkx.algorithms.bipartite.basic.sets>`
+is disconnected.
+
+Using the `bipartite` node attribute, you can easily get the two node sets:
+
+>>> top_nodes = {n for n, d in B.nodes(data=True) if d["bipartite"] == 0}
+>>> bottom_nodes = set(B) - top_nodes
+
+So you can easily use the bipartite algorithms that require, as an argument, a
+container with all nodes that belong to one node set:
+
+>>> print(round(bipartite.density(B, bottom_nodes), 2))
+0.5
+>>> G = bipartite.projected_graph(B, top_nodes)
+
+All bipartite graph generators in NetworkX build bipartite graphs with the
+`bipartite` node attribute. Thus, you can use the same approach:
+
+>>> RB = bipartite.random_graph(5, 7, 0.2)
+>>> RB_top = {n for n, d in RB.nodes(data=True) if d["bipartite"] == 0}
+>>> RB_bottom = set(RB) - RB_top
+>>> list(RB_top)
+[0, 1, 2, 3, 4]
+>>> list(RB_bottom)
+[5, 6, 7, 8, 9, 10, 11]
+
+For other bipartite graph generators see
+:mod:`Generators <networkx.algorithms.bipartite.generators>`.
+
+"""
+
+from networkx.algorithms.bipartite.basic import *
+from networkx.algorithms.bipartite.centrality import *
+from networkx.algorithms.bipartite.cluster import *
+from networkx.algorithms.bipartite.covering import *
+from networkx.algorithms.bipartite.edgelist import *
+from networkx.algorithms.bipartite.matching import *
+from networkx.algorithms.bipartite.matrix import *
+from networkx.algorithms.bipartite.projection import *
+from networkx.algorithms.bipartite.redundancy import *
+from networkx.algorithms.bipartite.spectral import *
+from networkx.algorithms.bipartite.generators import *
+from networkx.algorithms.bipartite.extendability import *
+from networkx.algorithms.bipartite.link_analysis import *
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/basic.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/basic.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d9a4d5b341bf9a14048acc1132e6f450685cc62
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/basic.py
@@ -0,0 +1,322 @@
+"""
+==========================
+Bipartite Graph Algorithms
+==========================
+"""
+
+import networkx as nx
+from networkx.algorithms.components import connected_components
+from networkx.exception import AmbiguousSolution
+
+__all__ = [
+    "is_bipartite",
+    "is_bipartite_node_set",
+    "color",
+    "sets",
+    "density",
+    "degrees",
+]
+
+
+@nx._dispatchable
+def color(G):
+    """Returns a two-coloring of the graph.
+
+    Raises an exception if the graph is not bipartite.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    color : dictionary
+        A dictionary keyed by node with a 1 or 0 as data for each node color.
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is not two-colorable.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import bipartite
+    >>> G = nx.path_graph(4)
+    >>> c = bipartite.color(G)
+    >>> print(c)
+    {0: 1, 1: 0, 2: 1, 3: 0}
+
+    You can use this to set a node attribute indicating the bipartite set:
+
+    >>> nx.set_node_attributes(G, c, "bipartite")
+    >>> print(G.nodes[0]["bipartite"])
+    1
+    >>> print(G.nodes[1]["bipartite"])
+    0
+    """
+    if G.is_directed():
+        import itertools
+
+        def neighbors(v):
+            return itertools.chain.from_iterable([G.predecessors(v), G.successors(v)])
+
+    else:
+        neighbors = G.neighbors
+
+    color = {}
+    for n in G:  # handle disconnected graphs
+        if n in color or len(G[n]) == 0:  # skip isolates
+            continue
+        queue = [n]
+        color[n] = 1  # nodes seen with color (1 or 0)
+        while queue:
+            v = queue.pop()
+            c = 1 - color[v]  # opposite color of node v
+            for w in neighbors(v):
+                if w in color:
+                    if color[w] == color[v]:
+                        raise nx.NetworkXError("Graph is not bipartite.")
+                else:
+                    color[w] = c
+                    queue.append(w)
+    # color isolates with 0
+    color.update(dict.fromkeys(nx.isolates(G), 0))
+    return color
+
+
+@nx._dispatchable
+def is_bipartite(G):
+    """Returns True if graph G is bipartite, False if not.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Examples
+    --------
+    >>> from networkx.algorithms import bipartite
+    >>> G = nx.path_graph(4)
+    >>> print(bipartite.is_bipartite(G))
+    True
+
+    See Also
+    --------
+    color, is_bipartite_node_set
+    """
+    try:
+        color(G)
+        return True
+    except nx.NetworkXError:
+        return False
+
+
+@nx._dispatchable
+def is_bipartite_node_set(G, nodes):
+    """Returns True if nodes and G/nodes are a bipartition of G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    nodes: list or container
+      Check if nodes are a one of a bipartite set.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import bipartite
+    >>> G = nx.path_graph(4)
+    >>> X = set([1, 3])
+    >>> bipartite.is_bipartite_node_set(G, X)
+    True
+
+    Notes
+    -----
+    An exception is raised if the input nodes are not distinct, because in this
+    case some bipartite algorithms will yield incorrect results.
+    For connected graphs the bipartite sets are unique.  This function handles
+    disconnected graphs.
+    """
+    S = set(nodes)
+
+    if len(S) < len(nodes):
+        # this should maybe just return False?
+        raise AmbiguousSolution(
+            "The input node set contains duplicates.\n"
+            "This may lead to incorrect results when using it in bipartite algorithms.\n"
+            "Consider using set(nodes) as the input"
+        )
+
+    for CC in (G.subgraph(c).copy() for c in connected_components(G)):
+        X, Y = sets(CC)
+        if not (
+            (X.issubset(S) and Y.isdisjoint(S)) or (Y.issubset(S) and X.isdisjoint(S))
+        ):
+            return False
+    return True
+
+
+@nx._dispatchable
+def sets(G, top_nodes=None):
+    """Returns bipartite node sets of graph G.
+
+    Raises an exception if the graph is not bipartite or if the input
+    graph is disconnected and thus more than one valid solution exists.
+    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
+    for further details on how bipartite graphs are handled in NetworkX.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    top_nodes : container, optional
+      Container with all nodes in one bipartite node set. If not supplied
+      it will be computed. But if more than one solution exists an exception
+      will be raised.
+
+    Returns
+    -------
+    X : set
+      Nodes from one side of the bipartite graph.
+    Y : set
+      Nodes from the other side.
+
+    Raises
+    ------
+    AmbiguousSolution
+      Raised if the input bipartite graph is disconnected and no container
+      with all nodes in one bipartite set is provided. When determining
+      the nodes in each bipartite set more than one valid solution is
+      possible if the input graph is disconnected.
+    NetworkXError
+      Raised if the input graph is not bipartite.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import bipartite
+    >>> G = nx.path_graph(4)
+    >>> X, Y = bipartite.sets(G)
+    >>> list(X)
+    [0, 2]
+    >>> list(Y)
+    [1, 3]
+
+    See Also
+    --------
+    color
+
+    """
+    if G.is_directed():
+        is_connected = nx.is_weakly_connected
+    else:
+        is_connected = nx.is_connected
+    if top_nodes is not None:
+        X = set(top_nodes)
+        Y = set(G) - X
+    else:
+        if not is_connected(G):
+            msg = "Disconnected graph: Ambiguous solution for bipartite sets."
+            raise nx.AmbiguousSolution(msg)
+        c = color(G)
+        X = {n for n, is_top in c.items() if is_top}
+        Y = {n for n, is_top in c.items() if not is_top}
+    return (X, Y)
+
+
+@nx._dispatchable(graphs="B")
+def density(B, nodes):
+    """Returns density of bipartite graph B.
+
+    Parameters
+    ----------
+    B : NetworkX graph
+
+    nodes: list or container
+      Nodes in one node set of the bipartite graph.
+
+    Returns
+    -------
+    d : float
+       The bipartite density
+
+    Examples
+    --------
+    >>> from networkx.algorithms import bipartite
+    >>> G = nx.complete_bipartite_graph(3, 2)
+    >>> X = set([0, 1, 2])
+    >>> bipartite.density(G, X)
+    1.0
+    >>> Y = set([3, 4])
+    >>> bipartite.density(G, Y)
+    1.0
+
+    Notes
+    -----
+    The container of nodes passed as argument must contain all nodes
+    in one of the two bipartite node sets to avoid ambiguity in the
+    case of disconnected graphs.
+    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
+    for further details on how bipartite graphs are handled in NetworkX.
+
+    See Also
+    --------
+    color
+    """
+    n = len(B)
+    m = nx.number_of_edges(B)
+    nb = len(nodes)
+    nt = n - nb
+    if m == 0:  # includes cases n==0 and n==1
+        d = 0.0
+    else:
+        if B.is_directed():
+            d = m / (2 * nb * nt)
+        else:
+            d = m / (nb * nt)
+    return d
+
+
+@nx._dispatchable(graphs="B", edge_attrs="weight")
+def degrees(B, nodes, weight=None):
+    """Returns the degrees of the two node sets in the bipartite graph B.
+
+    Parameters
+    ----------
+    B : NetworkX graph
+
+    nodes: list or container
+      Nodes in one node set of the bipartite graph.
+
+    weight : string or None, optional (default=None)
+       The edge attribute that holds the numerical value used as a weight.
+       If None, then each edge has weight 1.
+       The degree is the sum of the edge weights adjacent to the node.
+
+    Returns
+    -------
+    (degX,degY) : tuple of dictionaries
+       The degrees of the two bipartite sets as dictionaries keyed by node.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import bipartite
+    >>> G = nx.complete_bipartite_graph(3, 2)
+    >>> Y = set([3, 4])
+    >>> degX, degY = bipartite.degrees(G, Y)
+    >>> dict(degX)
+    {0: 2, 1: 2, 2: 2}
+
+    Notes
+    -----
+    The container of nodes passed as argument must contain all nodes
+    in one of the two bipartite node sets to avoid ambiguity in the
+    case of disconnected graphs.
+    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
+    for further details on how bipartite graphs are handled in NetworkX.
+
+    See Also
+    --------
+    color, density
+    """
+    bottom = set(nodes)
+    top = set(B) - bottom
+    return (B.degree(top, weight), B.degree(bottom, weight))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/centrality.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/centrality.py
new file mode 100644
index 0000000000000000000000000000000000000000..42d7270ee7d0bb18b56a55dc4c17dc19f5dc77a7
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/centrality.py
@@ -0,0 +1,290 @@
+import networkx as nx
+
+__all__ = ["degree_centrality", "betweenness_centrality", "closeness_centrality"]
+
+
+@nx._dispatchable(name="bipartite_degree_centrality")
+def degree_centrality(G, nodes):
+    r"""Compute the degree centrality for nodes in a bipartite network.
+
+    The degree centrality for a node `v` is the fraction of nodes
+    connected to it.
+
+    Parameters
+    ----------
+    G : graph
+       A bipartite network
+
+    nodes : list or container
+      Container with all nodes in one bipartite node set.
+
+    Returns
+    -------
+    centrality : dictionary
+       Dictionary keyed by node with bipartite degree centrality as the value.
+
+    Examples
+    --------
+    >>> G = nx.wheel_graph(5)
+    >>> top_nodes = {0, 1, 2}
+    >>> nx.bipartite.degree_centrality(G, nodes=top_nodes)
+    {0: 2.0, 1: 1.5, 2: 1.5, 3: 1.0, 4: 1.0}
+
+    See Also
+    --------
+    betweenness_centrality
+    closeness_centrality
+    :func:`~networkx.algorithms.bipartite.basic.sets`
+    :func:`~networkx.algorithms.bipartite.basic.is_bipartite`
+
+    Notes
+    -----
+    The nodes input parameter must contain all nodes in one bipartite node set,
+    but the dictionary returned contains all nodes from both bipartite node
+    sets. See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
+    for further details on how bipartite graphs are handled in NetworkX.
+
+    For unipartite networks, the degree centrality values are
+    normalized by dividing by the maximum possible degree (which is
+    `n-1` where `n` is the number of nodes in G).
+
+    In the bipartite case, the maximum possible degree of a node in a
+    bipartite node set is the number of nodes in the opposite node set
+    [1]_.  The degree centrality for a node `v` in the bipartite
+    sets `U` with `n` nodes and `V` with `m` nodes is
+
+    .. math::
+
+        d_{v} = \frac{deg(v)}{m}, \mbox{for} v \in U ,
+
+        d_{v} = \frac{deg(v)}{n}, \mbox{for} v \in V ,
+
+
+    where `deg(v)` is the degree of node `v`.
+
+    References
+    ----------
+    .. [1] Borgatti, S.P. and Halgin, D. In press. "Analyzing Affiliation
+        Networks". In Carrington, P. and Scott, J. (eds) The Sage Handbook
+        of Social Network Analysis. Sage Publications.
+        https://dx.doi.org/10.4135/9781446294413.n28
+    """
+    top = set(nodes)
+    bottom = set(G) - top
+    s = 1.0 / len(bottom)
+    centrality = {n: d * s for n, d in G.degree(top)}
+    s = 1.0 / len(top)
+    centrality.update({n: d * s for n, d in G.degree(bottom)})
+    return centrality
+
+
+@nx._dispatchable(name="bipartite_betweenness_centrality")
+def betweenness_centrality(G, nodes):
+    r"""Compute betweenness centrality for nodes in a bipartite network.
+
+    Betweenness centrality of a node `v` is the sum of the
+    fraction of all-pairs shortest paths that pass through `v`.
+
+    Values of betweenness are normalized by the maximum possible
+    value which for bipartite graphs is limited by the relative size
+    of the two node sets [1]_.
+
+    Let `n` be the number of nodes in the node set `U` and
+    `m` be the number of nodes in the node set `V`, then
+    nodes in `U` are normalized by dividing by
+
+    .. math::
+
+       \frac{1}{2} [m^2 (s + 1)^2 + m (s + 1)(2t - s - 1) - t (2s - t + 3)] ,
+
+    where
+
+    .. math::
+
+        s = (n - 1) \div m , t = (n - 1) \mod m ,
+
+    and nodes in `V` are normalized by dividing by
+
+    .. math::
+
+        \frac{1}{2} [n^2 (p + 1)^2 + n (p + 1)(2r - p - 1) - r (2p - r + 3)] ,
+
+    where,
+
+    .. math::
+
+        p = (m - 1) \div n , r = (m - 1) \mod n .
+
+    Parameters
+    ----------
+    G : graph
+        A bipartite graph
+
+    nodes : list or container
+        Container with all nodes in one bipartite node set.
+
+    Returns
+    -------
+    betweenness : dictionary
+        Dictionary keyed by node with bipartite betweenness centrality
+        as the value.
+
+    Examples
+    --------
+    >>> G = nx.cycle_graph(4)
+    >>> top_nodes = {1, 2}
+    >>> nx.bipartite.betweenness_centrality(G, nodes=top_nodes)
+    {0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25}
+
+    See Also
+    --------
+    degree_centrality
+    closeness_centrality
+    :func:`~networkx.algorithms.bipartite.basic.sets`
+    :func:`~networkx.algorithms.bipartite.basic.is_bipartite`
+
+    Notes
+    -----
+    The nodes input parameter must contain all nodes in one bipartite node set,
+    but the dictionary returned contains all nodes from both node sets.
+    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
+    for further details on how bipartite graphs are handled in NetworkX.
+
+
+    References
+    ----------
+    .. [1] Borgatti, S.P. and Halgin, D. In press. "Analyzing Affiliation
+        Networks". In Carrington, P. and Scott, J. (eds) The Sage Handbook
+        of Social Network Analysis. Sage Publications.
+        https://dx.doi.org/10.4135/9781446294413.n28
+    """
+    top = set(nodes)
+    bottom = set(G) - top
+    n = len(top)
+    m = len(bottom)
+    s, t = divmod(n - 1, m)
+    bet_max_top = (
+        ((m**2) * ((s + 1) ** 2))
+        + (m * (s + 1) * (2 * t - s - 1))
+        - (t * ((2 * s) - t + 3))
+    ) / 2.0
+    p, r = divmod(m - 1, n)
+    bet_max_bot = (
+        ((n**2) * ((p + 1) ** 2))
+        + (n * (p + 1) * (2 * r - p - 1))
+        - (r * ((2 * p) - r + 3))
+    ) / 2.0
+    betweenness = nx.betweenness_centrality(G, normalized=False, weight=None)
+    for node in top:
+        betweenness[node] /= bet_max_top
+    for node in bottom:
+        betweenness[node] /= bet_max_bot
+    return betweenness
+
+
+@nx._dispatchable(name="bipartite_closeness_centrality")
+def closeness_centrality(G, nodes, normalized=True):
+    r"""Compute the closeness centrality for nodes in a bipartite network.
+
+    The closeness of a node is the distance to all other nodes in the
+    graph or in the case that the graph is not connected to all other nodes
+    in the connected component containing that node.
+
+    Parameters
+    ----------
+    G : graph
+        A bipartite network
+
+    nodes : list or container
+        Container with all nodes in one bipartite node set.
+
+    normalized : bool, optional
+      If True (default) normalize by connected component size.
+
+    Returns
+    -------
+    closeness : dictionary
+        Dictionary keyed by node with bipartite closeness centrality
+        as the value.
+
+    Examples
+    --------
+    >>> G = nx.wheel_graph(5)
+    >>> top_nodes = {0, 1, 2}
+    >>> nx.bipartite.closeness_centrality(G, nodes=top_nodes)
+    {0: 1.5, 1: 1.2, 2: 1.2, 3: 1.0, 4: 1.0}
+
+    See Also
+    --------
+    betweenness_centrality
+    degree_centrality
+    :func:`~networkx.algorithms.bipartite.basic.sets`
+    :func:`~networkx.algorithms.bipartite.basic.is_bipartite`
+
+    Notes
+    -----
+    The nodes input parameter must contain all nodes in one bipartite node set,
+    but the dictionary returned contains all nodes from both node sets.
+    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
+    for further details on how bipartite graphs are handled in NetworkX.
+
+
+    Closeness centrality is normalized by the minimum distance possible.
+    In the bipartite case the minimum distance for a node in one bipartite
+    node set is 1 from all nodes in the other node set and 2 from all
+    other nodes in its own set [1]_. Thus the closeness centrality
+    for node `v`  in the two bipartite sets `U` with
+    `n` nodes and `V` with `m` nodes is
+
+    .. math::
+
+        c_{v} = \frac{m + 2(n - 1)}{d}, \mbox{for} v \in U,
+
+        c_{v} = \frac{n + 2(m - 1)}{d}, \mbox{for} v \in V,
+
+    where `d` is the sum of the distances from `v` to all
+    other nodes.
+
+    Higher values of closeness  indicate higher centrality.
+
+    As in the unipartite case, setting normalized=True causes the
+    values to normalized further to n-1 / size(G)-1 where n is the
+    number of nodes in the connected part of graph containing the
+    node.  If the graph is not completely connected, this algorithm
+    computes the closeness centrality for each connected part
+    separately.
+
+    References
+    ----------
+    .. [1] Borgatti, S.P. and Halgin, D. In press. "Analyzing Affiliation
+        Networks". In Carrington, P. and Scott, J. (eds) The Sage Handbook
+        of Social Network Analysis. Sage Publications.
+        https://dx.doi.org/10.4135/9781446294413.n28
+    """
+    closeness = {}
+    path_length = nx.single_source_shortest_path_length
+    top = set(nodes)
+    bottom = set(G) - top
+    n = len(top)
+    m = len(bottom)
+    for node in top:
+        sp = dict(path_length(G, node))
+        totsp = sum(sp.values())
+        if totsp > 0.0 and len(G) > 1:
+            closeness[node] = (m + 2 * (n - 1)) / totsp
+            if normalized:
+                s = (len(sp) - 1) / (len(G) - 1)
+                closeness[node] *= s
+        else:
+            closeness[node] = 0.0
+    for node in bottom:
+        sp = dict(path_length(G, node))
+        totsp = sum(sp.values())
+        if totsp > 0.0 and len(G) > 1:
+            closeness[node] = (n + 2 * (m - 1)) / totsp
+            if normalized:
+                s = (len(sp) - 1) / (len(G) - 1)
+                closeness[node] *= s
+        else:
+            closeness[node] = 0.0
+    return closeness
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/cluster.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/cluster.py
new file mode 100644
index 0000000000000000000000000000000000000000..78b3c0f087638483b594f52591363fb03a3bc0a3
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/cluster.py
@@ -0,0 +1,289 @@
+"""Functions for computing clustering of pairs"""
+
+import itertools
+
+import networkx as nx
+
+__all__ = [
+    "clustering",
+    "average_clustering",
+    "latapy_clustering",
+    "robins_alexander_clustering",
+]
+
+
+def cc_dot(nu, nv):
+    return len(nu & nv) / len(nu | nv)
+
+
+def cc_max(nu, nv):
+    return len(nu & nv) / max(len(nu), len(nv))
+
+
+def cc_min(nu, nv):
+    return len(nu & nv) / min(len(nu), len(nv))
+
+
+modes = {"dot": cc_dot, "min": cc_min, "max": cc_max}
+
+
+@nx._dispatchable
+def latapy_clustering(G, nodes=None, mode="dot"):
+    r"""Compute a bipartite clustering coefficient for nodes.
+
+    The bipartite clustering coefficient is a measure of local density
+    of connections defined as [1]_:
+
+    .. math::
+
+       c_u = \frac{\sum_{v \in N(N(u))} c_{uv} }{|N(N(u))|}
+
+    where `N(N(u))` are the second order neighbors of `u` in `G` excluding `u`,
+    and `c_{uv}` is the pairwise clustering coefficient between nodes
+    `u` and `v`.
+
+    The mode selects the function for `c_{uv}` which can be:
+
+    `dot`:
+
+    .. math::
+
+       c_{uv}=\frac{|N(u)\cap N(v)|}{|N(u) \cup N(v)|}
+
+    `min`:
+
+    .. math::
+
+       c_{uv}=\frac{|N(u)\cap N(v)|}{min(|N(u)|,|N(v)|)}
+
+    `max`:
+
+    .. math::
+
+       c_{uv}=\frac{|N(u)\cap N(v)|}{max(|N(u)|,|N(v)|)}
+
+
+    Parameters
+    ----------
+    G : graph
+        A bipartite graph
+
+    nodes : list or iterable (optional)
+        Compute bipartite clustering for these nodes. The default
+        is all nodes in G.
+
+    mode : string
+        The pairwise bipartite clustering method to be used in the computation.
+        It must be "dot", "max", or "min".
+
+    Returns
+    -------
+    clustering : dictionary
+        A dictionary keyed by node with the clustering coefficient value.
+
+
+    Examples
+    --------
+    >>> from networkx.algorithms import bipartite
+    >>> G = nx.path_graph(4)  # path graphs are bipartite
+    >>> c = bipartite.clustering(G)
+    >>> c[0]
+    0.5
+    >>> c = bipartite.clustering(G, mode="min")
+    >>> c[0]
+    1.0
+
+    See Also
+    --------
+    robins_alexander_clustering
+    average_clustering
+    networkx.algorithms.cluster.square_clustering
+
+    References
+    ----------
+    .. [1] Latapy, Matthieu, Clémence Magnien, and Nathalie Del Vecchio (2008).
+       Basic notions for the analysis of large two-mode networks.
+       Social Networks 30(1), 31--48.
+    """
+    if not nx.algorithms.bipartite.is_bipartite(G):
+        raise nx.NetworkXError("Graph is not bipartite")
+
+    try:
+        cc_func = modes[mode]
+    except KeyError as err:
+        raise nx.NetworkXError(
+            "Mode for bipartite clustering must be: dot, min or max"
+        ) from err
+
+    if nodes is None:
+        nodes = G
+    ccs = {}
+    for v in nodes:
+        cc = 0.0
+        nbrs2 = {u for nbr in G[v] for u in G[nbr]} - {v}
+        for u in nbrs2:
+            cc += cc_func(set(G[u]), set(G[v]))
+        if cc > 0.0:  # len(nbrs2)>0
+            cc /= len(nbrs2)
+        ccs[v] = cc
+    return ccs
+
+
+clustering = latapy_clustering
+
+
+@nx._dispatchable(name="bipartite_average_clustering")
+def average_clustering(G, nodes=None, mode="dot"):
+    r"""Compute the average bipartite clustering coefficient.
+
+    A clustering coefficient for the whole graph is the average,
+
+    .. math::
+
+       C = \frac{1}{n}\sum_{v \in G} c_v,
+
+    where `n` is the number of nodes in `G`.
+
+    Similar measures for the two bipartite sets can be defined [1]_
+
+    .. math::
+
+       C_X = \frac{1}{|X|}\sum_{v \in X} c_v,
+
+    where `X` is a bipartite set of `G`.
+
+    Parameters
+    ----------
+    G : graph
+        a bipartite graph
+
+    nodes : list or iterable, optional
+        A container of nodes to use in computing the average.
+        The nodes should be either the entire graph (the default) or one of the
+        bipartite sets.
+
+    mode : string
+        The pairwise bipartite clustering method.
+        It must be "dot", "max", or "min"
+
+    Returns
+    -------
+    clustering : float
+       The average bipartite clustering for the given set of nodes or the
+       entire graph if no nodes are specified.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import bipartite
+    >>> G = nx.star_graph(3)  # star graphs are bipartite
+    >>> bipartite.average_clustering(G)
+    0.75
+    >>> X, Y = bipartite.sets(G)
+    >>> bipartite.average_clustering(G, X)
+    0.0
+    >>> bipartite.average_clustering(G, Y)
+    1.0
+
+    See Also
+    --------
+    clustering
+
+    Notes
+    -----
+    The container of nodes passed to this function must contain all of the nodes
+    in one of the bipartite sets ("top" or "bottom") in order to compute
+    the correct average bipartite clustering coefficients.
+    See :mod:`bipartite documentation <networkx.algorithms.bipartite>`
+    for further details on how bipartite graphs are handled in NetworkX.
+
+
+    References
+    ----------
+    .. [1] Latapy, Matthieu, Clémence Magnien, and Nathalie Del Vecchio (2008).
+        Basic notions for the analysis of large two-mode networks.
+        Social Networks 30(1), 31--48.
+    """
+    if nodes is None:
+        nodes = G
+    ccs = latapy_clustering(G, nodes=nodes, mode=mode)
+    return sum(ccs[v] for v in nodes) / len(nodes)
+
+
+@nx._dispatchable
+def robins_alexander_clustering(G):
+    r"""Compute the bipartite clustering of G.
+
+    Robins and Alexander [1]_ defined bipartite clustering coefficient as
+    four times the number of four cycles `C_4` divided by the number of
+    three paths `L_3` in a bipartite graph:
+
+    .. math::
+
+       CC_4 = \frac{4 * C_4}{L_3}
+
+    Parameters
+    ----------
+    G : graph
+        a bipartite graph
+
+    Returns
+    -------
+    clustering : float
+       The Robins and Alexander bipartite clustering for the input graph.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import bipartite
+    >>> G = nx.davis_southern_women_graph()
+    >>> print(round(bipartite.robins_alexander_clustering(G), 3))
+    0.468
+
+    See Also
+    --------
+    latapy_clustering
+    networkx.algorithms.cluster.square_clustering
+
+    References
+    ----------
+    .. [1] Robins, G. and M. Alexander (2004). Small worlds among interlocking
+           directors: Network structure and distance in bipartite graphs.
+           Computational & Mathematical Organization Theory 10(1), 69–94.
+
+    """
+    if G.order() < 4 or G.size() < 3:
+        return 0
+    L_3 = _threepaths(G)
+    if L_3 == 0:
+        return 0
+    C_4 = _four_cycles(G)
+    return (4.0 * C_4) / L_3
+
+
+def _four_cycles(G):
+    # Also see `square_clustering` which counts squares in a similar way
+    cycles = 0
+    seen = set()
+    G_adj = G._adj
+    for v in G:
+        seen.add(v)
+        v_neighbors = set(G_adj[v])
+        if len(v_neighbors) < 2:
+            # Can't form a square without at least two neighbors
+            continue
+        two_hop_neighbors = set().union(*(G_adj[u] for u in v_neighbors))
+        two_hop_neighbors -= seen
+        for x in two_hop_neighbors:
+            p2 = len(v_neighbors.intersection(G_adj[x]))
+            cycles += p2 * (p2 - 1)
+    return cycles / 4
+
+
+def _threepaths(G):
+    paths = 0
+    for v in G:
+        for u in G[v]:
+            for w in set(G[u]) - {v}:
+                paths += len(set(G[w]) - {v, u})
+    # Divide by two because we count each three path twice
+    # one for each possible starting point
+    return paths / 2
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/covering.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/covering.py
new file mode 100644
index 0000000000000000000000000000000000000000..f937903e5576ec7313a774863c8470a4a271a252
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/covering.py
@@ -0,0 +1,57 @@
+"""Functions related to graph covers."""
+
+import networkx as nx
+from networkx.algorithms.bipartite.matching import hopcroft_karp_matching
+from networkx.algorithms.covering import min_edge_cover as _min_edge_cover
+from networkx.utils import not_implemented_for
+
+__all__ = ["min_edge_cover"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(name="bipartite_min_edge_cover")
+def min_edge_cover(G, matching_algorithm=None):
+    """Returns a set of edges which constitutes
+    the minimum edge cover of the graph.
+
+    The smallest edge cover can be found in polynomial time by finding
+    a maximum matching and extending it greedily so that all nodes
+    are covered.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected bipartite graph.
+
+    matching_algorithm : function
+        A function that returns a maximum cardinality matching in a
+        given bipartite graph. The function must take one input, the
+        graph ``G``, and return a dictionary mapping each node to its
+        mate. If not specified,
+        :func:`~networkx.algorithms.bipartite.matching.hopcroft_karp_matching`
+        will be used. Other possibilities include
+        :func:`~networkx.algorithms.bipartite.matching.eppstein_matching`,
+
+    Returns
+    -------
+    set
+        A set of the edges in a minimum edge cover of the graph, given as
+        pairs of nodes. It contains both the edges `(u, v)` and `(v, u)`
+        for given nodes `u` and `v` among the edges of minimum edge cover.
+
+    Notes
+    -----
+    An edge cover of a graph is a set of edges such that every node of
+    the graph is incident to at least one edge of the set.
+    A minimum edge cover is an edge covering of smallest cardinality.
+
+    Due to its implementation, the worst-case running time of this algorithm
+    is bounded by the worst-case running time of the function
+    ``matching_algorithm``.
+    """
+    if G.order() == 0:  # Special case for the empty graph
+        return set()
+    if matching_algorithm is None:
+        matching_algorithm = hopcroft_karp_matching
+    return _min_edge_cover(G, matching_algorithm=matching_algorithm)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/edgelist.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/edgelist.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2c6b9c94fa697884546a63d365040db056d233f
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/edgelist.py
@@ -0,0 +1,360 @@
+"""
+********************
+Bipartite Edge Lists
+********************
+Read and write NetworkX graphs as bipartite edge lists.
+
+Format
+------
+You can read or write three formats of edge lists with these functions.
+
+Node pairs with no data::
+
+ 1 2
+
+Python dictionary as data::
+
+ 1 2 {'weight':7, 'color':'green'}
+
+Arbitrary data::
+
+ 1 2 7 green
+
+For each edge (u, v) the node u is assigned to part 0 and the node v to part 1.
+"""
+
+__all__ = ["generate_edgelist", "write_edgelist", "parse_edgelist", "read_edgelist"]
+
+import networkx as nx
+from networkx.utils import not_implemented_for, open_file
+
+
+@open_file(1, mode="wb")
+def write_edgelist(G, path, comments="#", delimiter=" ", data=True, encoding="utf-8"):
+    """Write a bipartite graph as a list of edges.
+
+    Parameters
+    ----------
+    G : Graph
+       A NetworkX bipartite graph
+    path : file or string
+       File or filename to write. If a file is provided, it must be
+       opened in 'wb' mode. Filenames ending in .gz or .bz2 will be compressed.
+    comments : string, optional
+       The character used to indicate the start of a comment
+    delimiter : string, optional
+       The string used to separate values.  The default is whitespace.
+    data : bool or list, optional
+       If False write no edge data.
+       If True write a string representation of the edge data dictionary..
+       If a list (or other iterable) is provided, write the  keys specified
+       in the list.
+    encoding: string, optional
+       Specify which encoding to use when writing file.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> G.add_nodes_from([0, 2], bipartite=0)
+    >>> G.add_nodes_from([1, 3], bipartite=1)
+    >>> nx.write_edgelist(G, "test.edgelist")
+    >>> fh = open("test.edgelist_open", "wb")
+    >>> nx.write_edgelist(G, fh)
+    >>> nx.write_edgelist(G, "test.edgelist.gz")
+    >>> nx.write_edgelist(G, "test.edgelist_nodata.gz", data=False)
+
+    >>> G = nx.Graph()
+    >>> G.add_edge(1, 2, weight=7, color="red")
+    >>> nx.write_edgelist(G, "test.edgelist_bigger_nodata", data=False)
+    >>> nx.write_edgelist(G, "test.edgelist_color", data=["color"])
+    >>> nx.write_edgelist(G, "test.edgelist_color_weight", data=["color", "weight"])
+
+    See Also
+    --------
+    write_edgelist
+    generate_edgelist
+    """
+    for line in generate_edgelist(G, delimiter, data):
+        line += "\n"
+        path.write(line.encode(encoding))
+
+
+@not_implemented_for("directed")
+def generate_edgelist(G, delimiter=" ", data=True):
+    """Generate a single line of the bipartite graph G in edge list format.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       The graph is assumed to have node attribute `part` set to 0,1 representing
+       the two graph parts
+
+    delimiter : string, optional
+       Separator for node labels
+
+    data : bool or list of keys
+       If False generate no edge data.  If True use a dictionary
+       representation of edge data.  If a list of keys use a list of data
+       values corresponding to the keys.
+
+    Returns
+    -------
+    lines : string
+        Lines of data in adjlist format.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import bipartite
+    >>> G = nx.path_graph(4)
+    >>> G.add_nodes_from([0, 2], bipartite=0)
+    >>> G.add_nodes_from([1, 3], bipartite=1)
+    >>> G[1][2]["weight"] = 3
+    >>> G[2][3]["capacity"] = 12
+    >>> for line in bipartite.generate_edgelist(G, data=False):
+    ...     print(line)
+    0 1
+    2 1
+    2 3
+
+    >>> for line in bipartite.generate_edgelist(G):
+    ...     print(line)
+    0 1 {}
+    2 1 {'weight': 3}
+    2 3 {'capacity': 12}
+
+    >>> for line in bipartite.generate_edgelist(G, data=["weight"]):
+    ...     print(line)
+    0 1
+    2 1 3
+    2 3
+    """
+    try:
+        part0 = [n for n, d in G.nodes.items() if d["bipartite"] == 0]
+    except BaseException as err:
+        raise AttributeError("Missing node attribute `bipartite`") from err
+    if data is True or data is False:
+        for n in part0:
+            for edge in G.edges(n, data=data):
+                yield delimiter.join(map(str, edge))
+    else:
+        for n in part0:
+            for u, v, d in G.edges(n, data=True):
+                edge = [u, v]
+                try:
+                    edge.extend(d[k] for k in data)
+                except KeyError:
+                    pass  # missing data for this edge, should warn?
+                yield delimiter.join(map(str, edge))
+
+
+@nx._dispatchable(name="bipartite_parse_edgelist", graphs=None, returns_graph=True)
+def parse_edgelist(
+    lines, comments="#", delimiter=None, create_using=None, nodetype=None, data=True
+):
+    """Parse lines of an edge list representation of a bipartite graph.
+
+    Parameters
+    ----------
+    lines : list or iterator of strings
+        Input data in edgelist format
+    comments : string, optional
+       Marker for comment lines
+    delimiter : string, optional
+       Separator for node labels
+    create_using: NetworkX graph container, optional
+       Use given NetworkX graph for holding nodes or edges.
+    nodetype : Python type, optional
+       Convert nodes to this type.
+    data : bool or list of (label,type) tuples
+       If False generate no edge data or if True use a dictionary
+       representation of edge data or a list tuples specifying dictionary
+       key names and types for edge data.
+
+    Returns
+    -------
+    G: NetworkX Graph
+        The bipartite graph corresponding to lines
+
+    Examples
+    --------
+    Edgelist with no data:
+
+    >>> from networkx.algorithms import bipartite
+    >>> lines = ["1 2", "2 3", "3 4"]
+    >>> G = bipartite.parse_edgelist(lines, nodetype=int)
+    >>> sorted(G.nodes())
+    [1, 2, 3, 4]
+    >>> sorted(G.nodes(data=True))
+    [(1, {'bipartite': 0}), (2, {'bipartite': 0}), (3, {'bipartite': 0}), (4, {'bipartite': 1})]
+    >>> sorted(G.edges())
+    [(1, 2), (2, 3), (3, 4)]
+
+    Edgelist with data in Python dictionary representation:
+
+    >>> lines = ["1 2 {'weight':3}", "2 3 {'weight':27}", "3 4 {'weight':3.0}"]
+    >>> G = bipartite.parse_edgelist(lines, nodetype=int)
+    >>> sorted(G.nodes())
+    [1, 2, 3, 4]
+    >>> sorted(G.edges(data=True))
+    [(1, 2, {'weight': 3}), (2, 3, {'weight': 27}), (3, 4, {'weight': 3.0})]
+
+    Edgelist with data in a list:
+
+    >>> lines = ["1 2 3", "2 3 27", "3 4 3.0"]
+    >>> G = bipartite.parse_edgelist(lines, nodetype=int, data=(("weight", float),))
+    >>> sorted(G.nodes())
+    [1, 2, 3, 4]
+    >>> sorted(G.edges(data=True))
+    [(1, 2, {'weight': 3.0}), (2, 3, {'weight': 27.0}), (3, 4, {'weight': 3.0})]
+
+    See Also
+    --------
+    """
+    from ast import literal_eval
+
+    G = nx.empty_graph(0, create_using)
+    for line in lines:
+        p = line.find(comments)
+        if p >= 0:
+            line = line[:p]
+        if not len(line):
+            continue
+        # split line, should have 2 or more
+        s = line.rstrip("\n").split(delimiter)
+        if len(s) < 2:
+            continue
+        u = s.pop(0)
+        v = s.pop(0)
+        d = s
+        if nodetype is not None:
+            try:
+                u = nodetype(u)
+                v = nodetype(v)
+            except BaseException as err:
+                raise TypeError(
+                    f"Failed to convert nodes {u},{v} to type {nodetype}."
+                ) from err
+
+        if len(d) == 0 or data is False:
+            # no data or data type specified
+            edgedata = {}
+        elif data is True:
+            # no edge types specified
+            try:  # try to evaluate as dictionary
+                edgedata = dict(literal_eval(" ".join(d)))
+            except BaseException as err:
+                raise TypeError(
+                    f"Failed to convert edge data ({d}) to dictionary."
+                ) from err
+        else:
+            # convert edge data to dictionary with specified keys and type
+            if len(d) != len(data):
+                raise IndexError(
+                    f"Edge data {d} and data_keys {data} are not the same length"
+                )
+            edgedata = {}
+            for (edge_key, edge_type), edge_value in zip(data, d):
+                try:
+                    edge_value = edge_type(edge_value)
+                except BaseException as err:
+                    raise TypeError(
+                        f"Failed to convert {edge_key} data "
+                        f"{edge_value} to type {edge_type}."
+                    ) from err
+                edgedata.update({edge_key: edge_value})
+        G.add_node(u, bipartite=0)
+        G.add_node(v, bipartite=1)
+        G.add_edge(u, v, **edgedata)
+    return G
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(name="bipartite_read_edgelist", graphs=None, returns_graph=True)
+def read_edgelist(
+    path,
+    comments="#",
+    delimiter=None,
+    create_using=None,
+    nodetype=None,
+    data=True,
+    edgetype=None,
+    encoding="utf-8",
+):
+    """Read a bipartite graph from a list of edges.
+
+    Parameters
+    ----------
+    path : file or string
+       File or filename to read. If a file is provided, it must be
+       opened in 'rb' mode.
+       Filenames ending in .gz or .bz2 will be decompressed.
+    comments : string, optional
+       The character used to indicate the start of a comment.
+    delimiter : string, optional
+       The string used to separate values.  The default is whitespace.
+    create_using : Graph container, optional,
+       Use specified container to build graph.  The default is networkx.Graph,
+       an undirected graph.
+    nodetype : int, float, str, Python type, optional
+       Convert node data from strings to specified type
+    data : bool or list of (label,type) tuples
+       Tuples specifying dictionary key names and types for edge data
+    edgetype : int, float, str, Python type, optional OBSOLETE
+       Convert edge data from strings to specified type and use as 'weight'
+    encoding: string, optional
+       Specify which encoding to use when reading file.
+
+    Returns
+    -------
+    G : graph
+       A networkx Graph or other type specified with create_using
+
+    Examples
+    --------
+    >>> from networkx.algorithms import bipartite
+    >>> G = nx.path_graph(4)
+    >>> G.add_nodes_from([0, 2], bipartite=0)
+    >>> G.add_nodes_from([1, 3], bipartite=1)
+    >>> bipartite.write_edgelist(G, "test.edgelist")
+    >>> G = bipartite.read_edgelist("test.edgelist")
+
+    >>> fh = open("test.edgelist", "rb")
+    >>> G = bipartite.read_edgelist(fh)
+    >>> fh.close()
+
+    >>> G = bipartite.read_edgelist("test.edgelist", nodetype=int)
+
+    Edgelist with data in a list:
+
+    >>> textline = "1 2 3"
+    >>> fh = open("test.edgelist", "w")
+    >>> d = fh.write(textline)
+    >>> fh.close()
+    >>> G = bipartite.read_edgelist(
+    ...     "test.edgelist", nodetype=int, data=(("weight", float),)
+    ... )
+    >>> list(G)
+    [1, 2]
+    >>> list(G.edges(data=True))
+    [(1, 2, {'weight': 3.0})]
+
+    See parse_edgelist() for more examples of formatting.
+
+    See Also
+    --------
+    parse_edgelist
+
+    Notes
+    -----
+    Since nodes must be hashable, the function nodetype must return hashable
+    types (e.g. int, float, str, frozenset - or tuples of those, etc.)
+    """
+    lines = (line.decode(encoding) for line in path)
+    return parse_edgelist(
+        lines,
+        comments=comments,
+        delimiter=delimiter,
+        create_using=create_using,
+        nodetype=nodetype,
+        data=data,
+    )
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/extendability.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/extendability.py
new file mode 100644
index 0000000000000000000000000000000000000000..61d8d067d9792659ed7097340c5ece28d9dc2e8c
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/extendability.py
@@ -0,0 +1,105 @@
+"""Provides a function for computing the extendability of a graph which is
+undirected, simple, connected and bipartite and contains at least one perfect matching."""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["maximal_extendability"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def maximal_extendability(G):
+    """Computes the extendability of a graph.
+
+    The extendability of a graph is defined as the maximum $k$ for which `G`
+    is $k$-extendable. Graph `G` is $k$-extendable if and only if `G` has a
+    perfect matching and every set of $k$ independent edges can be extended
+    to a perfect matching in `G`.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        A fully-connected bipartite graph without self-loops
+
+    Returns
+    -------
+    extendability : int
+
+    Raises
+    ------
+    NetworkXError
+       If the graph `G` is disconnected.
+       If the graph `G` is not bipartite.
+       If the graph `G` does not contain a perfect matching.
+       If the residual graph of `G` is not strongly connected.
+
+    Notes
+    -----
+    Definition:
+    Let `G` be a simple, connected, undirected and bipartite graph with a perfect
+    matching M and bipartition (U,V). The residual graph of `G`, denoted by $G_M$,
+    is the graph obtained from G by directing the edges of M from V to U and the
+    edges that do not belong to M from U to V.
+
+    Lemma [1]_ :
+    Let M be a perfect matching of `G`. `G` is $k$-extendable if and only if its residual
+    graph $G_M$ is strongly connected and there are $k$ vertex-disjoint directed
+    paths between every vertex of U and every vertex of V.
+
+    Assuming that input graph `G` is undirected, simple, connected, bipartite and contains
+    a perfect matching M, this function constructs the residual graph $G_M$ of G and
+    returns the minimum value among the maximum vertex-disjoint directed paths between
+    every vertex of U and every vertex of V in $G_M$. By combining the definitions
+    and the lemma, this value represents the extendability of the graph `G`.
+
+    Time complexity O($n^3$ $m^2$)) where $n$ is the number of vertices
+    and $m$ is the number of edges.
+
+    References
+    ----------
+    .. [1] "A polynomial algorithm for the extendability problem in bipartite graphs",
+          J. Lakhal, L. Litzler, Information Processing Letters, 1998.
+    .. [2] "On n-extendible graphs", M. D. Plummer, Discrete Mathematics, 31:201–210, 1980
+          https://doi.org/10.1016/0012-365X(80)90037-0
+
+    """
+    if not nx.is_connected(G):
+        raise nx.NetworkXError("Graph G is not connected")
+
+    if not nx.bipartite.is_bipartite(G):
+        raise nx.NetworkXError("Graph G is not bipartite")
+
+    U, V = nx.bipartite.sets(G)
+
+    maximum_matching = nx.bipartite.hopcroft_karp_matching(G)
+
+    if not nx.is_perfect_matching(G, maximum_matching):
+        raise nx.NetworkXError("Graph G does not contain a perfect matching")
+
+    # list of edges in perfect matching, directed from V to U
+    pm = [(node, maximum_matching[node]) for node in V & maximum_matching.keys()]
+
+    # Direct all the edges of G, from V to U if in matching, else from U to V
+    directed_edges = [
+        (x, y) if (x in V and (x, y) in pm) or (x in U and (y, x) not in pm) else (y, x)
+        for x, y in G.edges
+    ]
+
+    # Construct the residual graph of G
+    residual_G = nx.DiGraph()
+    residual_G.add_nodes_from(G)
+    residual_G.add_edges_from(directed_edges)
+
+    if not nx.is_strongly_connected(residual_G):
+        raise nx.NetworkXError("The residual graph of G is not strongly connected")
+
+    # For node-pairs between V & U, keep min of max number of node-disjoint paths
+    # Variable $k$ stands for the extendability of graph G
+    k = float("inf")
+    for u in U:
+        for v in V:
+            num_paths = sum(1 for _ in nx.node_disjoint_paths(residual_G, u, v))
+            k = k if k < num_paths else num_paths
+    return k
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/generators.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/generators.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e73a4d132d5ab34586f0b5d4aee967b6f77738f
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/generators.py
@@ -0,0 +1,603 @@
+"""
+Generators and functions for bipartite graphs.
+"""
+
+import math
+import numbers
+from functools import reduce
+
+import networkx as nx
+from networkx.utils import nodes_or_number, py_random_state
+
+__all__ = [
+    "configuration_model",
+    "havel_hakimi_graph",
+    "reverse_havel_hakimi_graph",
+    "alternating_havel_hakimi_graph",
+    "preferential_attachment_graph",
+    "random_graph",
+    "gnmk_random_graph",
+    "complete_bipartite_graph",
+]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number([0, 1])
+def complete_bipartite_graph(n1, n2, create_using=None):
+    """Returns the complete bipartite graph `K_{n_1,n_2}`.
+
+    The graph is composed of two partitions with nodes 0 to (n1 - 1)
+    in the first and nodes n1 to (n1 + n2 - 1) in the second.
+    Each node in the first is connected to each node in the second.
+
+    Parameters
+    ----------
+    n1, n2 : integer or iterable container of nodes
+        If integers, nodes are from `range(n1)` and `range(n1, n1 + n2)`.
+        If a container, the elements are the nodes.
+    create_using : NetworkX graph instance, (default: nx.Graph)
+       Return graph of this type.
+
+    Notes
+    -----
+    Nodes are the integers 0 to `n1 + n2 - 1` unless either n1 or n2 are
+    containers of nodes. If only one of n1 or n2 are integers, that
+    integer is replaced by `range` of that integer.
+
+    The nodes are assigned the attribute 'bipartite' with the value 0 or 1
+    to indicate which bipartite set the node belongs to.
+
+    This function is not imported in the main namespace.
+    To use it use nx.bipartite.complete_bipartite_graph
+    """
+    G = nx.empty_graph(0, create_using)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    n1, top = n1
+    n2, bottom = n2
+    if isinstance(n1, numbers.Integral) and isinstance(n2, numbers.Integral):
+        bottom = [n1 + i for i in bottom]
+    G.add_nodes_from(top, bipartite=0)
+    G.add_nodes_from(bottom, bipartite=1)
+    if len(G) != len(top) + len(bottom):
+        raise nx.NetworkXError("Inputs n1 and n2 must contain distinct nodes")
+    G.add_edges_from((u, v) for u in top for v in bottom)
+    G.graph["name"] = f"complete_bipartite_graph({len(top)}, {len(bottom)})"
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(name="bipartite_configuration_model", graphs=None, returns_graph=True)
+def configuration_model(aseq, bseq, create_using=None, seed=None):
+    """Returns a random bipartite graph from two given degree sequences.
+
+    Parameters
+    ----------
+    aseq : list
+       Degree sequence for node set A.
+    bseq : list
+       Degree sequence for node set B.
+    create_using : NetworkX graph instance, optional
+       Return graph of this type.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    The graph is composed of two partitions. Set A has nodes 0 to
+    (len(aseq) - 1) and set B has nodes len(aseq) to (len(bseq) - 1).
+    Nodes from set A are connected to nodes in set B by choosing
+    randomly from the possible free stubs, one in A and one in B.
+
+    Notes
+    -----
+    The sum of the two sequences must be equal: sum(aseq)=sum(bseq)
+    If no graph type is specified use MultiGraph with parallel edges.
+    If you want a graph with no parallel edges use create_using=Graph()
+    but then the resulting degree sequences might not be exact.
+
+    The nodes are assigned the attribute 'bipartite' with the value 0 or 1
+    to indicate which bipartite set the node belongs to.
+
+    This function is not imported in the main namespace.
+    To use it use nx.bipartite.configuration_model
+    """
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    # length and sum of each sequence
+    lena = len(aseq)
+    lenb = len(bseq)
+    suma = sum(aseq)
+    sumb = sum(bseq)
+
+    if not suma == sumb:
+        raise nx.NetworkXError(
+            f"invalid degree sequences, sum(aseq)!=sum(bseq),{suma},{sumb}"
+        )
+
+    G = _add_nodes_with_bipartite_label(G, lena, lenb)
+
+    if len(aseq) == 0 or max(aseq) == 0:
+        return G  # done if no edges
+
+    # build lists of degree-repeated vertex numbers
+    stubs = [[v] * aseq[v] for v in range(lena)]
+    astubs = [x for subseq in stubs for x in subseq]
+
+    stubs = [[v] * bseq[v - lena] for v in range(lena, lena + lenb)]
+    bstubs = [x for subseq in stubs for x in subseq]
+
+    # shuffle lists
+    seed.shuffle(astubs)
+    seed.shuffle(bstubs)
+
+    G.add_edges_from([astubs[i], bstubs[i]] for i in range(suma))
+
+    G.name = "bipartite_configuration_model"
+    return G
+
+
+@nx._dispatchable(name="bipartite_havel_hakimi_graph", graphs=None, returns_graph=True)
+def havel_hakimi_graph(aseq, bseq, create_using=None):
+    """Returns a bipartite graph from two given degree sequences using a
+    Havel-Hakimi style construction.
+
+    The graph is composed of two partitions. Set A has nodes 0 to
+    (len(aseq) - 1) and set B has nodes len(aseq) to (len(bseq) - 1).
+    Nodes from the set A are connected to nodes in the set B by
+    connecting the highest degree nodes in set A to the highest degree
+    nodes in set B until all stubs are connected.
+
+    Parameters
+    ----------
+    aseq : list
+       Degree sequence for node set A.
+    bseq : list
+       Degree sequence for node set B.
+    create_using : NetworkX graph instance, optional
+       Return graph of this type.
+
+    Notes
+    -----
+    The sum of the two sequences must be equal: sum(aseq)=sum(bseq)
+    If no graph type is specified use MultiGraph with parallel edges.
+    If you want a graph with no parallel edges use create_using=Graph()
+    but then the resulting degree sequences might not be exact.
+
+    The nodes are assigned the attribute 'bipartite' with the value 0 or 1
+    to indicate which bipartite set the node belongs to.
+
+    This function is not imported in the main namespace.
+    To use it use nx.bipartite.havel_hakimi_graph
+    """
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    # length of the each sequence
+    naseq = len(aseq)
+    nbseq = len(bseq)
+
+    suma = sum(aseq)
+    sumb = sum(bseq)
+
+    if not suma == sumb:
+        raise nx.NetworkXError(
+            f"invalid degree sequences, sum(aseq)!=sum(bseq),{suma},{sumb}"
+        )
+
+    G = _add_nodes_with_bipartite_label(G, naseq, nbseq)
+
+    if len(aseq) == 0 or max(aseq) == 0:
+        return G  # done if no edges
+
+    # build list of degree-repeated vertex numbers
+    astubs = [[aseq[v], v] for v in range(naseq)]
+    bstubs = [[bseq[v - naseq], v] for v in range(naseq, naseq + nbseq)]
+    astubs.sort()
+    while astubs:
+        (degree, u) = astubs.pop()  # take of largest degree node in the a set
+        if degree == 0:
+            break  # done, all are zero
+        # connect the source to largest degree nodes in the b set
+        bstubs.sort()
+        for target in bstubs[-degree:]:
+            v = target[1]
+            G.add_edge(u, v)
+            target[0] -= 1  # note this updates bstubs too.
+            if target[0] == 0:
+                bstubs.remove(target)
+
+    G.name = "bipartite_havel_hakimi_graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def reverse_havel_hakimi_graph(aseq, bseq, create_using=None):
+    """Returns a bipartite graph from two given degree sequences using a
+    Havel-Hakimi style construction.
+
+    The graph is composed of two partitions. Set A has nodes 0 to
+    (len(aseq) - 1) and set B has nodes len(aseq) to (len(bseq) - 1).
+    Nodes from set A are connected to nodes in the set B by connecting
+    the highest degree nodes in set A to the lowest degree nodes in
+    set B until all stubs are connected.
+
+    Parameters
+    ----------
+    aseq : list
+       Degree sequence for node set A.
+    bseq : list
+       Degree sequence for node set B.
+    create_using : NetworkX graph instance, optional
+       Return graph of this type.
+
+    Notes
+    -----
+    The sum of the two sequences must be equal: sum(aseq)=sum(bseq)
+    If no graph type is specified use MultiGraph with parallel edges.
+    If you want a graph with no parallel edges use create_using=Graph()
+    but then the resulting degree sequences might not be exact.
+
+    The nodes are assigned the attribute 'bipartite' with the value 0 or 1
+    to indicate which bipartite set the node belongs to.
+
+    This function is not imported in the main namespace.
+    To use it use nx.bipartite.reverse_havel_hakimi_graph
+    """
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    # length of the each sequence
+    lena = len(aseq)
+    lenb = len(bseq)
+    suma = sum(aseq)
+    sumb = sum(bseq)
+
+    if not suma == sumb:
+        raise nx.NetworkXError(
+            f"invalid degree sequences, sum(aseq)!=sum(bseq),{suma},{sumb}"
+        )
+
+    G = _add_nodes_with_bipartite_label(G, lena, lenb)
+
+    if len(aseq) == 0 or max(aseq) == 0:
+        return G  # done if no edges
+
+    # build list of degree-repeated vertex numbers
+    astubs = [[aseq[v], v] for v in range(lena)]
+    bstubs = [[bseq[v - lena], v] for v in range(lena, lena + lenb)]
+    astubs.sort()
+    bstubs.sort()
+    while astubs:
+        (degree, u) = astubs.pop()  # take of largest degree node in the a set
+        if degree == 0:
+            break  # done, all are zero
+        # connect the source to the smallest degree nodes in the b set
+        for target in bstubs[0:degree]:
+            v = target[1]
+            G.add_edge(u, v)
+            target[0] -= 1  # note this updates bstubs too.
+            if target[0] == 0:
+                bstubs.remove(target)
+
+    G.name = "bipartite_reverse_havel_hakimi_graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def alternating_havel_hakimi_graph(aseq, bseq, create_using=None):
+    """Returns a bipartite graph from two given degree sequences using
+    an alternating Havel-Hakimi style construction.
+
+    The graph is composed of two partitions. Set A has nodes 0 to
+    (len(aseq) - 1) and set B has nodes len(aseq) to (len(bseq) - 1).
+    Nodes from the set A are connected to nodes in the set B by
+    connecting the highest degree nodes in set A to alternatively the
+    highest and the lowest degree nodes in set B until all stubs are
+    connected.
+
+    Parameters
+    ----------
+    aseq : list
+       Degree sequence for node set A.
+    bseq : list
+       Degree sequence for node set B.
+    create_using : NetworkX graph instance, optional
+       Return graph of this type.
+
+    Notes
+    -----
+    The sum of the two sequences must be equal: sum(aseq)=sum(bseq)
+    If no graph type is specified use MultiGraph with parallel edges.
+    If you want a graph with no parallel edges use create_using=Graph()
+    but then the resulting degree sequences might not be exact.
+
+    The nodes are assigned the attribute 'bipartite' with the value 0 or 1
+    to indicate which bipartite set the node belongs to.
+
+    This function is not imported in the main namespace.
+    To use it use nx.bipartite.alternating_havel_hakimi_graph
+    """
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    # length of the each sequence
+    naseq = len(aseq)
+    nbseq = len(bseq)
+    suma = sum(aseq)
+    sumb = sum(bseq)
+
+    if not suma == sumb:
+        raise nx.NetworkXError(
+            f"invalid degree sequences, sum(aseq)!=sum(bseq),{suma},{sumb}"
+        )
+
+    G = _add_nodes_with_bipartite_label(G, naseq, nbseq)
+
+    if len(aseq) == 0 or max(aseq) == 0:
+        return G  # done if no edges
+    # build list of degree-repeated vertex numbers
+    astubs = [[aseq[v], v] for v in range(naseq)]
+    bstubs = [[bseq[v - naseq], v] for v in range(naseq, naseq + nbseq)]
+    while astubs:
+        astubs.sort()
+        (degree, u) = astubs.pop()  # take of largest degree node in the a set
+        if degree == 0:
+            break  # done, all are zero
+        bstubs.sort()
+        small = bstubs[0 : degree // 2]  # add these low degree targets
+        large = bstubs[(-degree + degree // 2) :]  # now high degree targets
+        stubs = [x for z in zip(large, small) for x in z]  # combine, sorry
+        if len(stubs) < len(small) + len(large):  # check for zip truncation
+            stubs.append(large.pop())
+        for target in stubs:
+            v = target[1]
+            G.add_edge(u, v)
+            target[0] -= 1  # note this updates bstubs too.
+            if target[0] == 0:
+                bstubs.remove(target)
+
+    G.name = "bipartite_alternating_havel_hakimi_graph"
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def preferential_attachment_graph(aseq, p, create_using=None, seed=None):
+    """Create a bipartite graph with a preferential attachment model from
+    a given single degree sequence.
+
+    The graph is composed of two partitions. Set A has nodes 0 to
+    (len(aseq) - 1) and set B has nodes starting with node len(aseq).
+    The number of nodes in set B is random.
+
+    Parameters
+    ----------
+    aseq : list
+       Degree sequence for node set A.
+    p :  float
+       Probability that a new bottom node is added.
+    create_using : NetworkX graph instance, optional
+       Return graph of this type.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    References
+    ----------
+    .. [1] Guillaume, J.L. and Latapy, M.,
+       Bipartite graphs as models of complex networks.
+       Physica A: Statistical Mechanics and its Applications,
+       2006, 371(2), pp.795-813.
+    .. [2] Jean-Loup Guillaume and Matthieu Latapy,
+       Bipartite structure of all complex networks,
+       Inf. Process. Lett. 90, 2004, pg. 215-221
+       https://doi.org/10.1016/j.ipl.2004.03.007
+
+    Notes
+    -----
+    The nodes are assigned the attribute 'bipartite' with the value 0 or 1
+    to indicate which bipartite set the node belongs to.
+
+    This function is not imported in the main namespace.
+    To use it use nx.bipartite.preferential_attachment_graph
+    """
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    if p > 1:
+        raise nx.NetworkXError(f"probability {p} > 1")
+
+    naseq = len(aseq)
+    G = _add_nodes_with_bipartite_label(G, naseq, 0)
+    vv = [[v] * aseq[v] for v in range(naseq)]
+    while vv:
+        while vv[0]:
+            source = vv[0][0]
+            vv[0].remove(source)
+            if seed.random() < p or len(G) == naseq:
+                target = len(G)
+                G.add_node(target, bipartite=1)
+                G.add_edge(source, target)
+            else:
+                bb = [[b] * G.degree(b) for b in range(naseq, len(G))]
+                # flatten the list of lists into a list.
+                bbstubs = reduce(lambda x, y: x + y, bb)
+                # choose preferentially a bottom node.
+                target = seed.choice(bbstubs)
+                G.add_node(target, bipartite=1)
+                G.add_edge(source, target)
+        vv.remove(vv[0])
+    G.name = "bipartite_preferential_attachment_model"
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_graph(n, m, p, seed=None, directed=False):
+    """Returns a bipartite random graph.
+
+    This is a bipartite version of the binomial (Erdős-Rényi) graph.
+    The graph is composed of two partitions. Set A has nodes 0 to
+    (n - 1) and set B has nodes n to (n + m - 1).
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the first bipartite set.
+    m : int
+        The number of nodes in the second bipartite set.
+    p : float
+        Probability for edge creation.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : bool, optional (default=False)
+        If True return a directed graph
+
+    Notes
+    -----
+    The bipartite random graph algorithm chooses each of the n*m (undirected)
+    or 2*nm (directed) possible edges with probability p.
+
+    This algorithm is $O(n+m)$ where $m$ is the expected number of edges.
+
+    The nodes are assigned the attribute 'bipartite' with the value 0 or 1
+    to indicate which bipartite set the node belongs to.
+
+    This function is not imported in the main namespace.
+    To use it use nx.bipartite.random_graph
+
+    See Also
+    --------
+    gnp_random_graph, configuration_model
+
+    References
+    ----------
+    .. [1] Vladimir Batagelj and Ulrik Brandes,
+       "Efficient generation of large random networks",
+       Phys. Rev. E, 71, 036113, 2005.
+    """
+    G = nx.Graph()
+    G = _add_nodes_with_bipartite_label(G, n, m)
+    if directed:
+        G = nx.DiGraph(G)
+    G.name = f"fast_gnp_random_graph({n},{m},{p})"
+
+    if p <= 0:
+        return G
+    if p >= 1:
+        return nx.complete_bipartite_graph(n, m)
+
+    lp = math.log(1.0 - p)
+
+    v = 0
+    w = -1
+    while v < n:
+        lr = math.log(1.0 - seed.random())
+        w = w + 1 + int(lr / lp)
+        while w >= m and v < n:
+            w = w - m
+            v = v + 1
+        if v < n:
+            G.add_edge(v, n + w)
+
+    if directed:
+        # use the same algorithm to
+        # add edges from the "m" to "n" set
+        v = 0
+        w = -1
+        while v < n:
+            lr = math.log(1.0 - seed.random())
+            w = w + 1 + int(lr / lp)
+            while w >= m and v < n:
+                w = w - m
+                v = v + 1
+            if v < n:
+                G.add_edge(n + w, v)
+
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gnmk_random_graph(n, m, k, seed=None, directed=False):
+    """Returns a random bipartite graph G_{n,m,k}.
+
+    Produces a bipartite graph chosen randomly out of the set of all graphs
+    with n top nodes, m bottom nodes, and k edges.
+    The graph is composed of two sets of nodes.
+    Set A has nodes 0 to (n - 1) and set B has nodes n to (n + m - 1).
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the first bipartite set.
+    m : int
+        The number of nodes in the second bipartite set.
+    k : int
+        The number of edges
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : bool, optional (default=False)
+        If True return a directed graph
+
+    Examples
+    --------
+    >>> G = nx.bipartite.gnmk_random_graph(10, 20, 50)
+
+    See Also
+    --------
+    gnm_random_graph
+
+    Notes
+    -----
+    If k > m * n then a complete bipartite graph is returned.
+
+    This graph is a bipartite version of the `G_{nm}` random graph model.
+
+    The nodes are assigned the attribute 'bipartite' with the value 0 or 1
+    to indicate which bipartite set the node belongs to.
+
+    This function is not imported in the main namespace.
+    To use it use nx.bipartite.gnmk_random_graph
+    """
+    G = nx.Graph()
+    G = _add_nodes_with_bipartite_label(G, n, m)
+    if directed:
+        G = nx.DiGraph(G)
+    G.name = f"bipartite_gnm_random_graph({n},{m},{k})"
+    if n == 1 or m == 1:
+        return G
+    max_edges = n * m  # max_edges for bipartite networks
+    if k >= max_edges:  # Maybe we should raise an exception here
+        return nx.complete_bipartite_graph(n, m, create_using=G)
+
+    top = [n for n, d in G.nodes(data=True) if d["bipartite"] == 0]
+    bottom = list(set(G) - set(top))
+    edge_count = 0
+    while edge_count < k:
+        # generate random edge,u,v
+        u = seed.choice(top)
+        v = seed.choice(bottom)
+        if v in G[u]:
+            continue
+        else:
+            G.add_edge(u, v)
+            edge_count += 1
+    return G
+
+
+def _add_nodes_with_bipartite_label(G, lena, lenb):
+    G.add_nodes_from(range(lena + lenb))
+    b = dict(zip(range(lena), [0] * lena))
+    b.update(dict(zip(range(lena, lena + lenb), [1] * lenb)))
+    nx.set_node_attributes(G, b, "bipartite")
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/boundary.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/boundary.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba05d803037d8812bfff83df5382e8ea942711b2
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/boundary.py
@@ -0,0 +1,168 @@
+"""Routines to find the boundary of a set of nodes.
+
+An edge boundary is a set of edges, each of which has exactly one
+endpoint in a given set of nodes (or, in the case of directed graphs,
+the set of edges whose source node is in the set).
+
+A node boundary of a set *S* of nodes is the set of (out-)neighbors of
+nodes in *S* that are outside *S*.
+
+"""
+
+from itertools import chain
+
+import networkx as nx
+
+__all__ = ["edge_boundary", "node_boundary"]
+
+
+@nx._dispatchable(edge_attrs={"data": "default"}, preserve_edge_attrs="data")
+def edge_boundary(G, nbunch1, nbunch2=None, data=False, keys=False, default=None):
+    """Returns the edge boundary of `nbunch1`.
+
+    The *edge boundary* of a set *S* with respect to a set *T* is the
+    set of edges (*u*, *v*) such that *u* is in *S* and *v* is in *T*.
+    If *T* is not specified, it is assumed to be the set of all nodes
+    not in *S*.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    nbunch1 : iterable
+        Iterable of nodes in the graph representing the set of nodes
+        whose edge boundary will be returned. (This is the set *S* from
+        the definition above.)
+
+    nbunch2 : iterable
+        Iterable of nodes representing the target (or "exterior") set of
+        nodes. (This is the set *T* from the definition above.) If not
+        specified, this is assumed to be the set of all nodes in `G`
+        not in `nbunch1`.
+
+    keys : bool
+        This parameter has the same meaning as in
+        :meth:`MultiGraph.edges`.
+
+    data : bool or object
+        This parameter has the same meaning as in
+        :meth:`MultiGraph.edges`.
+
+    default : object
+        This parameter has the same meaning as in
+        :meth:`MultiGraph.edges`.
+
+    Returns
+    -------
+    iterator
+        An iterator over the edges in the boundary of `nbunch1` with
+        respect to `nbunch2`. If `keys`, `data`, or `default`
+        are specified and `G` is a multigraph, then edges are returned
+        with keys and/or data, as in :meth:`MultiGraph.edges`.
+
+    Examples
+    --------
+    >>> G = nx.wheel_graph(6)
+
+    When nbunch2=None:
+
+    >>> list(nx.edge_boundary(G, (1, 3)))
+    [(1, 0), (1, 2), (1, 5), (3, 0), (3, 2), (3, 4)]
+
+    When nbunch2 is given:
+
+    >>> list(nx.edge_boundary(G, (1, 3), (2, 0)))
+    [(1, 0), (1, 2), (3, 0), (3, 2)]
+
+    Notes
+    -----
+    Any element of `nbunch` that is not in the graph `G` will be
+    ignored.
+
+    `nbunch1` and `nbunch2` are usually meant to be disjoint, but in
+    the interest of speed and generality, that is not required here.
+
+    """
+    nset1 = {n for n in nbunch1 if n in G}
+    # Here we create an iterator over edges incident to nodes in the set
+    # `nset1`. The `Graph.edges()` method does not provide a guarantee
+    # on the orientation of the edges, so our algorithm below must
+    # handle the case in which exactly one orientation, either (u, v) or
+    # (v, u), appears in this iterable.
+    if G.is_multigraph():
+        edges = G.edges(nset1, data=data, keys=keys, default=default)
+    else:
+        edges = G.edges(nset1, data=data, default=default)
+    # If `nbunch2` is not provided, then it is assumed to be the set
+    # complement of `nbunch1`. For the sake of efficiency, this is
+    # implemented by using the `not in` operator, instead of by creating
+    # an additional set and using the `in` operator.
+    if nbunch2 is None:
+        return (e for e in edges if (e[0] in nset1) ^ (e[1] in nset1))
+    nset2 = set(nbunch2)
+    return (
+        e
+        for e in edges
+        if (e[0] in nset1 and e[1] in nset2) or (e[1] in nset1 and e[0] in nset2)
+    )
+
+
+@nx._dispatchable
+def node_boundary(G, nbunch1, nbunch2=None):
+    """Returns the node boundary of `nbunch1`.
+
+    The *node boundary* of a set *S* with respect to a set *T* is the
+    set of nodes *v* in *T* such that for some *u* in *S*, there is an
+    edge joining *u* to *v*. If *T* is not specified, it is assumed to
+    be the set of all nodes not in *S*.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    nbunch1 : iterable
+        Iterable of nodes in the graph representing the set of nodes
+        whose node boundary will be returned. (This is the set *S* from
+        the definition above.)
+
+    nbunch2 : iterable
+        Iterable of nodes representing the target (or "exterior") set of
+        nodes. (This is the set *T* from the definition above.) If not
+        specified, this is assumed to be the set of all nodes in `G`
+        not in `nbunch1`.
+
+    Returns
+    -------
+    set
+        The node boundary of `nbunch1` with respect to `nbunch2`.
+
+    Examples
+    --------
+    >>> G = nx.wheel_graph(6)
+
+    When nbunch2=None:
+
+    >>> list(nx.node_boundary(G, (3, 4)))
+    [0, 2, 5]
+
+    When nbunch2 is given:
+
+    >>> list(nx.node_boundary(G, (3, 4), (0, 1, 5)))
+    [0, 5]
+
+    Notes
+    -----
+    Any element of `nbunch` that is not in the graph `G` will be
+    ignored.
+
+    `nbunch1` and `nbunch2` are usually meant to be disjoint, but in
+    the interest of speed and generality, that is not required here.
+
+    """
+    nset1 = {n for n in nbunch1 if n in G}
+    bdy = set(chain.from_iterable(G[v] for v in nset1)) - nset1
+    # If `nbunch2` is not specified, it is assumed to be the set
+    # complement of `nbunch1`.
+    if nbunch2 is not None:
+        bdy &= set(nbunch2)
+    return bdy
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bridges.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bridges.py
new file mode 100644
index 0000000000000000000000000000000000000000..eaa6fd3bd7ef881abf93682315b76dc3b11e40ce
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/bridges.py
@@ -0,0 +1,205 @@
+"""Bridge-finding algorithms."""
+
+from itertools import chain
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["bridges", "has_bridges", "local_bridges"]
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def bridges(G, root=None):
+    """Generate all bridges in a graph.
+
+    A *bridge* in a graph is an edge whose removal causes the number of
+    connected components of the graph to increase.  Equivalently, a bridge is an
+    edge that does not belong to any cycle. Bridges are also known as cut-edges,
+    isthmuses, or cut arcs.
+
+    Parameters
+    ----------
+    G : undirected graph
+
+    root : node (optional)
+       A node in the graph `G`. If specified, only the bridges in the
+       connected component containing this node will be returned.
+
+    Yields
+    ------
+    e : edge
+       An edge in the graph whose removal disconnects the graph (or
+       causes the number of connected components to increase).
+
+    Raises
+    ------
+    NodeNotFound
+       If `root` is not in the graph `G`.
+
+    NetworkXNotImplemented
+        If `G` is a directed graph.
+
+    Examples
+    --------
+    The barbell graph with parameter zero has a single bridge:
+
+    >>> G = nx.barbell_graph(10, 0)
+    >>> list(nx.bridges(G))
+    [(9, 10)]
+
+    Notes
+    -----
+    This is an implementation of the algorithm described in [1]_.  An edge is a
+    bridge if and only if it is not contained in any chain. Chains are found
+    using the :func:`networkx.chain_decomposition` function.
+
+    The algorithm described in [1]_ requires a simple graph. If the provided
+    graph is a multigraph, we convert it to a simple graph and verify that any
+    bridges discovered by the chain decomposition algorithm are not multi-edges.
+
+    Ignoring polylogarithmic factors, the worst-case time complexity is the
+    same as the :func:`networkx.chain_decomposition` function,
+    $O(m + n)$, where $n$ is the number of nodes in the graph and $m$ is
+    the number of edges.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Bridge_%28graph_theory%29#Bridge-Finding_with_Chain_Decompositions
+    """
+    multigraph = G.is_multigraph()
+    H = nx.Graph(G) if multigraph else G
+    chains = nx.chain_decomposition(H, root=root)
+    chain_edges = set(chain.from_iterable(chains))
+    if root is not None:
+        H = H.subgraph(nx.node_connected_component(H, root)).copy()
+    for u, v in H.edges():
+        if (u, v) not in chain_edges and (v, u) not in chain_edges:
+            if multigraph and len(G[u][v]) > 1:
+                continue
+            yield u, v
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def has_bridges(G, root=None):
+    """Decide whether a graph has any bridges.
+
+    A *bridge* in a graph is an edge whose removal causes the number of
+    connected components of the graph to increase.
+
+    Parameters
+    ----------
+    G : undirected graph
+
+    root : node (optional)
+       A node in the graph `G`. If specified, only the bridges in the
+       connected component containing this node will be considered.
+
+    Returns
+    -------
+    bool
+       Whether the graph (or the connected component containing `root`)
+       has any bridges.
+
+    Raises
+    ------
+    NodeNotFound
+       If `root` is not in the graph `G`.
+
+    NetworkXNotImplemented
+        If `G` is a directed graph.
+
+    Examples
+    --------
+    The barbell graph with parameter zero has a single bridge::
+
+        >>> G = nx.barbell_graph(10, 0)
+        >>> nx.has_bridges(G)
+        True
+
+    On the other hand, the cycle graph has no bridges::
+
+        >>> G = nx.cycle_graph(5)
+        >>> nx.has_bridges(G)
+        False
+
+    Notes
+    -----
+    This implementation uses the :func:`networkx.bridges` function, so
+    it shares its worst-case time complexity, $O(m + n)$, ignoring
+    polylogarithmic factors, where $n$ is the number of nodes in the
+    graph and $m$ is the number of edges.
+
+    """
+    try:
+        next(bridges(G, root=root))
+    except StopIteration:
+        return False
+    else:
+        return True
+
+
+@not_implemented_for("multigraph")
+@not_implemented_for("directed")
+@nx._dispatchable(edge_attrs="weight")
+def local_bridges(G, with_span=True, weight=None):
+    """Iterate over local bridges of `G` optionally computing the span
+
+    A *local bridge* is an edge whose endpoints have no common neighbors.
+    That is, the edge is not part of a triangle in the graph.
+
+    The *span* of a *local bridge* is the shortest path length between
+    the endpoints if the local bridge is removed.
+
+    Parameters
+    ----------
+    G : undirected graph
+
+    with_span : bool
+        If True, yield a 3-tuple `(u, v, span)`
+
+    weight : function, string or None (default: None)
+        If function, used to compute edge weights for the span.
+        If string, the edge data attribute used in calculating span.
+        If None, all edges have weight 1.
+
+    Yields
+    ------
+    e : edge
+        The local bridges as an edge 2-tuple of nodes `(u, v)` or
+        as a 3-tuple `(u, v, span)` when `with_span is True`.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is a directed graph or multigraph.
+
+    Examples
+    --------
+    A cycle graph has every edge a local bridge with span N-1.
+
+       >>> G = nx.cycle_graph(9)
+       >>> (0, 8, 8) in set(nx.local_bridges(G))
+       True
+    """
+    if with_span is not True:
+        for u, v in G.edges:
+            if not (set(G[u]) & set(G[v])):
+                yield u, v
+    else:
+        wt = nx.weighted._weight_function(G, weight)
+        for u, v in G.edges:
+            if not (set(G[u]) & set(G[v])):
+                enodes = {u, v}
+
+                def hide_edge(n, nbr, d):
+                    if n not in enodes or nbr not in enodes:
+                        return wt(n, nbr, d)
+                    return None
+
+                try:
+                    span = nx.shortest_path_length(G, u, v, weight=hide_edge)
+                    yield u, v, span
+                except nx.NetworkXNoPath:
+                    yield u, v, float("inf")
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/broadcasting.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/broadcasting.py
new file mode 100644
index 0000000000000000000000000000000000000000..db8553b42c2e12ef7223ba9f5f6e5a8bcc6e2d0a
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/broadcasting.py
@@ -0,0 +1,155 @@
+"""Routines to calculate the broadcast time of certain graphs.
+
+Broadcasting is an information dissemination problem in which a node in a graph,
+called the originator, must distribute a message to all other nodes by placing
+a series of calls along the edges of the graph. Once informed, other nodes aid
+the originator in distributing the message.
+
+The broadcasting must be completed as quickly as possible subject to the
+following constraints:
+- Each call requires one unit of time.
+- A node can only participate in one call per unit of time.
+- Each call only involves two adjacent nodes: a sender and a receiver.
+"""
+
+import networkx as nx
+from networkx import NetworkXError
+from networkx.utils import not_implemented_for
+
+__all__ = [
+    "tree_broadcast_center",
+    "tree_broadcast_time",
+]
+
+
+def _get_max_broadcast_value(G, U, v, values):
+    adj = sorted(set(G.neighbors(v)) & U, key=values.get, reverse=True)
+    return max(values[u] + i for i, u in enumerate(adj, start=1))
+
+
+def _get_broadcast_centers(G, v, values, target):
+    adj = sorted(G.neighbors(v), key=values.get, reverse=True)
+    j = next(i for i, u in enumerate(adj, start=1) if values[u] + i == target)
+    return set([v] + adj[:j])
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def tree_broadcast_center(G):
+    """Return the Broadcast Center of the tree `G`.
+
+    The broadcast center of a graph G denotes the set of nodes having
+    minimum broadcast time [1]_. This is a linear algorithm for determining
+    the broadcast center of a tree with ``N`` nodes, as a by-product it also
+    determines the broadcast time from the broadcast center.
+
+    Parameters
+    ----------
+    G : undirected graph
+        The graph should be an undirected tree
+
+    Returns
+    -------
+    BC : (int, set) tuple
+        minimum broadcast number of the tree, set of broadcast centers
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+
+    References
+    ----------
+    .. [1] Slater, P.J., Cockayne, E.J., Hedetniemi, S.T,
+       Information dissemination in trees. SIAM J.Comput. 10(4), 692–701 (1981)
+    """
+    # Assert that the graph G is a tree
+    if not nx.is_tree(G):
+        NetworkXError("Input graph is not a tree")
+    # step 0
+    if G.number_of_nodes() == 2:
+        return 1, set(G.nodes())
+    if G.number_of_nodes() == 1:
+        return 0, set(G.nodes())
+
+    # step 1
+    U = {node for node, deg in G.degree if deg == 1}
+    values = dict.fromkeys(U, 0)
+    T = G.copy()
+    T.remove_nodes_from(U)
+
+    # step 2
+    W = {node for node, deg in T.degree if deg == 1}
+    values.update((w, G.degree[w] - 1) for w in W)
+
+    # step 3
+    while T.number_of_nodes() >= 2:
+        # step 4
+        w = min(W, key=lambda n: values[n])
+        v = next(T.neighbors(w))
+
+        # step 5
+        U.add(w)
+        W.remove(w)
+        T.remove_node(w)
+
+        # step 6
+        if T.degree(v) == 1:
+            # update t(v)
+            values.update({v: _get_max_broadcast_value(G, U, v, values)})
+            W.add(v)
+
+    # step 7
+    v = nx.utils.arbitrary_element(T)
+    b_T = _get_max_broadcast_value(G, U, v, values)
+    return b_T, _get_broadcast_centers(G, v, values, b_T)
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def tree_broadcast_time(G, node=None):
+    """Return the Broadcast Time of the tree `G`.
+
+    The minimum broadcast time of a node is defined as the minimum amount
+    of time required to complete broadcasting starting from the
+    originator. The broadcast time of a graph is the maximum over
+    all nodes of the minimum broadcast time from that node [1]_.
+    This function returns the minimum broadcast time of `node`.
+    If `node` is None the broadcast time for the graph is returned.
+
+    Parameters
+    ----------
+    G : undirected graph
+        The graph should be an undirected tree
+    node: int, optional
+        index of starting node. If `None`, the algorithm returns the broadcast
+        time of the tree.
+
+    Returns
+    -------
+    BT : int
+        Broadcast Time of a node in a tree
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+
+    References
+    ----------
+    .. [1] Harutyunyan, H. A. and Li, Z.
+        "A Simple Construction of Broadcast Graphs."
+        In Computing and Combinatorics. COCOON 2019
+        (Ed. D. Z. Du and C. Tian.) Springer, pp. 240-253, 2019.
+    """
+    b_T, b_C = tree_broadcast_center(G)
+    if node is not None:
+        return b_T + min(nx.shortest_path_length(G, node, u) for u in b_C)
+    dist_from_center = dict.fromkeys(G, len(G))
+    for u in b_C:
+        for v, dist in nx.shortest_path_length(G, u).items():
+            if dist < dist_from_center[v]:
+                dist_from_center[v] = dist
+    return b_T + max(dist_from_center.values())
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/chains.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/chains.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae342d9c8669acd832a3bdb4fe8eecf3e300464f
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/chains.py
@@ -0,0 +1,172 @@
+"""Functions for finding chains in a graph."""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["chain_decomposition"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def chain_decomposition(G, root=None):
+    """Returns the chain decomposition of a graph.
+
+    The *chain decomposition* of a graph with respect a depth-first
+    search tree is a set of cycles or paths derived from the set of
+    fundamental cycles of the tree in the following manner. Consider
+    each fundamental cycle with respect to the given tree, represented
+    as a list of edges beginning with the nontree edge oriented away
+    from the root of the tree. For each fundamental cycle, if it
+    overlaps with any previous fundamental cycle, just take the initial
+    non-overlapping segment, which is a path instead of a cycle. Each
+    cycle or path is called a *chain*. For more information, see [1]_.
+
+    Parameters
+    ----------
+    G : undirected graph
+
+    root : node (optional)
+       A node in the graph `G`. If specified, only the chain
+       decomposition for the connected component containing this node
+       will be returned. This node indicates the root of the depth-first
+       search tree.
+
+    Yields
+    ------
+    chain : list
+       A list of edges representing a chain. There is no guarantee on
+       the orientation of the edges in each chain (for example, if a
+       chain includes the edge joining nodes 1 and 2, the chain may
+       include either (1, 2) or (2, 1)).
+
+    Raises
+    ------
+    NodeNotFound
+       If `root` is not in the graph `G`.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (1, 4), (3, 4), (3, 5), (4, 5)])
+    >>> list(nx.chain_decomposition(G))
+    [[(4, 5), (5, 3), (3, 4)]]
+
+    Notes
+    -----
+    The worst-case running time of this implementation is linear in the
+    number of nodes and number of edges [1]_.
+
+    References
+    ----------
+    .. [1] Jens M. Schmidt (2013). "A simple test on 2-vertex-
+       and 2-edge-connectivity." *Information Processing Letters*,
+       113, 241–244. Elsevier. <https://doi.org/10.1016/j.ipl.2013.01.016>
+
+    """
+
+    def _dfs_cycle_forest(G, root=None):
+        """Builds a directed graph composed of cycles from the given graph.
+
+        `G` is an undirected simple graph. `root` is a node in the graph
+        from which the depth-first search is started.
+
+        This function returns both the depth-first search cycle graph
+        (as a :class:`~networkx.DiGraph`) and the list of nodes in
+        depth-first preorder. The depth-first search cycle graph is a
+        directed graph whose edges are the edges of `G` oriented toward
+        the root if the edge is a tree edge and away from the root if
+        the edge is a non-tree edge. If `root` is not specified, this
+        performs a depth-first search on each connected component of `G`
+        and returns a directed forest instead.
+
+        If `root` is not in the graph, this raises :exc:`KeyError`.
+
+        """
+        # Create a directed graph from the depth-first search tree with
+        # root node `root` in which tree edges are directed toward the
+        # root and nontree edges are directed away from the root. For
+        # each node with an incident nontree edge, this creates a
+        # directed cycle starting with the nontree edge and returning to
+        # that node.
+        #
+        # The `parent` node attribute stores the parent of each node in
+        # the DFS tree. The `nontree` edge attribute indicates whether
+        # the edge is a tree edge or a nontree edge.
+        #
+        # We also store the order of the nodes found in the depth-first
+        # search in the `nodes` list.
+        H = nx.DiGraph()
+        nodes = []
+        for u, v, d in nx.dfs_labeled_edges(G, source=root):
+            if d == "forward":
+                # `dfs_labeled_edges()` yields (root, root, 'forward')
+                # if it is beginning the search on a new connected
+                # component.
+                if u == v:
+                    H.add_node(v, parent=None)
+                    nodes.append(v)
+                else:
+                    H.add_node(v, parent=u)
+                    H.add_edge(v, u, nontree=False)
+                    nodes.append(v)
+            # `dfs_labeled_edges` considers nontree edges in both
+            # orientations, so we need to not add the edge if it its
+            # other orientation has been added.
+            elif d == "nontree" and v not in H[u]:
+                H.add_edge(v, u, nontree=True)
+            else:
+                # Do nothing on 'reverse' edges; we only care about
+                # forward and nontree edges.
+                pass
+        return H, nodes
+
+    def _build_chain(G, u, v, visited):
+        """Generate the chain starting from the given nontree edge.
+
+        `G` is a DFS cycle graph as constructed by
+        :func:`_dfs_cycle_graph`. The edge (`u`, `v`) is a nontree edge
+        that begins a chain. `visited` is a set representing the nodes
+        in `G` that have already been visited.
+
+        This function yields the edges in an initial segment of the
+        fundamental cycle of `G` starting with the nontree edge (`u`,
+        `v`) that includes all the edges up until the first node that
+        appears in `visited`. The tree edges are given by the 'parent'
+        node attribute. The `visited` set is updated to add each node in
+        an edge yielded by this function.
+
+        """
+        while v not in visited:
+            yield u, v
+            visited.add(v)
+            u, v = v, G.nodes[v]["parent"]
+        yield u, v
+
+    # Check if the root is in the graph G. If not, raise NodeNotFound
+    if root is not None and root not in G:
+        raise nx.NodeNotFound(f"Root node {root} is not in graph")
+
+    # Create a directed version of H that has the DFS edges directed
+    # toward the root and the nontree edges directed away from the root
+    # (in each connected component).
+    H, nodes = _dfs_cycle_forest(G, root)
+
+    # Visit the nodes again in DFS order. For each node, and for each
+    # nontree edge leaving that node, compute the fundamental cycle for
+    # that nontree edge starting with that edge. If the fundamental
+    # cycle overlaps with any visited nodes, just take the prefix of the
+    # cycle up to the point of visited nodes.
+    #
+    # We repeat this process for each connected component (implicitly,
+    # since `nodes` already has a list of the nodes grouped by connected
+    # component).
+    visited = set()
+    for u in nodes:
+        visited.add(u)
+        # For each nontree edge going out of node u...
+        edges = ((u, v) for u, v, d in H.out_edges(u, data="nontree") if d)
+        for u, v in edges:
+            # Create the cycle or cycle prefix starting with the
+            # nontree edge.
+            chain = list(_build_chain(H, u, v, visited))
+            yield chain
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/chordal.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/chordal.py
new file mode 100644
index 0000000000000000000000000000000000000000..deb6ed0095b5ce2234183f7bac3d06cc98310ffb
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/chordal.py
@@ -0,0 +1,443 @@
+"""
+Algorithms for chordal graphs.
+
+A graph is chordal if every cycle of length at least 4 has a chord
+(an edge joining two nodes not adjacent in the cycle).
+https://en.wikipedia.org/wiki/Chordal_graph
+"""
+
+import sys
+
+import networkx as nx
+from networkx.algorithms.components import connected_components
+from networkx.utils import arbitrary_element, not_implemented_for
+
+__all__ = [
+    "is_chordal",
+    "find_induced_nodes",
+    "chordal_graph_cliques",
+    "chordal_graph_treewidth",
+    "NetworkXTreewidthBoundExceeded",
+    "complete_to_chordal_graph",
+]
+
+
+class NetworkXTreewidthBoundExceeded(nx.NetworkXException):
+    """Exception raised when a treewidth bound has been provided and it has
+    been exceeded"""
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def is_chordal(G):
+    """Checks whether G is a chordal graph.
+
+    A graph is chordal if every cycle of length at least 4 has a chord
+    (an edge joining two nodes not adjacent in the cycle).
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph.
+
+    Returns
+    -------
+    chordal : bool
+      True if G is a chordal graph and False otherwise.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
+
+    Examples
+    --------
+    >>> e = [
+    ...     (1, 2),
+    ...     (1, 3),
+    ...     (2, 3),
+    ...     (2, 4),
+    ...     (3, 4),
+    ...     (3, 5),
+    ...     (3, 6),
+    ...     (4, 5),
+    ...     (4, 6),
+    ...     (5, 6),
+    ... ]
+    >>> G = nx.Graph(e)
+    >>> nx.is_chordal(G)
+    True
+
+    Notes
+    -----
+    The routine tries to go through every node following maximum cardinality
+    search. It returns False when it finds that the separator for any node
+    is not a clique.  Based on the algorithms in [1]_.
+
+    Self loops are ignored.
+
+    References
+    ----------
+    .. [1] R. E. Tarjan and M. Yannakakis, Simple linear-time algorithms
+       to test chordality of graphs, test acyclicity of hypergraphs, and
+       selectively reduce acyclic hypergraphs, SIAM J. Comput., 13 (1984),
+       pp. 566–579.
+    """
+    if len(G.nodes) <= 3:
+        return True
+    return len(_find_chordality_breaker(G)) == 0
+
+
+@nx._dispatchable
+def find_induced_nodes(G, s, t, treewidth_bound=sys.maxsize):
+    """Returns the set of induced nodes in the path from s to t.
+
+    Parameters
+    ----------
+    G : graph
+      A chordal NetworkX graph
+    s : node
+        Source node to look for induced nodes
+    t : node
+        Destination node to look for induced nodes
+    treewidth_bound: float
+        Maximum treewidth acceptable for the graph H. The search
+        for induced nodes will end as soon as the treewidth_bound is exceeded.
+
+    Returns
+    -------
+    induced_nodes : Set of nodes
+        The set of induced nodes in the path from s to t in G
+
+    Raises
+    ------
+    NetworkXError
+        The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
+        If the input graph is an instance of one of these classes, a
+        :exc:`NetworkXError` is raised.
+        The algorithm can only be applied to chordal graphs. If the input
+        graph is found to be non-chordal, a :exc:`NetworkXError` is raised.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G = nx.generators.classic.path_graph(10)
+    >>> induced_nodes = nx.find_induced_nodes(G, 1, 9, 2)
+    >>> sorted(induced_nodes)
+    [1, 2, 3, 4, 5, 6, 7, 8, 9]
+
+    Notes
+    -----
+    G must be a chordal graph and (s,t) an edge that is not in G.
+
+    If a treewidth_bound is provided, the search for induced nodes will end
+    as soon as the treewidth_bound is exceeded.
+
+    The algorithm is inspired by Algorithm 4 in [1]_.
+    A formal definition of induced node can also be found on that reference.
+
+    Self Loops are ignored
+
+    References
+    ----------
+    .. [1] Learning Bounded Treewidth Bayesian Networks.
+       Gal Elidan, Stephen Gould; JMLR, 9(Dec):2699--2731, 2008.
+       http://jmlr.csail.mit.edu/papers/volume9/elidan08a/elidan08a.pdf
+    """
+    if not is_chordal(G):
+        raise nx.NetworkXError("Input graph is not chordal.")
+
+    H = nx.Graph(G)
+    H.add_edge(s, t)
+    induced_nodes = set()
+    triplet = _find_chordality_breaker(H, s, treewidth_bound)
+    while triplet:
+        (u, v, w) = triplet
+        induced_nodes.update(triplet)
+        for n in triplet:
+            if n != s:
+                H.add_edge(s, n)
+        triplet = _find_chordality_breaker(H, s, treewidth_bound)
+    if induced_nodes:
+        # Add t and the second node in the induced path from s to t.
+        induced_nodes.add(t)
+        for u in G[s]:
+            if len(induced_nodes & set(G[u])) == 2:
+                induced_nodes.add(u)
+                break
+    return induced_nodes
+
+
+@nx._dispatchable
+def chordal_graph_cliques(G):
+    """Returns all maximal cliques of a chordal graph.
+
+    The algorithm breaks the graph in connected components and performs a
+    maximum cardinality search in each component to get the cliques.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    Yields
+    ------
+    frozenset of nodes
+        Maximal cliques, each of which is a frozenset of
+        nodes in `G`. The order of cliques is arbitrary.
+
+    Raises
+    ------
+    NetworkXError
+        The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
+        The algorithm can only be applied to chordal graphs. If the input
+        graph is found to be non-chordal, a :exc:`NetworkXError` is raised.
+
+    Examples
+    --------
+    >>> e = [
+    ...     (1, 2),
+    ...     (1, 3),
+    ...     (2, 3),
+    ...     (2, 4),
+    ...     (3, 4),
+    ...     (3, 5),
+    ...     (3, 6),
+    ...     (4, 5),
+    ...     (4, 6),
+    ...     (5, 6),
+    ...     (7, 8),
+    ... ]
+    >>> G = nx.Graph(e)
+    >>> G.add_node(9)
+    >>> cliques = [c for c in chordal_graph_cliques(G)]
+    >>> cliques[0]
+    frozenset({1, 2, 3})
+    """
+    for C in (G.subgraph(c).copy() for c in connected_components(G)):
+        if C.number_of_nodes() == 1:
+            if nx.number_of_selfloops(C) > 0:
+                raise nx.NetworkXError("Input graph is not chordal.")
+            yield frozenset(C.nodes())
+        else:
+            unnumbered = set(C.nodes())
+            v = arbitrary_element(C)
+            unnumbered.remove(v)
+            numbered = {v}
+            clique_wanna_be = {v}
+            while unnumbered:
+                v = _max_cardinality_node(C, unnumbered, numbered)
+                unnumbered.remove(v)
+                numbered.add(v)
+                new_clique_wanna_be = set(C.neighbors(v)) & numbered
+                sg = C.subgraph(clique_wanna_be)
+                if _is_complete_graph(sg):
+                    new_clique_wanna_be.add(v)
+                    if not new_clique_wanna_be >= clique_wanna_be:
+                        yield frozenset(clique_wanna_be)
+                    clique_wanna_be = new_clique_wanna_be
+                else:
+                    raise nx.NetworkXError("Input graph is not chordal.")
+            yield frozenset(clique_wanna_be)
+
+
+@nx._dispatchable
+def chordal_graph_treewidth(G):
+    """Returns the treewidth of the chordal graph G.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    Returns
+    -------
+    treewidth : int
+        The size of the largest clique in the graph minus one.
+
+    Raises
+    ------
+    NetworkXError
+        The algorithm does not support DiGraph, MultiGraph and MultiDiGraph.
+        The algorithm can only be applied to chordal graphs. If the input
+        graph is found to be non-chordal, a :exc:`NetworkXError` is raised.
+
+    Examples
+    --------
+    >>> e = [
+    ...     (1, 2),
+    ...     (1, 3),
+    ...     (2, 3),
+    ...     (2, 4),
+    ...     (3, 4),
+    ...     (3, 5),
+    ...     (3, 6),
+    ...     (4, 5),
+    ...     (4, 6),
+    ...     (5, 6),
+    ...     (7, 8),
+    ... ]
+    >>> G = nx.Graph(e)
+    >>> G.add_node(9)
+    >>> nx.chordal_graph_treewidth(G)
+    3
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Tree_decomposition#Treewidth
+    """
+    if not is_chordal(G):
+        raise nx.NetworkXError("Input graph is not chordal.")
+
+    max_clique = -1
+    for clique in nx.chordal_graph_cliques(G):
+        max_clique = max(max_clique, len(clique))
+    return max_clique - 1
+
+
+def _is_complete_graph(G):
+    """Returns True if G is a complete graph."""
+    if nx.number_of_selfloops(G) > 0:
+        raise nx.NetworkXError("Self loop found in _is_complete_graph()")
+    n = G.number_of_nodes()
+    if n < 2:
+        return True
+    e = G.number_of_edges()
+    max_edges = (n * (n - 1)) / 2
+    return e == max_edges
+
+
+def _find_missing_edge(G):
+    """Given a non-complete graph G, returns a missing edge."""
+    nodes = set(G)
+    for u in G:
+        missing = nodes - set(list(G[u].keys()) + [u])
+        if missing:
+            return (u, missing.pop())
+
+
+def _max_cardinality_node(G, choices, wanna_connect):
+    """Returns a the node in choices that has more connections in G
+    to nodes in wanna_connect.
+    """
+    max_number = -1
+    for x in choices:
+        number = len([y for y in G[x] if y in wanna_connect])
+        if number > max_number:
+            max_number = number
+            max_cardinality_node = x
+    return max_cardinality_node
+
+
+def _find_chordality_breaker(G, s=None, treewidth_bound=sys.maxsize):
+    """Given a graph G, starts a max cardinality search
+    (starting from s if s is given and from an arbitrary node otherwise)
+    trying to find a non-chordal cycle.
+
+    If it does find one, it returns (u,v,w) where u,v,w are the three
+    nodes that together with s are involved in the cycle.
+
+    It ignores any self loops.
+    """
+    if len(G) == 0:
+        raise nx.NetworkXPointlessConcept("Graph has no nodes.")
+    unnumbered = set(G)
+    if s is None:
+        s = arbitrary_element(G)
+    unnumbered.remove(s)
+    numbered = {s}
+    current_treewidth = -1
+    while unnumbered:  # and current_treewidth <= treewidth_bound:
+        v = _max_cardinality_node(G, unnumbered, numbered)
+        unnumbered.remove(v)
+        numbered.add(v)
+        clique_wanna_be = set(G[v]) & numbered
+        sg = G.subgraph(clique_wanna_be)
+        if _is_complete_graph(sg):
+            # The graph seems to be chordal by now. We update the treewidth
+            current_treewidth = max(current_treewidth, len(clique_wanna_be))
+            if current_treewidth > treewidth_bound:
+                raise nx.NetworkXTreewidthBoundExceeded(
+                    f"treewidth_bound exceeded: {current_treewidth}"
+                )
+        else:
+            # sg is not a clique,
+            # look for an edge that is not included in sg
+            (u, w) = _find_missing_edge(sg)
+            return (u, v, w)
+    return ()
+
+
+@not_implemented_for("directed")
+@nx._dispatchable(returns_graph=True)
+def complete_to_chordal_graph(G):
+    """Return a copy of G completed to a chordal graph
+
+    Adds edges to a copy of G to create a chordal graph. A graph G=(V,E) is
+    called chordal if for each cycle with length bigger than 3, there exist
+    two non-adjacent nodes connected by an edge (called a chord).
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    Returns
+    -------
+    H : NetworkX graph
+        The chordal enhancement of G
+    alpha : Dictionary
+            The elimination ordering of nodes of G
+
+    Notes
+    -----
+    There are different approaches to calculate the chordal
+    enhancement of a graph. The algorithm used here is called
+    MCS-M and gives at least minimal (local) triangulation of graph. Note
+    that this triangulation is not necessarily a global minimum.
+
+    https://en.wikipedia.org/wiki/Chordal_graph
+
+    References
+    ----------
+    .. [1] Berry, Anne & Blair, Jean & Heggernes, Pinar & Peyton, Barry. (2004)
+           Maximum Cardinality Search for Computing Minimal Triangulations of
+           Graphs.  Algorithmica. 39. 287-298. 10.1007/s00453-004-1084-3.
+
+    Examples
+    --------
+    >>> from networkx.algorithms.chordal import complete_to_chordal_graph
+    >>> G = nx.wheel_graph(10)
+    >>> H, alpha = complete_to_chordal_graph(G)
+    """
+    H = G.copy()
+    alpha = dict.fromkeys(H, 0)
+    if nx.is_chordal(H):
+        return H, alpha
+    chords = set()
+    weight = dict.fromkeys(H.nodes(), 0)
+    unnumbered_nodes = list(H.nodes())
+    for i in range(len(H.nodes()), 0, -1):
+        # get the node in unnumbered_nodes with the maximum weight
+        z = max(unnumbered_nodes, key=lambda node: weight[node])
+        unnumbered_nodes.remove(z)
+        alpha[z] = i
+        update_nodes = []
+        for y in unnumbered_nodes:
+            if G.has_edge(y, z):
+                update_nodes.append(y)
+            else:
+                # y_weight will be bigger than node weights between y and z
+                y_weight = weight[y]
+                lower_nodes = [
+                    node for node in unnumbered_nodes if weight[node] < y_weight
+                ]
+                if nx.has_path(H.subgraph(lower_nodes + [z, y]), y, z):
+                    update_nodes.append(y)
+                    chords.add((z, y))
+        # during calculation of paths the weights should not be updated
+        for node in update_nodes:
+            weight[node] += 1
+    H.add_edges_from(chords)
+    return H, alpha
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/clique.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/clique.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe961a9db1849089f111db02148bf9d7a3d2ba53
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/clique.py
@@ -0,0 +1,757 @@
+"""Functions for finding and manipulating cliques.
+
+Finding the largest clique in a graph is NP-complete problem, so most of
+these algorithms have an exponential running time; for more information,
+see the Wikipedia article on the clique problem [1]_.
+
+.. [1] clique problem:: https://en.wikipedia.org/wiki/Clique_problem
+
+"""
+
+from collections import Counter, defaultdict, deque
+from itertools import chain, combinations, islice
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = [
+    "find_cliques",
+    "find_cliques_recursive",
+    "make_max_clique_graph",
+    "make_clique_bipartite",
+    "node_clique_number",
+    "number_of_cliques",
+    "enumerate_all_cliques",
+    "max_weight_clique",
+]
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def enumerate_all_cliques(G):
+    """Returns all cliques in an undirected graph.
+
+    This function returns an iterator over cliques, each of which is a
+    list of nodes. The iteration is ordered by cardinality of the
+    cliques: first all cliques of size one, then all cliques of size
+    two, etc.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    Returns
+    -------
+    iterator
+        An iterator over cliques, each of which is a list of nodes in
+        `G`. The cliques are ordered according to size.
+
+    Notes
+    -----
+    To obtain a list of all cliques, use
+    `list(enumerate_all_cliques(G))`. However, be aware that in the
+    worst-case, the length of this list can be exponential in the number
+    of nodes in the graph (for example, when the graph is the complete
+    graph). This function avoids storing all cliques in memory by only
+    keeping current candidate node lists in memory during its search.
+
+    The implementation is adapted from the algorithm by Zhang, et
+    al. (2005) [1]_ to output all cliques discovered.
+
+    This algorithm ignores self-loops and parallel edges, since cliques
+    are not conventionally defined with such edges.
+
+    References
+    ----------
+    .. [1] Yun Zhang, Abu-Khzam, F.N., Baldwin, N.E., Chesler, E.J.,
+           Langston, M.A., Samatova, N.F.,
+           "Genome-Scale Computational Approaches to Memory-Intensive
+           Applications in Systems Biology".
+           *Supercomputing*, 2005. Proceedings of the ACM/IEEE SC 2005
+           Conference, pp. 12, 12--18 Nov. 2005.
+           <https://doi.org/10.1109/SC.2005.29>.
+
+    """
+    index = {}
+    nbrs = {}
+    for u in G:
+        index[u] = len(index)
+        # Neighbors of u that appear after u in the iteration order of G.
+        nbrs[u] = {v for v in G[u] if v not in index}
+
+    queue = deque(([u], sorted(nbrs[u], key=index.__getitem__)) for u in G)
+    # Loop invariants:
+    # 1. len(base) is nondecreasing.
+    # 2. (base + cnbrs) is sorted with respect to the iteration order of G.
+    # 3. cnbrs is a set of common neighbors of nodes in base.
+    while queue:
+        base, cnbrs = map(list, queue.popleft())
+        yield base
+        for i, u in enumerate(cnbrs):
+            # Use generators to reduce memory consumption.
+            queue.append(
+                (
+                    chain(base, [u]),
+                    filter(nbrs[u].__contains__, islice(cnbrs, i + 1, None)),
+                )
+            )
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def find_cliques(G, nodes=None):
+    """Returns all maximal cliques in an undirected graph.
+
+    For each node *n*, a *maximal clique for n* is a largest complete
+    subgraph containing *n*. The largest maximal clique is sometimes
+    called the *maximum clique*.
+
+    This function returns an iterator over cliques, each of which is a
+    list of nodes. It is an iterative implementation, so should not
+    suffer from recursion depth issues.
+
+    This function accepts a list of `nodes` and only the maximal cliques
+    containing all of these `nodes` are returned. It can considerably speed up
+    the running time if some specific cliques are desired.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    nodes : list, optional (default=None)
+        If provided, only yield *maximal cliques* containing all nodes in `nodes`.
+        If `nodes` isn't a clique itself, a ValueError is raised.
+
+    Returns
+    -------
+    iterator
+        An iterator over maximal cliques, each of which is a list of
+        nodes in `G`. If `nodes` is provided, only the maximal cliques
+        containing all the nodes in `nodes` are returned. The order of
+        cliques is arbitrary.
+
+    Raises
+    ------
+    ValueError
+        If `nodes` is not a clique.
+
+    Examples
+    --------
+    >>> from pprint import pprint  # For nice dict formatting
+    >>> G = nx.karate_club_graph()
+    >>> sum(1 for c in nx.find_cliques(G))  # The number of maximal cliques in G
+    36
+    >>> max(nx.find_cliques(G), key=len)  # The largest maximal clique in G
+    [0, 1, 2, 3, 13]
+
+    The size of the largest maximal clique is known as the *clique number* of
+    the graph, which can be found directly with:
+
+    >>> max(len(c) for c in nx.find_cliques(G))
+    5
+
+    One can also compute the number of maximal cliques in `G` that contain a given
+    node. The following produces a dictionary keyed by node whose
+    values are the number of maximal cliques in `G` that contain the node:
+
+    >>> from collections import Counter
+    >>> from itertools import chain
+    >>> counts = Counter(chain.from_iterable(nx.find_cliques(G)))
+    >>> pprint(dict(counts))
+    {0: 13,
+     1: 6,
+     2: 7,
+     3: 3,
+     4: 2,
+     5: 3,
+     6: 3,
+     7: 1,
+     8: 3,
+     9: 2,
+     10: 2,
+     11: 1,
+     12: 1,
+     13: 2,
+     14: 1,
+     15: 1,
+     16: 1,
+     17: 1,
+     18: 1,
+     19: 2,
+     20: 1,
+     21: 1,
+     22: 1,
+     23: 3,
+     24: 2,
+     25: 2,
+     26: 1,
+     27: 3,
+     28: 2,
+     29: 2,
+     30: 2,
+     31: 4,
+     32: 9,
+     33: 14}
+
+    Or, similarly, the maximal cliques in `G` that contain a given node.
+    For example, the 4 maximal cliques that contain node 31:
+
+    >>> [c for c in nx.find_cliques(G) if 31 in c]
+    [[0, 31], [33, 32, 31], [33, 28, 31], [24, 25, 31]]
+
+    See Also
+    --------
+    find_cliques_recursive
+        A recursive version of the same algorithm.
+
+    Notes
+    -----
+    To obtain a list of all maximal cliques, use
+    `list(find_cliques(G))`. However, be aware that in the worst-case,
+    the length of this list can be exponential in the number of nodes in
+    the graph. This function avoids storing all cliques in memory by
+    only keeping current candidate node lists in memory during its search.
+
+    This implementation is based on the algorithm published by Bron and
+    Kerbosch (1973) [1]_, as adapted by Tomita, Tanaka and Takahashi
+    (2006) [2]_ and discussed in Cazals and Karande (2008) [3]_. It
+    essentially unrolls the recursion used in the references to avoid
+    issues of recursion stack depth (for a recursive implementation, see
+    :func:`find_cliques_recursive`).
+
+    This algorithm ignores self-loops and parallel edges, since cliques
+    are not conventionally defined with such edges.
+
+    References
+    ----------
+    .. [1] Bron, C. and Kerbosch, J.
+       "Algorithm 457: finding all cliques of an undirected graph".
+       *Communications of the ACM* 16, 9 (Sep. 1973), 575--577.
+       <http://portal.acm.org/citation.cfm?doid=362342.362367>
+
+    .. [2] Etsuji Tomita, Akira Tanaka, Haruhisa Takahashi,
+       "The worst-case time complexity for generating all maximal
+       cliques and computational experiments",
+       *Theoretical Computer Science*, Volume 363, Issue 1,
+       Computing and Combinatorics,
+       10th Annual International Conference on
+       Computing and Combinatorics (COCOON 2004), 25 October 2006, Pages 28--42
+       <https://doi.org/10.1016/j.tcs.2006.06.015>
+
+    .. [3] F. Cazals, C. Karande,
+       "A note on the problem of reporting maximal cliques",
+       *Theoretical Computer Science*,
+       Volume 407, Issues 1--3, 6 November 2008, Pages 564--568,
+       <https://doi.org/10.1016/j.tcs.2008.05.010>
+
+    """
+    if len(G) == 0:
+        return
+
+    adj = {u: {v for v in G[u] if v != u} for u in G}
+
+    # Initialize Q with the given nodes and subg, cand with their nbrs
+    Q = nodes[:] if nodes is not None else []
+    cand = set(G)
+    for node in Q:
+        if node not in cand:
+            raise ValueError(f"The given `nodes` {nodes} do not form a clique")
+        cand &= adj[node]
+
+    if not cand:
+        yield Q[:]
+        return
+
+    subg = cand.copy()
+    stack = []
+    Q.append(None)
+
+    u = max(subg, key=lambda u: len(cand & adj[u]))
+    ext_u = cand - adj[u]
+
+    try:
+        while True:
+            if ext_u:
+                q = ext_u.pop()
+                cand.remove(q)
+                Q[-1] = q
+                adj_q = adj[q]
+                subg_q = subg & adj_q
+                if not subg_q:
+                    yield Q[:]
+                else:
+                    cand_q = cand & adj_q
+                    if cand_q:
+                        stack.append((subg, cand, ext_u))
+                        Q.append(None)
+                        subg = subg_q
+                        cand = cand_q
+                        u = max(subg, key=lambda u: len(cand & adj[u]))
+                        ext_u = cand - adj[u]
+            else:
+                Q.pop()
+                subg, cand, ext_u = stack.pop()
+    except IndexError:
+        pass
+
+
+# TODO Should this also be not implemented for directed graphs?
+@nx._dispatchable
+def find_cliques_recursive(G, nodes=None):
+    """Returns all maximal cliques in a graph.
+
+    For each node *v*, a *maximal clique for v* is a largest complete
+    subgraph containing *v*. The largest maximal clique is sometimes
+    called the *maximum clique*.
+
+    This function returns an iterator over cliques, each of which is a
+    list of nodes. It is a recursive implementation, so may suffer from
+    recursion depth issues, but is included for pedagogical reasons.
+    For a non-recursive implementation, see :func:`find_cliques`.
+
+    This function accepts a list of `nodes` and only the maximal cliques
+    containing all of these `nodes` are returned. It can considerably speed up
+    the running time if some specific cliques are desired.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    nodes : list, optional (default=None)
+        If provided, only yield *maximal cliques* containing all nodes in `nodes`.
+        If `nodes` isn't a clique itself, a ValueError is raised.
+
+    Returns
+    -------
+    iterator
+        An iterator over maximal cliques, each of which is a list of
+        nodes in `G`. If `nodes` is provided, only the maximal cliques
+        containing all the nodes in `nodes` are yielded. The order of
+        cliques is arbitrary.
+
+    Raises
+    ------
+    ValueError
+        If `nodes` is not a clique.
+
+    See Also
+    --------
+    find_cliques
+        An iterative version of the same algorithm. See docstring for examples.
+
+    Notes
+    -----
+    To obtain a list of all maximal cliques, use
+    `list(find_cliques_recursive(G))`. However, be aware that in the
+    worst-case, the length of this list can be exponential in the number
+    of nodes in the graph. This function avoids storing all cliques in memory
+    by only keeping current candidate node lists in memory during its search.
+
+    This implementation is based on the algorithm published by Bron and
+    Kerbosch (1973) [1]_, as adapted by Tomita, Tanaka and Takahashi
+    (2006) [2]_ and discussed in Cazals and Karande (2008) [3]_. For a
+    non-recursive implementation, see :func:`find_cliques`.
+
+    This algorithm ignores self-loops and parallel edges, since cliques
+    are not conventionally defined with such edges.
+
+    References
+    ----------
+    .. [1] Bron, C. and Kerbosch, J.
+       "Algorithm 457: finding all cliques of an undirected graph".
+       *Communications of the ACM* 16, 9 (Sep. 1973), 575--577.
+       <http://portal.acm.org/citation.cfm?doid=362342.362367>
+
+    .. [2] Etsuji Tomita, Akira Tanaka, Haruhisa Takahashi,
+       "The worst-case time complexity for generating all maximal
+       cliques and computational experiments",
+       *Theoretical Computer Science*, Volume 363, Issue 1,
+       Computing and Combinatorics,
+       10th Annual International Conference on
+       Computing and Combinatorics (COCOON 2004), 25 October 2006, Pages 28--42
+       <https://doi.org/10.1016/j.tcs.2006.06.015>
+
+    .. [3] F. Cazals, C. Karande,
+       "A note on the problem of reporting maximal cliques",
+       *Theoretical Computer Science*,
+       Volume 407, Issues 1--3, 6 November 2008, Pages 564--568,
+       <https://doi.org/10.1016/j.tcs.2008.05.010>
+
+    """
+    if len(G) == 0:
+        return iter([])
+
+    adj = {u: {v for v in G[u] if v != u} for u in G}
+
+    # Initialize Q with the given nodes and subg, cand with their nbrs
+    Q = nodes[:] if nodes is not None else []
+    cand_init = set(G)
+    for node in Q:
+        if node not in cand_init:
+            raise ValueError(f"The given `nodes` {nodes} do not form a clique")
+        cand_init &= adj[node]
+
+    if not cand_init:
+        return iter([Q])
+
+    subg_init = cand_init.copy()
+
+    def expand(subg, cand):
+        u = max(subg, key=lambda u: len(cand & adj[u]))
+        for q in cand - adj[u]:
+            cand.remove(q)
+            Q.append(q)
+            adj_q = adj[q]
+            subg_q = subg & adj_q
+            if not subg_q:
+                yield Q[:]
+            else:
+                cand_q = cand & adj_q
+                if cand_q:
+                    yield from expand(subg_q, cand_q)
+            Q.pop()
+
+    return expand(subg_init, cand_init)
+
+
+@nx._dispatchable(returns_graph=True)
+def make_max_clique_graph(G, create_using=None):
+    """Returns the maximal clique graph of the given graph.
+
+    The nodes of the maximal clique graph of `G` are the cliques of
+    `G` and an edge joins two cliques if the cliques are not disjoint.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        A graph whose nodes are the cliques of `G` and whose edges
+        join two cliques if they are not disjoint.
+
+    Notes
+    -----
+    This function behaves like the following code::
+
+        import networkx as nx
+
+        G = nx.make_clique_bipartite(G)
+        cliques = [v for v in G.nodes() if G.nodes[v]["bipartite"] == 0]
+        G = nx.bipartite.projected_graph(G, cliques)
+        G = nx.relabel_nodes(G, {-v: v - 1 for v in G})
+
+    It should be faster, though, since it skips all the intermediate
+    steps.
+
+    """
+    if create_using is None:
+        B = G.__class__()
+    else:
+        B = nx.empty_graph(0, create_using)
+    cliques = list(enumerate(set(c) for c in find_cliques(G)))
+    # Add a numbered node for each clique.
+    B.add_nodes_from(i for i, c in cliques)
+    # Join cliques by an edge if they share a node.
+    clique_pairs = combinations(cliques, 2)
+    B.add_edges_from((i, j) for (i, c1), (j, c2) in clique_pairs if c1 & c2)
+    return B
+
+
+@nx._dispatchable(returns_graph=True)
+def make_clique_bipartite(G, fpos=None, create_using=None, name=None):
+    """Returns the bipartite clique graph corresponding to `G`.
+
+    In the returned bipartite graph, the "bottom" nodes are the nodes of
+    `G` and the "top" nodes represent the maximal cliques of `G`.
+    There is an edge from node *v* to clique *C* in the returned graph
+    if and only if *v* is an element of *C*.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    fpos : bool
+        If True or not None, the returned graph will have an
+        additional attribute, `pos`, a dictionary mapping node to
+        position in the Euclidean plane.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        A bipartite graph whose "bottom" set is the nodes of the graph
+        `G`, whose "top" set is the cliques of `G`, and whose edges
+        join nodes of `G` to the cliques that contain them.
+
+        The nodes of the graph `G` have the node attribute
+        'bipartite' set to 1 and the nodes representing cliques
+        have the node attribute 'bipartite' set to 0, as is the
+        convention for bipartite graphs in NetworkX.
+
+    """
+    B = nx.empty_graph(0, create_using)
+    B.clear()
+    # The "bottom" nodes in the bipartite graph are the nodes of the
+    # original graph, G.
+    B.add_nodes_from(G, bipartite=1)
+    for i, cl in enumerate(find_cliques(G)):
+        # The "top" nodes in the bipartite graph are the cliques. These
+        # nodes get negative numbers as labels.
+        name = -i - 1
+        B.add_node(name, bipartite=0)
+        B.add_edges_from((v, name) for v in cl)
+    return B
+
+
+@nx._dispatchable
+def node_clique_number(G, nodes=None, cliques=None, separate_nodes=False):
+    """Returns the size of the largest maximal clique containing each given node.
+
+    Returns a single or list depending on input nodes.
+    An optional list of cliques can be input if already computed.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    cliques : list, optional (default=None)
+        A list of cliques, each of which is itself a list of nodes.
+        If not specified, the list of all cliques will be computed
+        using :func:`find_cliques`.
+
+    Returns
+    -------
+    int or dict
+        If `nodes` is a single node, returns the size of the
+        largest maximal clique in `G` containing that node.
+        Otherwise return a dict keyed by node to the size
+        of the largest maximal clique containing that node.
+
+    See Also
+    --------
+    find_cliques
+        find_cliques yields the maximal cliques of G.
+        It accepts a `nodes` argument which restricts consideration to
+        maximal cliques containing all the given `nodes`.
+        The search for the cliques is optimized for `nodes`.
+    """
+    if cliques is None:
+        if nodes is not None:
+            # Use ego_graph to decrease size of graph
+            # check for single node
+            if nodes in G:
+                return max(len(c) for c in find_cliques(nx.ego_graph(G, nodes)))
+            # handle multiple nodes
+            return {
+                n: max(len(c) for c in find_cliques(nx.ego_graph(G, n))) for n in nodes
+            }
+
+        # nodes is None--find all cliques
+        cliques = list(find_cliques(G))
+
+    # single node requested
+    if nodes in G:
+        return max(len(c) for c in cliques if nodes in c)
+
+    # multiple nodes requested
+    # preprocess all nodes (faster than one at a time for even 2 nodes)
+    size_for_n = defaultdict(int)
+    for c in cliques:
+        size_of_c = len(c)
+        for n in c:
+            if size_for_n[n] < size_of_c:
+                size_for_n[n] = size_of_c
+    if nodes is None:
+        return size_for_n
+    return {n: size_for_n[n] for n in nodes}
+
+
+def number_of_cliques(G, nodes=None, cliques=None):
+    """Returns the number of maximal cliques for each node.
+
+    Returns a single or list depending on input nodes.
+    Optional list of cliques can be input if already computed.
+    """
+    if cliques is None:
+        cliques = find_cliques(G)
+
+    if nodes is None:
+        nodes = list(G.nodes())  # none, get entire graph
+
+    if not isinstance(nodes, list):  # check for a list
+        v = nodes
+        # assume it is a single value
+        numcliq = sum(1 for c in cliques if v in c)
+    else:
+        numcliq = Counter(chain.from_iterable(cliques))
+        numcliq = {v: numcliq[v] for v in nodes}  # return a dict
+    return numcliq
+
+
+class MaxWeightClique:
+    """A class for the maximum weight clique algorithm.
+
+    This class is a helper for the `max_weight_clique` function.  The class
+    should not normally be used directly.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The undirected graph for which a maximum weight clique is sought
+    weight : string or None, optional (default='weight')
+        The node attribute that holds the integer value used as a weight.
+        If None, then each node has weight 1.
+
+    Attributes
+    ----------
+    G : NetworkX graph
+        The undirected graph for which a maximum weight clique is sought
+    node_weights: dict
+        The weight of each node
+    incumbent_nodes : list
+        The nodes of the incumbent clique (the best clique found so far)
+    incumbent_weight: int
+        The weight of the incumbent clique
+    """
+
+    def __init__(self, G, weight):
+        self.G = G
+        self.incumbent_nodes = []
+        self.incumbent_weight = 0
+
+        if weight is None:
+            self.node_weights = dict.fromkeys(G.nodes(), 1)
+        else:
+            for v in G.nodes():
+                if weight not in G.nodes[v]:
+                    errmsg = f"Node {v!r} does not have the requested weight field."
+                    raise KeyError(errmsg)
+                if not isinstance(G.nodes[v][weight], int):
+                    errmsg = f"The {weight!r} field of node {v!r} is not an integer."
+                    raise ValueError(errmsg)
+            self.node_weights = {v: G.nodes[v][weight] for v in G.nodes()}
+
+    def update_incumbent_if_improved(self, C, C_weight):
+        """Update the incumbent if the node set C has greater weight.
+
+        C is assumed to be a clique.
+        """
+        if C_weight > self.incumbent_weight:
+            self.incumbent_nodes = C[:]
+            self.incumbent_weight = C_weight
+
+    def greedily_find_independent_set(self, P):
+        """Greedily find an independent set of nodes from a set of
+        nodes P."""
+        independent_set = []
+        P = P[:]
+        while P:
+            v = P[0]
+            independent_set.append(v)
+            P = [w for w in P if v != w and not self.G.has_edge(v, w)]
+        return independent_set
+
+    def find_branching_nodes(self, P, target):
+        """Find a set of nodes to branch on."""
+        residual_wt = {v: self.node_weights[v] for v in P}
+        total_wt = 0
+        P = P[:]
+        while P:
+            independent_set = self.greedily_find_independent_set(P)
+            min_wt_in_class = min(residual_wt[v] for v in independent_set)
+            total_wt += min_wt_in_class
+            if total_wt > target:
+                break
+            for v in independent_set:
+                residual_wt[v] -= min_wt_in_class
+            P = [v for v in P if residual_wt[v] != 0]
+        return P
+
+    def expand(self, C, C_weight, P):
+        """Look for the best clique that contains all the nodes in C and zero or
+        more of the nodes in P, backtracking if it can be shown that no such
+        clique has greater weight than the incumbent.
+        """
+        self.update_incumbent_if_improved(C, C_weight)
+        branching_nodes = self.find_branching_nodes(P, self.incumbent_weight - C_weight)
+        while branching_nodes:
+            v = branching_nodes.pop()
+            P.remove(v)
+            new_C = C + [v]
+            new_C_weight = C_weight + self.node_weights[v]
+            new_P = [w for w in P if self.G.has_edge(v, w)]
+            self.expand(new_C, new_C_weight, new_P)
+
+    def find_max_weight_clique(self):
+        """Find a maximum weight clique."""
+        # Sort nodes in reverse order of degree for speed
+        nodes = sorted(self.G.nodes(), key=lambda v: self.G.degree(v), reverse=True)
+        nodes = [v for v in nodes if self.node_weights[v] > 0]
+        self.expand([], 0, nodes)
+
+
+@not_implemented_for("directed")
+@nx._dispatchable(node_attrs="weight")
+def max_weight_clique(G, weight="weight"):
+    """Find a maximum weight clique in G.
+
+    A *clique* in a graph is a set of nodes such that every two distinct nodes
+    are adjacent.  The *weight* of a clique is the sum of the weights of its
+    nodes.  A *maximum weight clique* of graph G is a clique C in G such that
+    no clique in G has weight greater than the weight of C.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+    weight : string or None, optional (default='weight')
+        The node attribute that holds the integer value used as a weight.
+        If None, then each node has weight 1.
+
+    Returns
+    -------
+    clique : list
+        the nodes of a maximum weight clique
+    weight : int
+        the weight of a maximum weight clique
+
+    Notes
+    -----
+    The implementation is recursive, and therefore it may run into recursion
+    depth issues if G contains a clique whose number of nodes is close to the
+    recursion depth limit.
+
+    At each search node, the algorithm greedily constructs a weighted
+    independent set cover of part of the graph in order to find a small set of
+    nodes on which to branch.  The algorithm is very similar to the algorithm
+    of Tavares et al. [1]_, other than the fact that the NetworkX version does
+    not use bitsets.  This style of algorithm for maximum weight clique (and
+    maximum weight independent set, which is the same problem but on the
+    complement graph) has a decades-long history.  See Algorithm B of Warren
+    and Hicks [2]_ and the references in that paper.
+
+    References
+    ----------
+    .. [1] Tavares, W.A., Neto, M.B.C., Rodrigues, C.D., Michelon, P.: Um
+           algoritmo de branch and bound para o problema da clique máxima
+           ponderada.  Proceedings of XLVII SBPO 1 (2015).
+
+    .. [2] Warren, Jeffrey S, Hicks, Illya V.: Combinatorial Branch-and-Bound
+           for the Maximum Weight Independent Set Problem.  Technical Report,
+           Texas A&M University (2016).
+    """
+
+    mwc = MaxWeightClique(G, weight)
+    mwc.find_max_weight_clique()
+    return mwc.incumbent_nodes, mwc.incumbent_weight
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/cluster.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/cluster.py
new file mode 100644
index 0000000000000000000000000000000000000000..ceb0c4eb381c6486e62b2d0564cafeba0c11d981
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/cluster.py
@@ -0,0 +1,658 @@
+"""Algorithms to characterize the number of triangles in a graph."""
+
+from collections import Counter
+from itertools import chain, combinations
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = [
+    "triangles",
+    "average_clustering",
+    "clustering",
+    "transitivity",
+    "square_clustering",
+    "generalized_degree",
+]
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def triangles(G, nodes=None):
+    """Compute the number of triangles.
+
+    Finds the number of triangles that include a node as one vertex.
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+
+    nodes : node, iterable of nodes, or None (default=None)
+        If a singleton node, return the number of triangles for that node.
+        If an iterable, compute the number of triangles for each of those nodes.
+        If `None` (the default) compute the number of triangles for all nodes in `G`.
+
+    Returns
+    -------
+    out : dict or int
+       If `nodes` is a container of nodes, returns number of triangles keyed by node (dict).
+       If `nodes` is a specific node, returns number of triangles for the node (int).
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> print(nx.triangles(G, 0))
+    6
+    >>> print(nx.triangles(G))
+    {0: 6, 1: 6, 2: 6, 3: 6, 4: 6}
+    >>> print(list(nx.triangles(G, [0, 1]).values()))
+    [6, 6]
+
+    Notes
+    -----
+    Self loops are ignored.
+
+    """
+    if nodes is not None:
+        # If `nodes` represents a single node, return only its number of triangles
+        if nodes in G:
+            return next(_triangles_and_degree_iter(G, nodes))[2] // 2
+
+        # if `nodes` is a container of nodes, then return a
+        # dictionary mapping node to number of triangles.
+        return {v: t // 2 for v, d, t, _ in _triangles_and_degree_iter(G, nodes)}
+
+    # if nodes is None, then compute triangles for the complete graph
+
+    # dict used to avoid visiting the same nodes twice
+    # this allows calculating/counting each triangle only once
+    later_nbrs = {}
+
+    # iterate over the nodes in a graph
+    for node, neighbors in G.adjacency():
+        later_nbrs[node] = {n for n in neighbors if n not in later_nbrs and n != node}
+
+    # instantiate Counter for each node to include isolated nodes
+    # add 1 to the count if a nodes neighbor's neighbor is also a neighbor
+    triangle_counts = Counter(dict.fromkeys(G, 0))
+    for node1, neighbors in later_nbrs.items():
+        for node2 in neighbors:
+            third_nodes = neighbors & later_nbrs[node2]
+            m = len(third_nodes)
+            triangle_counts[node1] += m
+            triangle_counts[node2] += m
+            triangle_counts.update(third_nodes)
+
+    return dict(triangle_counts)
+
+
+@not_implemented_for("multigraph")
+def _triangles_and_degree_iter(G, nodes=None):
+    """Return an iterator of (node, degree, triangles, generalized degree).
+
+    This double counts triangles so you may want to divide by 2.
+    See degree(), triangles() and generalized_degree() for definitions
+    and details.
+
+    """
+    if nodes is None:
+        nodes_nbrs = G.adj.items()
+    else:
+        nodes_nbrs = ((n, G[n]) for n in G.nbunch_iter(nodes))
+
+    for v, v_nbrs in nodes_nbrs:
+        vs = set(v_nbrs) - {v}
+        gen_degree = Counter(len(vs & (set(G[w]) - {w})) for w in vs)
+        ntriangles = sum(k * val for k, val in gen_degree.items())
+        yield (v, len(vs), ntriangles, gen_degree)
+
+
+@not_implemented_for("multigraph")
+def _weighted_triangles_and_degree_iter(G, nodes=None, weight="weight"):
+    """Return an iterator of (node, degree, weighted_triangles).
+
+    Used for weighted clustering.
+    Note: this returns the geometric average weight of edges in the triangle.
+    Also, each triangle is counted twice (each direction).
+    So you may want to divide by 2.
+
+    """
+    import numpy as np
+
+    if weight is None or G.number_of_edges() == 0:
+        max_weight = 1
+    else:
+        max_weight = max(d.get(weight, 1) for u, v, d in G.edges(data=True))
+    if nodes is None:
+        nodes_nbrs = G.adj.items()
+    else:
+        nodes_nbrs = ((n, G[n]) for n in G.nbunch_iter(nodes))
+
+    def wt(u, v):
+        return G[u][v].get(weight, 1) / max_weight
+
+    for i, nbrs in nodes_nbrs:
+        inbrs = set(nbrs) - {i}
+        weighted_triangles = 0
+        seen = set()
+        for j in inbrs:
+            seen.add(j)
+            # This avoids counting twice -- we double at the end.
+            jnbrs = set(G[j]) - seen
+            # Only compute the edge weight once, before the inner inner
+            # loop.
+            wij = wt(i, j)
+            weighted_triangles += np.cbrt(
+                [(wij * wt(j, k) * wt(k, i)) for k in inbrs & jnbrs]
+            ).sum()
+        yield (i, len(inbrs), 2 * float(weighted_triangles))
+
+
+@not_implemented_for("multigraph")
+def _directed_triangles_and_degree_iter(G, nodes=None):
+    """Return an iterator of
+    (node, total_degree, reciprocal_degree, directed_triangles).
+
+    Used for directed clustering.
+    Note that unlike `_triangles_and_degree_iter()`, this function counts
+    directed triangles so does not count triangles twice.
+
+    """
+    nodes_nbrs = ((n, G._pred[n], G._succ[n]) for n in G.nbunch_iter(nodes))
+
+    for i, preds, succs in nodes_nbrs:
+        ipreds = set(preds) - {i}
+        isuccs = set(succs) - {i}
+
+        directed_triangles = 0
+        for j in chain(ipreds, isuccs):
+            jpreds = set(G._pred[j]) - {j}
+            jsuccs = set(G._succ[j]) - {j}
+            directed_triangles += sum(
+                1
+                for k in chain(
+                    (ipreds & jpreds),
+                    (ipreds & jsuccs),
+                    (isuccs & jpreds),
+                    (isuccs & jsuccs),
+                )
+            )
+        dtotal = len(ipreds) + len(isuccs)
+        dbidirectional = len(ipreds & isuccs)
+        yield (i, dtotal, dbidirectional, directed_triangles)
+
+
+@not_implemented_for("multigraph")
+def _directed_weighted_triangles_and_degree_iter(G, nodes=None, weight="weight"):
+    """Return an iterator of
+    (node, total_degree, reciprocal_degree, directed_weighted_triangles).
+
+    Used for directed weighted clustering.
+    Note that unlike `_weighted_triangles_and_degree_iter()`, this function counts
+    directed triangles so does not count triangles twice.
+
+    """
+    import numpy as np
+
+    if weight is None or G.number_of_edges() == 0:
+        max_weight = 1
+    else:
+        max_weight = max(d.get(weight, 1) for u, v, d in G.edges(data=True))
+
+    nodes_nbrs = ((n, G._pred[n], G._succ[n]) for n in G.nbunch_iter(nodes))
+
+    def wt(u, v):
+        return G[u][v].get(weight, 1) / max_weight
+
+    for i, preds, succs in nodes_nbrs:
+        ipreds = set(preds) - {i}
+        isuccs = set(succs) - {i}
+
+        directed_triangles = 0
+        for j in ipreds:
+            jpreds = set(G._pred[j]) - {j}
+            jsuccs = set(G._succ[j]) - {j}
+            directed_triangles += np.cbrt(
+                [(wt(j, i) * wt(k, i) * wt(k, j)) for k in ipreds & jpreds]
+            ).sum()
+            directed_triangles += np.cbrt(
+                [(wt(j, i) * wt(k, i) * wt(j, k)) for k in ipreds & jsuccs]
+            ).sum()
+            directed_triangles += np.cbrt(
+                [(wt(j, i) * wt(i, k) * wt(k, j)) for k in isuccs & jpreds]
+            ).sum()
+            directed_triangles += np.cbrt(
+                [(wt(j, i) * wt(i, k) * wt(j, k)) for k in isuccs & jsuccs]
+            ).sum()
+
+        for j in isuccs:
+            jpreds = set(G._pred[j]) - {j}
+            jsuccs = set(G._succ[j]) - {j}
+            directed_triangles += np.cbrt(
+                [(wt(i, j) * wt(k, i) * wt(k, j)) for k in ipreds & jpreds]
+            ).sum()
+            directed_triangles += np.cbrt(
+                [(wt(i, j) * wt(k, i) * wt(j, k)) for k in ipreds & jsuccs]
+            ).sum()
+            directed_triangles += np.cbrt(
+                [(wt(i, j) * wt(i, k) * wt(k, j)) for k in isuccs & jpreds]
+            ).sum()
+            directed_triangles += np.cbrt(
+                [(wt(i, j) * wt(i, k) * wt(j, k)) for k in isuccs & jsuccs]
+            ).sum()
+
+        dtotal = len(ipreds) + len(isuccs)
+        dbidirectional = len(ipreds & isuccs)
+        yield (i, dtotal, dbidirectional, float(directed_triangles))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def average_clustering(G, nodes=None, weight=None, count_zeros=True):
+    r"""Compute the average clustering coefficient for the graph G.
+
+    The clustering coefficient for the graph is the average,
+
+    .. math::
+
+       C = \frac{1}{n}\sum_{v \in G} c_v,
+
+    where :math:`n` is the number of nodes in `G`.
+
+    Parameters
+    ----------
+    G : graph
+
+    nodes : container of nodes, optional (default=all nodes in G)
+       Compute average clustering for nodes in this container.
+
+    weight : string or None, optional (default=None)
+       The edge attribute that holds the numerical value used as a weight.
+       If None, then each edge has weight 1.
+
+    count_zeros : bool
+       If False include only the nodes with nonzero clustering in the average.
+
+    Returns
+    -------
+    avg : float
+       Average clustering
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> print(nx.average_clustering(G))
+    1.0
+
+    Notes
+    -----
+    This is a space saving routine; it might be faster
+    to use the clustering function to get a list and then take the average.
+
+    Self loops are ignored.
+
+    References
+    ----------
+    .. [1] Generalizations of the clustering coefficient to weighted
+       complex networks by J. Saramäki, M. Kivelä, J.-P. Onnela,
+       K. Kaski, and J. Kertész, Physical Review E, 75 027105 (2007).
+       http://jponnela.com/web_documents/a9.pdf
+    .. [2] Marcus Kaiser,  Mean clustering coefficients: the role of isolated
+       nodes and leafs on clustering measures for small-world networks.
+       https://arxiv.org/abs/0802.2512
+    """
+    c = clustering(G, nodes, weight=weight).values()
+    if not count_zeros:
+        c = [v for v in c if abs(v) > 0]
+    return sum(c) / len(c)
+
+
+@nx._dispatchable(edge_attrs="weight")
+def clustering(G, nodes=None, weight=None):
+    r"""Compute the clustering coefficient for nodes.
+
+    For unweighted graphs, the clustering of a node :math:`u`
+    is the fraction of possible triangles through that node that exist,
+
+    .. math::
+
+      c_u = \frac{2 T(u)}{deg(u)(deg(u)-1)},
+
+    where :math:`T(u)` is the number of triangles through node :math:`u` and
+    :math:`deg(u)` is the degree of :math:`u`.
+
+    For weighted graphs, there are several ways to define clustering [1]_.
+    the one used here is defined
+    as the geometric average of the subgraph edge weights [2]_,
+
+    .. math::
+
+       c_u = \frac{1}{deg(u)(deg(u)-1))}
+             \sum_{vw} (\hat{w}_{uv} \hat{w}_{uw} \hat{w}_{vw})^{1/3}.
+
+    The edge weights :math:`\hat{w}_{uv}` are normalized by the maximum weight
+    in the network :math:`\hat{w}_{uv} = w_{uv}/\max(w)`.
+
+    The value of :math:`c_u` is assigned to 0 if :math:`deg(u) < 2`.
+
+    Additionally, this weighted definition has been generalized to support negative edge weights [3]_.
+
+    For directed graphs, the clustering is similarly defined as the fraction
+    of all possible directed triangles or geometric average of the subgraph
+    edge weights for unweighted and weighted directed graph respectively [4]_.
+
+    .. math::
+
+       c_u = \frac{T(u)}{2(deg^{tot}(u)(deg^{tot}(u)-1) - 2deg^{\leftrightarrow}(u))},
+
+    where :math:`T(u)` is the number of directed triangles through node
+    :math:`u`, :math:`deg^{tot}(u)` is the sum of in degree and out degree of
+    :math:`u` and :math:`deg^{\leftrightarrow}(u)` is the reciprocal degree of
+    :math:`u`.
+
+
+    Parameters
+    ----------
+    G : graph
+
+    nodes : node, iterable of nodes, or None (default=None)
+        If a singleton node, return the number of triangles for that node.
+        If an iterable, compute the number of triangles for each of those nodes.
+        If `None` (the default) compute the number of triangles for all nodes in `G`.
+
+    weight : string or None, optional (default=None)
+       The edge attribute that holds the numerical value used as a weight.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    out : float, or dictionary
+       Clustering coefficient at specified nodes
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> print(nx.clustering(G, 0))
+    1.0
+    >>> print(nx.clustering(G))
+    {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0}
+
+    Notes
+    -----
+    Self loops are ignored.
+
+    References
+    ----------
+    .. [1] Generalizations of the clustering coefficient to weighted
+       complex networks by J. Saramäki, M. Kivelä, J.-P. Onnela,
+       K. Kaski, and J. Kertész, Physical Review E, 75 027105 (2007).
+       http://jponnela.com/web_documents/a9.pdf
+    .. [2] Intensity and coherence of motifs in weighted complex
+       networks by J. P. Onnela, J. Saramäki, J. Kertész, and K. Kaski,
+       Physical Review E, 71(6), 065103 (2005).
+    .. [3] Generalization of Clustering Coefficients to Signed Correlation Networks
+       by G. Costantini and M. Perugini, PloS one, 9(2), e88669 (2014).
+    .. [4] Clustering in complex directed networks by G. Fagiolo,
+       Physical Review E, 76(2), 026107 (2007).
+    """
+    if G.is_directed():
+        if weight is not None:
+            td_iter = _directed_weighted_triangles_and_degree_iter(G, nodes, weight)
+            clusterc = {
+                v: 0 if t == 0 else t / ((dt * (dt - 1) - 2 * db) * 2)
+                for v, dt, db, t in td_iter
+            }
+        else:
+            td_iter = _directed_triangles_and_degree_iter(G, nodes)
+            clusterc = {
+                v: 0 if t == 0 else t / ((dt * (dt - 1) - 2 * db) * 2)
+                for v, dt, db, t in td_iter
+            }
+    else:
+        # The formula 2*T/(d*(d-1)) from docs is t/(d*(d-1)) here b/c t==2*T
+        if weight is not None:
+            td_iter = _weighted_triangles_and_degree_iter(G, nodes, weight)
+            clusterc = {v: 0 if t == 0 else t / (d * (d - 1)) for v, d, t in td_iter}
+        else:
+            td_iter = _triangles_and_degree_iter(G, nodes)
+            clusterc = {v: 0 if t == 0 else t / (d * (d - 1)) for v, d, t, _ in td_iter}
+    if nodes in G:
+        # Return the value of the sole entry in the dictionary.
+        return clusterc[nodes]
+    return clusterc
+
+
+@nx._dispatchable
+def transitivity(G):
+    r"""Compute graph transitivity, the fraction of all possible triangles
+    present in G.
+
+    Possible triangles are identified by the number of "triads"
+    (two edges with a shared vertex).
+
+    The transitivity is
+
+    .. math::
+
+        T = 3\frac{\#triangles}{\#triads}.
+
+    Parameters
+    ----------
+    G : graph
+
+    Returns
+    -------
+    out : float
+       Transitivity
+
+    Notes
+    -----
+    Self loops are ignored.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> print(nx.transitivity(G))
+    1.0
+    """
+    triangles_contri = [
+        (t, d * (d - 1)) for v, d, t, _ in _triangles_and_degree_iter(G)
+    ]
+    # If the graph is empty
+    if len(triangles_contri) == 0:
+        return 0
+    triangles, contri = map(sum, zip(*triangles_contri))
+    return 0 if triangles == 0 else triangles / contri
+
+
+@nx._dispatchable
+def square_clustering(G, nodes=None):
+    r"""Compute the squares clustering coefficient for nodes.
+
+    For each node return the fraction of possible squares that exist at
+    the node [1]_
+
+    .. math::
+       C_4(v) = \frac{ \sum_{u=1}^{k_v}
+       \sum_{w=u+1}^{k_v} q_v(u,w) }{ \sum_{u=1}^{k_v}
+       \sum_{w=u+1}^{k_v} [a_v(u,w) + q_v(u,w)]},
+
+    where :math:`q_v(u,w)` are the number of common neighbors of :math:`u` and
+    :math:`w` other than :math:`v` (ie squares), and :math:`a_v(u,w) = (k_u -
+    (1+q_v(u,w)+\theta_{uv})) + (k_w - (1+q_v(u,w)+\theta_{uw}))`, where
+    :math:`\theta_{uw} = 1` if :math:`u` and :math:`w` are connected and 0
+    otherwise. [2]_
+
+    Parameters
+    ----------
+    G : graph
+
+    nodes : container of nodes, optional (default=all nodes in G)
+       Compute clustering for nodes in this container.
+
+    Returns
+    -------
+    c4 : dictionary
+       A dictionary keyed by node with the square clustering coefficient value.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> print(nx.square_clustering(G, 0))
+    1.0
+    >>> print(nx.square_clustering(G))
+    {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0}
+
+    Notes
+    -----
+    Self loops are ignored.
+
+    While :math:`C_3(v)` (triangle clustering) gives the probability that
+    two neighbors of node v are connected with each other, :math:`C_4(v)` is
+    the probability that two neighbors of node v share a common
+    neighbor different from v. This algorithm can be applied to both
+    bipartite and unipartite networks.
+
+    References
+    ----------
+    .. [1] Pedro G. Lind, Marta C. González, and Hans J. Herrmann. 2005
+        Cycles and clustering in bipartite networks.
+        Physical Review E (72) 056127.
+    .. [2] Zhang, Peng et al. Clustering Coefficient and Community Structure of
+        Bipartite Networks. Physica A: Statistical Mechanics and its Applications 387.27 (2008): 6869–6875.
+        https://arxiv.org/abs/0710.0117v1
+    """
+    if nodes is None:
+        node_iter = G
+    else:
+        node_iter = G.nbunch_iter(nodes)
+    clustering = {}
+    _G_adj = G._adj
+
+    class GAdj(dict):
+        """Calculate (and cache) node neighbor sets excluding self-loops."""
+
+        def __missing__(self, v):
+            v_neighbors = self[v] = set(_G_adj[v])
+            v_neighbors.discard(v)  # Ignore self-loops
+            return v_neighbors
+
+    G_adj = GAdj()  # Values are sets of neighbors (no self-loops)
+
+    for v in node_iter:
+        v_neighbors = G_adj[v]
+        v_degrees_m1 = len(v_neighbors) - 1  # degrees[v] - 1 (used below)
+        if v_degrees_m1 <= 0:
+            # Can't form a square without at least two neighbors
+            clustering[v] = 0
+            continue
+
+        # Count squares with nodes u-v-w-x from the current node v.
+        # Terms of the denominator: potential = uw_degrees - uw_count - triangles - squares
+        # uw_degrees: degrees[u] + degrees[w] for each u-w combo
+        uw_degrees = 0
+        # uw_count: 1 for each u and 1 for each w for all combos (degrees * (degrees - 1))
+        uw_count = len(v_neighbors) * v_degrees_m1
+        # triangles: 1 for each edge where u-w or w-u are connected (i.e. triangles)
+        triangles = 0
+        # squares: the number of squares (also the numerator)
+        squares = 0
+
+        # Iterate over all neighbors
+        for u in v_neighbors:
+            u_neighbors = G_adj[u]
+            uw_degrees += len(u_neighbors) * v_degrees_m1
+            # P2 from https://arxiv.org/abs/2007.11111
+            p2 = len(u_neighbors & v_neighbors)
+            # triangles is C_3, sigma_4 from https://arxiv.org/abs/2007.11111
+            # This double-counts triangles compared to `triangles` function
+            triangles += p2
+            # squares is C_4, sigma_12 from https://arxiv.org/abs/2007.11111
+            # Include this term, b/c a neighbor u can also be a neighbor of neighbor x
+            squares += p2 * (p2 - 1)  # Will divide by 2 later
+
+        # And iterate over all neighbors of neighbors.
+        # These nodes x may be the corners opposite v in squares u-v-w-x.
+        two_hop_neighbors = set.union(*(G_adj[u] for u in v_neighbors))
+        two_hop_neighbors -= v_neighbors  # Neighbors already counted above
+        two_hop_neighbors.discard(v)
+        for x in two_hop_neighbors:
+            p2 = len(v_neighbors & G_adj[x])
+            squares += p2 * (p2 - 1)  # Will divide by 2 later
+
+        squares //= 2
+        potential = uw_degrees - uw_count - triangles - squares
+        if potential > 0:
+            clustering[v] = squares / potential
+        else:
+            clustering[v] = 0
+    if nodes in G:
+        # Return the value of the sole entry in the dictionary.
+        return clustering[nodes]
+    return clustering
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def generalized_degree(G, nodes=None):
+    r"""Compute the generalized degree for nodes.
+
+    For each node, the generalized degree shows how many edges of given
+    triangle multiplicity the node is connected to. The triangle multiplicity
+    of an edge is the number of triangles an edge participates in. The
+    generalized degree of node :math:`i` can be written as a vector
+    :math:`\mathbf{k}_i=(k_i^{(0)}, \dotsc, k_i^{(N-2)})` where
+    :math:`k_i^{(j)}` is the number of edges attached to node :math:`i` that
+    participate in :math:`j` triangles.
+
+    Parameters
+    ----------
+    G : graph
+
+    nodes : container of nodes, optional (default=all nodes in G)
+       Compute the generalized degree for nodes in this container.
+
+    Returns
+    -------
+    out : Counter, or dictionary of Counters
+       Generalized degree of specified nodes. The Counter is keyed by edge
+       triangle multiplicity.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> print(nx.generalized_degree(G, 0))
+    Counter({3: 4})
+    >>> print(nx.generalized_degree(G))
+    {0: Counter({3: 4}), 1: Counter({3: 4}), 2: Counter({3: 4}), 3: Counter({3: 4}), 4: Counter({3: 4})}
+
+    To recover the number of triangles attached to a node:
+
+    >>> k1 = nx.generalized_degree(G, 0)
+    >>> sum([k * v for k, v in k1.items()]) / 2 == nx.triangles(G, 0)
+    True
+
+    Notes
+    -----
+    Self loops are ignored.
+
+    In a network of N nodes, the highest triangle multiplicity an edge can have
+    is N-2.
+
+    The return value does not include a `zero` entry if no edges of a
+    particular triangle multiplicity are present.
+
+    The number of triangles node :math:`i` is attached to can be recovered from
+    the generalized degree :math:`\mathbf{k}_i=(k_i^{(0)}, \dotsc,
+    k_i^{(N-2)})` by :math:`(k_i^{(1)}+2k_i^{(2)}+\dotsc +(N-2)k_i^{(N-2)})/2`.
+
+    References
+    ----------
+    .. [1] Networks with arbitrary edge multiplicities by V. Zlatić,
+        D. Garlaschelli and G. Caldarelli, EPL (Europhysics Letters),
+        Volume 97, Number 2 (2012).
+        https://iopscience.iop.org/article/10.1209/0295-5075/97/28005
+    """
+    if nodes in G:
+        return next(_triangles_and_degree_iter(G, nodes))[3]
+    return {v: gd for v, d, t, gd in _triangles_and_degree_iter(G, nodes)}
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/communicability_alg.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/communicability_alg.py
new file mode 100644
index 0000000000000000000000000000000000000000..dea156b633a2b367c184f4bf31ab465812de68b4
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/communicability_alg.py
@@ -0,0 +1,163 @@
+"""
+Communicability.
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["communicability", "communicability_exp"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def communicability(G):
+    r"""Returns communicability between all pairs of nodes in G.
+
+    The communicability between pairs of nodes in G is the sum of
+    walks of different lengths starting at node u and ending at node v.
+
+    Parameters
+    ----------
+    G: graph
+
+    Returns
+    -------
+    comm: dictionary of dictionaries
+        Dictionary of dictionaries keyed by nodes with communicability
+        as the value.
+
+    Raises
+    ------
+    NetworkXError
+       If the graph is not undirected and simple.
+
+    See Also
+    --------
+    communicability_exp:
+       Communicability between all pairs of nodes in G  using spectral
+       decomposition.
+    communicability_betweenness_centrality:
+       Communicability betweenness centrality for each node in G.
+
+    Notes
+    -----
+    This algorithm uses a spectral decomposition of the adjacency matrix.
+    Let G=(V,E) be a simple undirected graph.  Using the connection between
+    the powers  of the adjacency matrix and the number of walks in the graph,
+    the communicability  between nodes `u` and `v` based on the graph spectrum
+    is [1]_
+
+    .. math::
+        C(u,v)=\sum_{j=1}^{n}\phi_{j}(u)\phi_{j}(v)e^{\lambda_{j}},
+
+    where `\phi_{j}(u)` is the `u\rm{th}` element of the `j\rm{th}` orthonormal
+    eigenvector of the adjacency matrix associated with the eigenvalue
+    `\lambda_{j}`.
+
+    References
+    ----------
+    .. [1] Ernesto Estrada, Naomichi Hatano,
+       "Communicability in complex networks",
+       Phys. Rev. E 77, 036111 (2008).
+       https://arxiv.org/abs/0707.0756
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (1, 2), (1, 5), (5, 4), (2, 4), (2, 3), (4, 3), (3, 6)])
+    >>> c = nx.communicability(G)
+    """
+    import numpy as np
+
+    nodelist = list(G)  # ordering of nodes in matrix
+    A = nx.to_numpy_array(G, nodelist)
+    # convert to 0-1 matrix
+    A[A != 0.0] = 1
+    w, vec = np.linalg.eigh(A)
+    expw = np.exp(w)
+    mapping = dict(zip(nodelist, range(len(nodelist))))
+    c = {}
+    # computing communicabilities
+    for u in G:
+        c[u] = {}
+        for v in G:
+            s = 0
+            p = mapping[u]
+            q = mapping[v]
+            for j in range(len(nodelist)):
+                s += vec[:, j][p] * vec[:, j][q] * expw[j]
+            c[u][v] = float(s)
+    return c
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def communicability_exp(G):
+    r"""Returns communicability between all pairs of nodes in G.
+
+    Communicability between pair of node (u,v) of node in G is the sum of
+    walks of different lengths starting at node u and ending at node v.
+
+    Parameters
+    ----------
+    G: graph
+
+    Returns
+    -------
+    comm: dictionary of dictionaries
+        Dictionary of dictionaries keyed by nodes with communicability
+        as the value.
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is not undirected and simple.
+
+    See Also
+    --------
+    communicability:
+       Communicability between pairs of nodes in G.
+    communicability_betweenness_centrality:
+       Communicability betweenness centrality for each node in G.
+
+    Notes
+    -----
+    This algorithm uses matrix exponentiation of the adjacency matrix.
+
+    Let G=(V,E) be a simple undirected graph.  Using the connection between
+    the powers  of the adjacency matrix and the number of walks in the graph,
+    the communicability between nodes u and v is [1]_,
+
+    .. math::
+        C(u,v) = (e^A)_{uv},
+
+    where `A` is the adjacency matrix of G.
+
+    References
+    ----------
+    .. [1] Ernesto Estrada, Naomichi Hatano,
+       "Communicability in complex networks",
+       Phys. Rev. E 77, 036111 (2008).
+       https://arxiv.org/abs/0707.0756
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (1, 2), (1, 5), (5, 4), (2, 4), (2, 3), (4, 3), (3, 6)])
+    >>> c = nx.communicability_exp(G)
+    """
+    import scipy as sp
+
+    nodelist = list(G)  # ordering of nodes in matrix
+    A = nx.to_numpy_array(G, nodelist)
+    # convert to 0-1 matrix
+    A[A != 0.0] = 1
+    # communicability matrix
+    expA = sp.linalg.expm(A)
+    mapping = dict(zip(nodelist, range(len(nodelist))))
+    c = {}
+    for u in G:
+        c[u] = {}
+        for v in G:
+            c[u][v] = float(expA[mapping[u], mapping[v]])
+    return c
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/core.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..fec26ec984161eb2d927019d90540690cbc5aa45
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/core.py
@@ -0,0 +1,588 @@
+"""
+Find the k-cores of a graph.
+
+The k-core is found by recursively pruning nodes with degrees less than k.
+
+See the following references for details:
+
+An O(m) Algorithm for Cores Decomposition of Networks
+Vladimir Batagelj and Matjaz Zaversnik, 2003.
+https://arxiv.org/abs/cs.DS/0310049
+
+Generalized Cores
+Vladimir Batagelj and Matjaz Zaversnik, 2002.
+https://arxiv.org/pdf/cs/0202039
+
+For directed graphs a more general notion is that of D-cores which
+looks at (k, l) restrictions on (in, out) degree. The (k, k) D-core
+is the k-core.
+
+D-cores: Measuring Collaboration of Directed Graphs Based on Degeneracy
+Christos Giatsidis, Dimitrios M. Thilikos, Michalis Vazirgiannis, ICDM 2011.
+http://www.graphdegeneracy.org/dcores_ICDM_2011.pdf
+
+Multi-scale structure and topological anomaly detection via a new network \
+statistic: The onion decomposition
+L. Hébert-Dufresne, J. A. Grochow, and A. Allard
+Scientific Reports 6, 31708 (2016)
+http://doi.org/10.1038/srep31708
+
+"""
+
+import networkx as nx
+
+__all__ = [
+    "core_number",
+    "k_core",
+    "k_shell",
+    "k_crust",
+    "k_corona",
+    "k_truss",
+    "onion_layers",
+]
+
+
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable
+def core_number(G):
+    """Returns the core number for each node.
+
+    A k-core is a maximal subgraph that contains nodes of degree k or more.
+
+    The core number of a node is the largest value k of a k-core containing
+    that node.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       An undirected or directed graph
+
+    Returns
+    -------
+    core_number : dictionary
+       A dictionary keyed by node to the core number.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is a multigraph or contains self loops.
+
+    Notes
+    -----
+    For directed graphs the node degree is defined to be the
+    in-degree + out-degree.
+
+    Examples
+    --------
+    >>> degrees = [0, 1, 2, 2, 2, 2, 3]
+    >>> H = nx.havel_hakimi_graph(degrees)
+    >>> nx.core_number(H)
+    {0: 1, 1: 2, 2: 2, 3: 2, 4: 1, 5: 2, 6: 0}
+    >>> G = nx.DiGraph()
+    >>> G.add_edges_from([(1, 2), (2, 1), (2, 3), (2, 4), (3, 4), (4, 3)])
+    >>> nx.core_number(G)
+    {1: 2, 2: 2, 3: 2, 4: 2}
+
+    References
+    ----------
+    .. [1] An O(m) Algorithm for Cores Decomposition of Networks
+       Vladimir Batagelj and Matjaz Zaversnik, 2003.
+       https://arxiv.org/abs/cs.DS/0310049
+    """
+    if nx.number_of_selfloops(G) > 0:
+        msg = (
+            "Input graph has self loops which is not permitted; "
+            "Consider using G.remove_edges_from(nx.selfloop_edges(G))."
+        )
+        raise nx.NetworkXNotImplemented(msg)
+    degrees = dict(G.degree())
+    # Sort nodes by degree.
+    nodes = sorted(degrees, key=degrees.get)
+    bin_boundaries = [0]
+    curr_degree = 0
+    for i, v in enumerate(nodes):
+        if degrees[v] > curr_degree:
+            bin_boundaries.extend([i] * (degrees[v] - curr_degree))
+            curr_degree = degrees[v]
+    node_pos = {v: pos for pos, v in enumerate(nodes)}
+    # The initial guess for the core number of a node is its degree.
+    core = degrees
+    nbrs = {v: list(nx.all_neighbors(G, v)) for v in G}
+    for v in nodes:
+        for u in nbrs[v]:
+            if core[u] > core[v]:
+                nbrs[u].remove(v)
+                pos = node_pos[u]
+                bin_start = bin_boundaries[core[u]]
+                node_pos[u] = bin_start
+                node_pos[nodes[bin_start]] = pos
+                nodes[bin_start], nodes[pos] = nodes[pos], nodes[bin_start]
+                bin_boundaries[core[u]] += 1
+                core[u] -= 1
+    return core
+
+
+def _core_subgraph(G, k_filter, k=None, core=None):
+    """Returns the subgraph induced by nodes passing filter `k_filter`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       The graph or directed graph to process
+    k_filter : filter function
+       This function filters the nodes chosen. It takes three inputs:
+       A node of G, the filter's cutoff, and the core dict of the graph.
+       The function should return a Boolean value.
+    k : int, optional
+      The order of the core. If not specified use the max core number.
+      This value is used as the cutoff for the filter.
+    core : dict, optional
+      Precomputed core numbers keyed by node for the graph `G`.
+      If not specified, the core numbers will be computed from `G`.
+
+    """
+    if core is None:
+        core = core_number(G)
+    if k is None:
+        k = max(core.values())
+    nodes = (v for v in core if k_filter(v, k, core))
+    return G.subgraph(nodes).copy()
+
+
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def k_core(G, k=None, core_number=None):
+    """Returns the k-core of G.
+
+    A k-core is a maximal subgraph that contains nodes of degree `k` or more.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+      A graph or directed graph
+    k : int, optional
+      The order of the core. If not specified return the main core.
+    core_number : dictionary, optional
+      Precomputed core numbers for the graph G.
+
+    Returns
+    -------
+    G : NetworkX graph
+      The k-core subgraph
+
+    Raises
+    ------
+    NetworkXNotImplemented
+      The k-core is not defined for multigraphs or graphs with self loops.
+
+    Notes
+    -----
+    The main core is the core with `k` as the largest core_number.
+
+    For directed graphs the node degree is defined to be the
+    in-degree + out-degree.
+
+    Graph, node, and edge attributes are copied to the subgraph.
+
+    Examples
+    --------
+    >>> degrees = [0, 1, 2, 2, 2, 2, 3]
+    >>> H = nx.havel_hakimi_graph(degrees)
+    >>> H.degree
+    DegreeView({0: 1, 1: 2, 2: 2, 3: 2, 4: 2, 5: 3, 6: 0})
+    >>> nx.k_core(H).nodes
+    NodeView((1, 2, 3, 5))
+
+    See Also
+    --------
+    core_number
+
+    References
+    ----------
+    .. [1] An O(m) Algorithm for Cores Decomposition of Networks
+       Vladimir Batagelj and Matjaz Zaversnik,  2003.
+       https://arxiv.org/abs/cs.DS/0310049
+    """
+
+    def k_filter(v, k, c):
+        return c[v] >= k
+
+    return _core_subgraph(G, k_filter, k, core_number)
+
+
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def k_shell(G, k=None, core_number=None):
+    """Returns the k-shell of G.
+
+    The k-shell is the subgraph induced by nodes with core number k.
+    That is, nodes in the k-core that are not in the (k+1)-core.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+      A graph or directed graph.
+    k : int, optional
+      The order of the shell. If not specified return the outer shell.
+    core_number : dictionary, optional
+      Precomputed core numbers for the graph G.
+
+
+    Returns
+    -------
+    G : NetworkX graph
+       The k-shell subgraph
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        The k-shell is not implemented for multigraphs or graphs with self loops.
+
+    Notes
+    -----
+    This is similar to k_corona but in that case only neighbors in the
+    k-core are considered.
+
+    For directed graphs the node degree is defined to be the
+    in-degree + out-degree.
+
+    Graph, node, and edge attributes are copied to the subgraph.
+
+    Examples
+    --------
+    >>> degrees = [0, 1, 2, 2, 2, 2, 3]
+    >>> H = nx.havel_hakimi_graph(degrees)
+    >>> H.degree
+    DegreeView({0: 1, 1: 2, 2: 2, 3: 2, 4: 2, 5: 3, 6: 0})
+    >>> nx.k_shell(H, k=1).nodes
+    NodeView((0, 4))
+
+    See Also
+    --------
+    core_number
+    k_corona
+
+
+    References
+    ----------
+    .. [1] A model of Internet topology using k-shell decomposition
+       Shai Carmi, Shlomo Havlin, Scott Kirkpatrick, Yuval Shavitt,
+       and Eran Shir, PNAS  July 3, 2007   vol. 104  no. 27  11150-11154
+       http://www.pnas.org/content/104/27/11150.full
+    """
+
+    def k_filter(v, k, c):
+        return c[v] == k
+
+    return _core_subgraph(G, k_filter, k, core_number)
+
+
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def k_crust(G, k=None, core_number=None):
+    """Returns the k-crust of G.
+
+    The k-crust is the graph G with the edges of the k-core removed
+    and isolated nodes found after the removal of edges are also removed.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       A graph or directed graph.
+    k : int, optional
+      The order of the shell. If not specified return the main crust.
+    core_number : dictionary, optional
+      Precomputed core numbers for the graph G.
+
+    Returns
+    -------
+    G : NetworkX graph
+       The k-crust subgraph
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        The k-crust is not implemented for multigraphs or graphs with self loops.
+
+    Notes
+    -----
+    This definition of k-crust is different than the definition in [1]_.
+    The k-crust in [1]_ is equivalent to the k+1 crust of this algorithm.
+
+    For directed graphs the node degree is defined to be the
+    in-degree + out-degree.
+
+    Graph, node, and edge attributes are copied to the subgraph.
+
+    Examples
+    --------
+    >>> degrees = [0, 1, 2, 2, 2, 2, 3]
+    >>> H = nx.havel_hakimi_graph(degrees)
+    >>> H.degree
+    DegreeView({0: 1, 1: 2, 2: 2, 3: 2, 4: 2, 5: 3, 6: 0})
+    >>> nx.k_crust(H, k=1).nodes
+    NodeView((0, 4, 6))
+
+    See Also
+    --------
+    core_number
+
+    References
+    ----------
+    .. [1] A model of Internet topology using k-shell decomposition
+       Shai Carmi, Shlomo Havlin, Scott Kirkpatrick, Yuval Shavitt,
+       and Eran Shir, PNAS  July 3, 2007   vol. 104  no. 27  11150-11154
+       http://www.pnas.org/content/104/27/11150.full
+    """
+    # Default for k is one less than in _core_subgraph, so just inline.
+    #    Filter is c[v] <= k
+    if core_number is None:
+        core_number = nx.core_number(G)
+    if k is None:
+        k = max(core_number.values()) - 1
+    nodes = (v for v in core_number if core_number[v] <= k)
+    return G.subgraph(nodes).copy()
+
+
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def k_corona(G, k, core_number=None):
+    """Returns the k-corona of G.
+
+    The k-corona is the subgraph of nodes in the k-core which have
+    exactly k neighbors in the k-core.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       A graph or directed graph
+    k : int
+       The order of the corona.
+    core_number : dictionary, optional
+       Precomputed core numbers for the graph G.
+
+    Returns
+    -------
+    G : NetworkX graph
+       The k-corona subgraph
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        The k-corona is not defined for multigraphs or graphs with self loops.
+
+    Notes
+    -----
+    For directed graphs the node degree is defined to be the
+    in-degree + out-degree.
+
+    Graph, node, and edge attributes are copied to the subgraph.
+
+    Examples
+    --------
+    >>> degrees = [0, 1, 2, 2, 2, 2, 3]
+    >>> H = nx.havel_hakimi_graph(degrees)
+    >>> H.degree
+    DegreeView({0: 1, 1: 2, 2: 2, 3: 2, 4: 2, 5: 3, 6: 0})
+    >>> nx.k_corona(H, k=2).nodes
+    NodeView((1, 2, 3, 5))
+
+    See Also
+    --------
+    core_number
+
+    References
+    ----------
+    .. [1]  k -core (bootstrap) percolation on complex networks:
+       Critical phenomena and nonlocal effects,
+       A. V. Goltsev, S. N. Dorogovtsev, and J. F. F. Mendes,
+       Phys. Rev. E 73, 056101 (2006)
+       http://link.aps.org/doi/10.1103/PhysRevE.73.056101
+    """
+
+    def func(v, k, c):
+        return c[v] == k and k == sum(1 for w in G[v] if c[w] >= k)
+
+    return _core_subgraph(G, func, k, core_number)
+
+
+@nx.utils.not_implemented_for("directed")
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def k_truss(G, k):
+    """Returns the k-truss of `G`.
+
+    The k-truss is the maximal induced subgraph of `G` which contains at least
+    three vertices where every edge is incident to at least `k-2` triangles.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+      An undirected graph
+    k : int
+      The order of the truss
+
+    Returns
+    -------
+    H : NetworkX graph
+      The k-truss subgraph
+
+    Raises
+    ------
+    NetworkXNotImplemented
+      If `G` is a multigraph or directed graph or if it contains self loops.
+
+    Notes
+    -----
+    A k-clique is a (k-2)-truss and a k-truss is a (k+1)-core.
+
+    Graph, node, and edge attributes are copied to the subgraph.
+
+    K-trusses were originally defined in [2] which states that the k-truss
+    is the maximal induced subgraph where each edge belongs to at least
+    `k-2` triangles. A more recent paper, [1], uses a slightly different
+    definition requiring that each edge belong to at least `k` triangles.
+    This implementation uses the original definition of `k-2` triangles.
+
+    Examples
+    --------
+    >>> degrees = [0, 1, 2, 2, 2, 2, 3]
+    >>> H = nx.havel_hakimi_graph(degrees)
+    >>> H.degree
+    DegreeView({0: 1, 1: 2, 2: 2, 3: 2, 4: 2, 5: 3, 6: 0})
+    >>> nx.k_truss(H, k=2).nodes
+    NodeView((0, 1, 2, 3, 4, 5))
+
+    References
+    ----------
+    .. [1] Bounds and Algorithms for k-truss. Paul Burkhardt, Vance Faber,
+       David G. Harris, 2018. https://arxiv.org/abs/1806.05523v2
+    .. [2] Trusses: Cohesive Subgraphs for Social Network Analysis. Jonathan
+       Cohen, 2005.
+    """
+    if nx.number_of_selfloops(G) > 0:
+        msg = (
+            "Input graph has self loops which is not permitted; "
+            "Consider using G.remove_edges_from(nx.selfloop_edges(G))."
+        )
+        raise nx.NetworkXNotImplemented(msg)
+
+    H = G.copy()
+
+    n_dropped = 1
+    while n_dropped > 0:
+        n_dropped = 0
+        to_drop = []
+        seen = set()
+        for u in H:
+            nbrs_u = set(H[u])
+            seen.add(u)
+            new_nbrs = [v for v in nbrs_u if v not in seen]
+            for v in new_nbrs:
+                if len(nbrs_u & set(H[v])) < (k - 2):
+                    to_drop.append((u, v))
+        H.remove_edges_from(to_drop)
+        n_dropped = len(to_drop)
+        H.remove_nodes_from(list(nx.isolates(H)))
+
+    return H
+
+
+@nx.utils.not_implemented_for("multigraph")
+@nx.utils.not_implemented_for("directed")
+@nx._dispatchable
+def onion_layers(G):
+    """Returns the layer of each vertex in an onion decomposition of the graph.
+
+    The onion decomposition refines the k-core decomposition by providing
+    information on the internal organization of each k-shell. It is usually
+    used alongside the `core numbers`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph without self loops.
+
+    Returns
+    -------
+    od_layers : dictionary
+        A dictionary keyed by node to the onion layer. The layers are
+        contiguous integers starting at 1.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is a multigraph or directed graph or if it contains self loops.
+
+    Examples
+    --------
+    >>> degrees = [0, 1, 2, 2, 2, 2, 3]
+    >>> H = nx.havel_hakimi_graph(degrees)
+    >>> H.degree
+    DegreeView({0: 1, 1: 2, 2: 2, 3: 2, 4: 2, 5: 3, 6: 0})
+    >>> nx.onion_layers(H)
+    {6: 1, 0: 2, 4: 3, 1: 4, 2: 4, 3: 4, 5: 4}
+
+    See Also
+    --------
+    core_number
+
+    References
+    ----------
+    .. [1] Multi-scale structure and topological anomaly detection via a new
+       network statistic: The onion decomposition
+       L. Hébert-Dufresne, J. A. Grochow, and A. Allard
+       Scientific Reports 6, 31708 (2016)
+       http://doi.org/10.1038/srep31708
+    .. [2] Percolation and the effective structure of complex networks
+       A. Allard and L. Hébert-Dufresne
+       Physical Review X 9, 011023 (2019)
+       http://doi.org/10.1103/PhysRevX.9.011023
+    """
+    if nx.number_of_selfloops(G) > 0:
+        msg = (
+            "Input graph contains self loops which is not permitted; "
+            "Consider using G.remove_edges_from(nx.selfloop_edges(G))."
+        )
+        raise nx.NetworkXNotImplemented(msg)
+    # Dictionaries to register the k-core/onion decompositions.
+    od_layers = {}
+    # Adjacency list
+    neighbors = {v: list(nx.all_neighbors(G, v)) for v in G}
+    # Effective degree of nodes.
+    degrees = dict(G.degree())
+    # Performs the onion decomposition.
+    current_core = 1
+    current_layer = 1
+    # Sets vertices of degree 0 to layer 1, if any.
+    isolated_nodes = list(nx.isolates(G))
+    if len(isolated_nodes) > 0:
+        for v in isolated_nodes:
+            od_layers[v] = current_layer
+            degrees.pop(v)
+        current_layer = 2
+    # Finds the layer for the remaining nodes.
+    while len(degrees) > 0:
+        # Sets the order for looking at nodes.
+        nodes = sorted(degrees, key=degrees.get)
+        # Sets properly the current core.
+        min_degree = degrees[nodes[0]]
+        if min_degree > current_core:
+            current_core = min_degree
+        # Identifies vertices in the current layer.
+        this_layer = []
+        for n in nodes:
+            if degrees[n] > current_core:
+                break
+            this_layer.append(n)
+        # Identifies the core/layer of the vertices in the current layer.
+        for v in this_layer:
+            od_layers[v] = current_layer
+            for n in neighbors[v]:
+                neighbors[n].remove(v)
+                degrees[n] = degrees[n] - 1
+            degrees.pop(v)
+        # Updates the layer count.
+        current_layer = current_layer + 1
+    # Returns the dictionaries containing the onion layer of each vertices.
+    return od_layers
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/covering.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/covering.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdf607b3d798a9d2efeabb0bb95721d6370ca8cd
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/covering.py
@@ -0,0 +1,142 @@
+"""Functions related to graph covers."""
+
+from functools import partial
+from itertools import chain
+
+import networkx as nx
+from networkx.utils import arbitrary_element, not_implemented_for
+
+__all__ = ["min_edge_cover", "is_edge_cover"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def min_edge_cover(G, matching_algorithm=None):
+    """Returns the min cardinality edge cover of the graph as a set of edges.
+
+    A smallest edge cover can be found in polynomial time by finding
+    a maximum matching and extending it greedily so that all nodes
+    are covered. This function follows that process. A maximum matching
+    algorithm can be specified for the first step of the algorithm.
+    The resulting set may return a set with one 2-tuple for each edge,
+    (the usual case) or with both 2-tuples `(u, v)` and `(v, u)` for
+    each edge. The latter is only done when a bipartite matching algorithm
+    is specified as `matching_algorithm`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    matching_algorithm : function
+        A function that returns a maximum cardinality matching for `G`.
+        The function must take one input, the graph `G`, and return
+        either a set of edges (with only one direction for the pair of nodes)
+        or a dictionary mapping each node to its mate. If not specified,
+        :func:`~networkx.algorithms.matching.max_weight_matching` is used.
+        Common bipartite matching functions include
+        :func:`~networkx.algorithms.bipartite.matching.hopcroft_karp_matching`
+        or
+        :func:`~networkx.algorithms.bipartite.matching.eppstein_matching`.
+
+    Returns
+    -------
+    min_cover : set
+
+        A set of the edges in a minimum edge cover in the form of tuples.
+        It contains only one of the equivalent 2-tuples `(u, v)` and `(v, u)`
+        for each edge. If a bipartite method is used to compute the matching,
+        the returned set contains both the 2-tuples `(u, v)` and `(v, u)`
+        for each edge of a minimum edge cover.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
+    >>> sorted(nx.min_edge_cover(G))
+    [(2, 1), (3, 0)]
+
+    Notes
+    -----
+    An edge cover of a graph is a set of edges such that every node of
+    the graph is incident to at least one edge of the set.
+    The minimum edge cover is an edge covering of smallest cardinality.
+
+    Due to its implementation, the worst-case running time of this algorithm
+    is bounded by the worst-case running time of the function
+    ``matching_algorithm``.
+
+    Minimum edge cover for `G` can also be found using
+    :func:`~networkx.algorithms.bipartite.covering.min_edge_covering` which is
+    simply this function with a default matching algorithm of
+    :func:`~networkx.algorithms.bipartite.matching.hopcroft_karp_matching`
+    """
+    if len(G) == 0:
+        return set()
+    if nx.number_of_isolates(G) > 0:
+        # ``min_cover`` does not exist as there is an isolated node
+        raise nx.NetworkXException(
+            "Graph has a node with no edge incident on it, so no edge cover exists."
+        )
+    if matching_algorithm is None:
+        matching_algorithm = partial(nx.max_weight_matching, maxcardinality=True)
+    maximum_matching = matching_algorithm(G)
+    # ``min_cover`` is superset of ``maximum_matching``
+    try:
+        # bipartite matching algs return dict so convert if needed
+        min_cover = set(maximum_matching.items())
+        bipartite_cover = True
+    except AttributeError:
+        min_cover = maximum_matching
+        bipartite_cover = False
+    # iterate for uncovered nodes
+    uncovered_nodes = set(G) - {v for u, v in min_cover} - {u for u, v in min_cover}
+    for v in uncovered_nodes:
+        # Since `v` is uncovered, each edge incident to `v` will join it
+        # with a covered node (otherwise, if there were an edge joining
+        # uncovered nodes `u` and `v`, the maximum matching algorithm
+        # would have found it), so we can choose an arbitrary edge
+        # incident to `v`. (This applies only in a simple graph, not a
+        # multigraph.)
+        u = arbitrary_element(G[v])
+        min_cover.add((u, v))
+        if bipartite_cover:
+            min_cover.add((v, u))
+    return min_cover
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def is_edge_cover(G, cover):
+    """Decides whether a set of edges is a valid edge cover of the graph.
+
+    Given a set of edges, whether it is an edge covering can
+    be decided if we just check whether all nodes of the graph
+    has an edge from the set, incident on it.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected bipartite graph.
+
+    cover : set
+        Set of edges to be checked.
+
+    Returns
+    -------
+    bool
+        Whether the set of edges is a valid edge cover of the graph.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
+    >>> cover = {(2, 1), (3, 0)}
+    >>> nx.is_edge_cover(G, cover)
+    True
+
+    Notes
+    -----
+    An edge cover of a graph is a set of edges such that every node of
+    the graph is incident to at least one edge of the set.
+    """
+    return set(G) <= set(chain.from_iterable(cover))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/cuts.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/cuts.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9514312765c25bfc64041165f8afda84d92e3e2
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/cuts.py
@@ -0,0 +1,398 @@
+"""Functions for finding and evaluating cuts in a graph."""
+
+from itertools import chain
+
+import networkx as nx
+
+__all__ = [
+    "boundary_expansion",
+    "conductance",
+    "cut_size",
+    "edge_expansion",
+    "mixing_expansion",
+    "node_expansion",
+    "normalized_cut_size",
+    "volume",
+]
+
+
+# TODO STILL NEED TO UPDATE ALL THE DOCUMENTATION!
+
+
+@nx._dispatchable(edge_attrs="weight")
+def cut_size(G, S, T=None, weight=None):
+    """Returns the size of the cut between two sets of nodes.
+
+    A *cut* is a partition of the nodes of a graph into two sets. The
+    *cut size* is the sum of the weights of the edges "between" the two
+    sets of nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    S : collection
+        A collection of nodes in `G`.
+
+    T : collection
+        A collection of nodes in `G`. If not specified, this is taken to
+        be the set complement of `S`.
+
+    weight : object
+        Edge attribute key to use as weight. If not specified, edges
+        have weight one.
+
+    Returns
+    -------
+    number
+        Total weight of all edges from nodes in set `S` to nodes in
+        set `T` (and, in the case of directed graphs, all edges from
+        nodes in `T` to nodes in `S`).
+
+    Examples
+    --------
+    In the graph with two cliques joined by a single edges, the natural
+    bipartition of the graph into two blocks, one for each clique,
+    yields a cut of weight one::
+
+        >>> G = nx.barbell_graph(3, 0)
+        >>> S = {0, 1, 2}
+        >>> T = {3, 4, 5}
+        >>> nx.cut_size(G, S, T)
+        1
+
+    Each parallel edge in a multigraph is counted when determining the
+    cut size::
+
+        >>> G = nx.MultiGraph(["ab", "ab"])
+        >>> S = {"a"}
+        >>> T = {"b"}
+        >>> nx.cut_size(G, S, T)
+        2
+
+    Notes
+    -----
+    In a multigraph, the cut size is the total weight of edges including
+    multiplicity.
+
+    """
+    edges = nx.edge_boundary(G, S, T, data=weight, default=1)
+    if G.is_directed():
+        edges = chain(edges, nx.edge_boundary(G, T, S, data=weight, default=1))
+    return sum(weight for u, v, weight in edges)
+
+
+@nx._dispatchable(edge_attrs="weight")
+def volume(G, S, weight=None):
+    """Returns the volume of a set of nodes.
+
+    The *volume* of a set *S* is the sum of the (out-)degrees of nodes
+    in *S* (taking into account parallel edges in multigraphs). [1]
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    S : collection
+        A collection of nodes in `G`.
+
+    weight : object
+        Edge attribute key to use as weight. If not specified, edges
+        have weight one.
+
+    Returns
+    -------
+    number
+        The volume of the set of nodes represented by `S` in the graph
+        `G`.
+
+    See also
+    --------
+    conductance
+    cut_size
+    edge_expansion
+    edge_boundary
+    normalized_cut_size
+
+    References
+    ----------
+    .. [1] David Gleich.
+           *Hierarchical Directed Spectral Graph Partitioning*.
+           <https://www.cs.purdue.edu/homes/dgleich/publications/Gleich%202005%20-%20hierarchical%20directed%20spectral.pdf>
+
+    """
+    degree = G.out_degree if G.is_directed() else G.degree
+    return sum(d for v, d in degree(S, weight=weight))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def normalized_cut_size(G, S, T=None, weight=None):
+    """Returns the normalized size of the cut between two sets of nodes.
+
+    The *normalized cut size* is the cut size times the sum of the
+    reciprocal sizes of the volumes of the two sets. [1]
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    S : collection
+        A collection of nodes in `G`.
+
+    T : collection
+        A collection of nodes in `G`.
+
+    weight : object
+        Edge attribute key to use as weight. If not specified, edges
+        have weight one.
+
+    Returns
+    -------
+    number
+        The normalized cut size between the two sets `S` and `T`.
+
+    Notes
+    -----
+    In a multigraph, the cut size is the total weight of edges including
+    multiplicity.
+
+    See also
+    --------
+    conductance
+    cut_size
+    edge_expansion
+    volume
+
+    References
+    ----------
+    .. [1] David Gleich.
+           *Hierarchical Directed Spectral Graph Partitioning*.
+           <https://www.cs.purdue.edu/homes/dgleich/publications/Gleich%202005%20-%20hierarchical%20directed%20spectral.pdf>
+
+    """
+    if T is None:
+        T = set(G) - set(S)
+    num_cut_edges = cut_size(G, S, T=T, weight=weight)
+    volume_S = volume(G, S, weight=weight)
+    volume_T = volume(G, T, weight=weight)
+    return num_cut_edges * ((1 / volume_S) + (1 / volume_T))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def conductance(G, S, T=None, weight=None):
+    """Returns the conductance of two sets of nodes.
+
+    The *conductance* is the quotient of the cut size and the smaller of
+    the volumes of the two sets. [1]
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    S : collection
+        A collection of nodes in `G`.
+
+    T : collection
+        A collection of nodes in `G`.
+
+    weight : object
+        Edge attribute key to use as weight. If not specified, edges
+        have weight one.
+
+    Returns
+    -------
+    number
+        The conductance between the two sets `S` and `T`.
+
+    See also
+    --------
+    cut_size
+    edge_expansion
+    normalized_cut_size
+    volume
+
+    References
+    ----------
+    .. [1] David Gleich.
+           *Hierarchical Directed Spectral Graph Partitioning*.
+           <https://www.cs.purdue.edu/homes/dgleich/publications/Gleich%202005%20-%20hierarchical%20directed%20spectral.pdf>
+
+    """
+    if T is None:
+        T = set(G) - set(S)
+    num_cut_edges = cut_size(G, S, T, weight=weight)
+    volume_S = volume(G, S, weight=weight)
+    volume_T = volume(G, T, weight=weight)
+    return num_cut_edges / min(volume_S, volume_T)
+
+
+@nx._dispatchable(edge_attrs="weight")
+def edge_expansion(G, S, T=None, weight=None):
+    """Returns the edge expansion between two node sets.
+
+    The *edge expansion* is the quotient of the cut size and the smaller
+    of the cardinalities of the two sets. [1]
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    S : collection
+        A collection of nodes in `G`.
+
+    T : collection
+        A collection of nodes in `G`.
+
+    weight : object
+        Edge attribute key to use as weight. If not specified, edges
+        have weight one.
+
+    Returns
+    -------
+    number
+        The edge expansion between the two sets `S` and `T`.
+
+    See also
+    --------
+    boundary_expansion
+    mixing_expansion
+    node_expansion
+
+    References
+    ----------
+    .. [1] Fan Chung.
+           *Spectral Graph Theory*.
+           (CBMS Regional Conference Series in Mathematics, No. 92),
+           American Mathematical Society, 1997, ISBN 0-8218-0315-8
+           <http://www.math.ucsd.edu/~fan/research/revised.html>
+
+    """
+    if T is None:
+        T = set(G) - set(S)
+    num_cut_edges = cut_size(G, S, T=T, weight=weight)
+    return num_cut_edges / min(len(S), len(T))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def mixing_expansion(G, S, T=None, weight=None):
+    """Returns the mixing expansion between two node sets.
+
+    The *mixing expansion* is the quotient of the cut size and twice the
+    number of edges in the graph. [1]
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    S : collection
+        A collection of nodes in `G`.
+
+    T : collection
+        A collection of nodes in `G`.
+
+    weight : object
+        Edge attribute key to use as weight. If not specified, edges
+        have weight one.
+
+    Returns
+    -------
+    number
+        The mixing expansion between the two sets `S` and `T`.
+
+    See also
+    --------
+    boundary_expansion
+    edge_expansion
+    node_expansion
+
+    References
+    ----------
+    .. [1] Vadhan, Salil P.
+           "Pseudorandomness."
+           *Foundations and Trends
+           in Theoretical Computer Science* 7.1–3 (2011): 1–336.
+           <https://doi.org/10.1561/0400000010>
+
+    """
+    num_cut_edges = cut_size(G, S, T=T, weight=weight)
+    num_total_edges = G.number_of_edges()
+    return num_cut_edges / (2 * num_total_edges)
+
+
+# TODO What is the generalization to two arguments, S and T? Does the
+# denominator become `min(len(S), len(T))`?
+@nx._dispatchable
+def node_expansion(G, S):
+    """Returns the node expansion of the set `S`.
+
+    The *node expansion* is the quotient of the size of the node
+    boundary of *S* and the cardinality of *S*. [1]
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    S : collection
+        A collection of nodes in `G`.
+
+    Returns
+    -------
+    number
+        The node expansion of the set `S`.
+
+    See also
+    --------
+    boundary_expansion
+    edge_expansion
+    mixing_expansion
+
+    References
+    ----------
+    .. [1] Vadhan, Salil P.
+           "Pseudorandomness."
+           *Foundations and Trends
+           in Theoretical Computer Science* 7.1–3 (2011): 1–336.
+           <https://doi.org/10.1561/0400000010>
+
+    """
+    neighborhood = set(chain.from_iterable(G.neighbors(v) for v in S))
+    return len(neighborhood) / len(S)
+
+
+# TODO What is the generalization to two arguments, S and T? Does the
+# denominator become `min(len(S), len(T))`?
+@nx._dispatchable
+def boundary_expansion(G, S):
+    """Returns the boundary expansion of the set `S`.
+
+    The *boundary expansion* is the quotient of the size
+    of the node boundary and the cardinality of *S*. [1]
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    S : collection
+        A collection of nodes in `G`.
+
+    Returns
+    -------
+    number
+        The boundary expansion of the set `S`.
+
+    See also
+    --------
+    edge_expansion
+    mixing_expansion
+    node_expansion
+
+    References
+    ----------
+    .. [1] Vadhan, Salil P.
+           "Pseudorandomness."
+           *Foundations and Trends in Theoretical Computer Science*
+           7.1–3 (2011): 1–336.
+           <https://doi.org/10.1561/0400000010>
+
+    """
+    return len(nx.node_boundary(G, S)) / len(S)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/cycles.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/cycles.py
new file mode 100644
index 0000000000000000000000000000000000000000..3890a20db504d30f98c950f1bb6c4f5f4bcc88ba
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/cycles.py
@@ -0,0 +1,1234 @@
+"""
+========================
+Cycle finding algorithms
+========================
+"""
+
+from collections import defaultdict
+from itertools import combinations, product
+from math import inf
+
+import networkx as nx
+from networkx.utils import not_implemented_for, pairwise
+
+__all__ = [
+    "cycle_basis",
+    "simple_cycles",
+    "recursive_simple_cycles",
+    "find_cycle",
+    "minimum_cycle_basis",
+    "chordless_cycles",
+    "girth",
+]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def cycle_basis(G, root=None):
+    """Returns a list of cycles which form a basis for cycles of G.
+
+    A basis for cycles of a network is a minimal collection of
+    cycles such that any cycle in the network can be written
+    as a sum of cycles in the basis.  Here summation of cycles
+    is defined as "exclusive or" of the edges. Cycle bases are
+    useful, e.g. when deriving equations for electric circuits
+    using Kirchhoff's Laws.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    root : node, optional
+       Specify starting node for basis.
+
+    Returns
+    -------
+    A list of cycle lists.  Each cycle list is a list of nodes
+    which forms a cycle (loop) in G.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_cycle(G, [0, 1, 2, 3])
+    >>> nx.add_cycle(G, [0, 3, 4, 5])
+    >>> nx.cycle_basis(G, 0)
+    [[3, 4, 5, 0], [1, 2, 3, 0]]
+
+    Notes
+    -----
+    This is adapted from algorithm CACM 491 [1]_.
+
+    References
+    ----------
+    .. [1] Paton, K. An algorithm for finding a fundamental set of
+       cycles of a graph. Comm. ACM 12, 9 (Sept 1969), 514-518.
+
+    See Also
+    --------
+    simple_cycles
+    minimum_cycle_basis
+    """
+    gnodes = dict.fromkeys(G)  # set-like object that maintains node order
+    cycles = []
+    while gnodes:  # loop over connected components
+        if root is None:
+            root = gnodes.popitem()[0]
+        stack = [root]
+        pred = {root: root}
+        used = {root: set()}
+        while stack:  # walk the spanning tree finding cycles
+            z = stack.pop()  # use last-in so cycles easier to find
+            zused = used[z]
+            for nbr in G[z]:
+                if nbr not in used:  # new node
+                    pred[nbr] = z
+                    stack.append(nbr)
+                    used[nbr] = {z}
+                elif nbr == z:  # self loops
+                    cycles.append([z])
+                elif nbr not in zused:  # found a cycle
+                    pn = used[nbr]
+                    cycle = [nbr, z]
+                    p = pred[z]
+                    while p not in pn:
+                        cycle.append(p)
+                        p = pred[p]
+                    cycle.append(p)
+                    cycles.append(cycle)
+                    used[nbr].add(z)
+        for node in pred:
+            gnodes.pop(node, None)
+        root = None
+    return cycles
+
+
+@nx._dispatchable
+def simple_cycles(G, length_bound=None):
+    """Find simple cycles (elementary circuits) of a graph.
+
+    A "simple cycle", or "elementary circuit", is a closed path where
+    no node appears twice.  In a directed graph, two simple cycles are distinct
+    if they are not cyclic permutations of each other.  In an undirected graph,
+    two simple cycles are distinct if they are not cyclic permutations of each
+    other nor of the other's reversal.
+
+    Optionally, the cycles are bounded in length.  In the unbounded case, we use
+    a nonrecursive, iterator/generator version of Johnson's algorithm [1]_.  In
+    the bounded case, we use a version of the algorithm of Gupta and
+    Suzumura [2]_. There may be better algorithms for some cases [3]_ [4]_ [5]_.
+
+    The algorithms of Johnson, and Gupta and Suzumura, are enhanced by some
+    well-known preprocessing techniques.  When `G` is directed, we restrict our
+    attention to strongly connected components of `G`, generate all simple cycles
+    containing a certain node, remove that node, and further decompose the
+    remainder into strongly connected components.  When `G` is undirected, we
+    restrict our attention to biconnected components, generate all simple cycles
+    containing a particular edge, remove that edge, and further decompose the
+    remainder into biconnected components.
+
+    Note that multigraphs are supported by this function -- and in undirected
+    multigraphs, a pair of parallel edges is considered a cycle of length 2.
+    Likewise, self-loops are considered to be cycles of length 1.  We define
+    cycles as sequences of nodes; so the presence of loops and parallel edges
+    does not change the number of simple cycles in a graph.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+       A networkx graph. Undirected, directed, and multigraphs are all supported.
+
+    length_bound : int or None, optional (default=None)
+       If `length_bound` is an int, generate all simple cycles of `G` with length at
+       most `length_bound`.  Otherwise, generate all simple cycles of `G`.
+
+    Yields
+    ------
+    list of nodes
+       Each cycle is represented by a list of nodes along the cycle.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(0, 0), (0, 1), (0, 2), (1, 2), (2, 0), (2, 1), (2, 2)])
+    >>> sorted(nx.simple_cycles(G))
+    [[0], [0, 1, 2], [0, 2], [1, 2], [2]]
+
+    To filter the cycles so that they don't include certain nodes or edges,
+    copy your graph and eliminate those nodes or edges before calling.
+    For example, to exclude self-loops from the above example:
+
+    >>> H = G.copy()
+    >>> H.remove_edges_from(nx.selfloop_edges(G))
+    >>> sorted(nx.simple_cycles(H))
+    [[0, 1, 2], [0, 2], [1, 2]]
+
+    Notes
+    -----
+    When `length_bound` is None, the time complexity is $O((n+e)(c+1))$ for $n$
+    nodes, $e$ edges and $c$ simple circuits.  Otherwise, when ``length_bound > 1``,
+    the time complexity is $O((c+n)(k-1)d^k)$ where $d$ is the average degree of
+    the nodes of `G` and $k$ = `length_bound`.
+
+    Raises
+    ------
+    ValueError
+        when ``length_bound < 0``.
+
+    References
+    ----------
+    .. [1] Finding all the elementary circuits of a directed graph.
+       D. B. Johnson, SIAM Journal on Computing 4, no. 1, 77-84, 1975.
+       https://doi.org/10.1137/0204007
+    .. [2] Finding All Bounded-Length Simple Cycles in a Directed Graph
+       A. Gupta and T. Suzumura https://arxiv.org/abs/2105.10094
+    .. [3] Enumerating the cycles of a digraph: a new preprocessing strategy.
+       G. Loizou and P. Thanish, Information Sciences, v. 27, 163-182, 1982.
+    .. [4] A search strategy for the elementary cycles of a directed graph.
+       J.L. Szwarcfiter and P.E. Lauer, BIT NUMERICAL MATHEMATICS,
+       v. 16, no. 2, 192-204, 1976.
+    .. [5] Optimal Listing of Cycles and st-Paths in Undirected Graphs
+        R. Ferreira and R. Grossi and A. Marino and N. Pisanti and R. Rizzi and
+        G. Sacomoto https://arxiv.org/abs/1205.2766
+
+    See Also
+    --------
+    cycle_basis
+    chordless_cycles
+    """
+
+    if length_bound is not None:
+        if length_bound == 0:
+            return
+        elif length_bound < 0:
+            raise ValueError("length bound must be non-negative")
+
+    directed = G.is_directed()
+    yield from ([v] for v, Gv in G.adj.items() if v in Gv)
+
+    if length_bound is not None and length_bound == 1:
+        return
+
+    if G.is_multigraph() and not directed:
+        visited = set()
+        for u, Gu in G.adj.items():
+            multiplicity = ((v, len(Guv)) for v, Guv in Gu.items() if v in visited)
+            yield from ([u, v] for v, m in multiplicity if m > 1)
+            visited.add(u)
+
+    # explicitly filter out loops; implicitly filter out parallel edges
+    if directed:
+        G = nx.DiGraph((u, v) for u, Gu in G.adj.items() for v in Gu if v != u)
+    else:
+        G = nx.Graph((u, v) for u, Gu in G.adj.items() for v in Gu if v != u)
+
+    # this case is not strictly necessary but improves performance
+    if length_bound is not None and length_bound == 2:
+        if directed:
+            visited = set()
+            for u, Gu in G.adj.items():
+                yield from (
+                    [v, u] for v in visited.intersection(Gu) if G.has_edge(v, u)
+                )
+                visited.add(u)
+        return
+
+    if directed:
+        yield from _directed_cycle_search(G, length_bound)
+    else:
+        yield from _undirected_cycle_search(G, length_bound)
+
+
+def _directed_cycle_search(G, length_bound):
+    """A dispatch function for `simple_cycles` for directed graphs.
+
+    We generate all cycles of G through binary partition.
+
+        1. Pick a node v in G which belongs to at least one cycle
+            a. Generate all cycles of G which contain the node v.
+            b. Recursively generate all cycles of G \\ v.
+
+    This is accomplished through the following:
+
+        1. Compute the strongly connected components SCC of G.
+        2. Select and remove a biconnected component C from BCC.  Select a
+           non-tree edge (u, v) of a depth-first search of G[C].
+        3. For each simple cycle P containing v in G[C], yield P.
+        4. Add the biconnected components of G[C \\ v] to BCC.
+
+    If the parameter length_bound is not None, then step 3 will be limited to
+    simple cycles of length at most length_bound.
+
+    Parameters
+    ----------
+    G : NetworkX DiGraph
+       A directed graph
+
+    length_bound : int or None
+       If length_bound is an int, generate all simple cycles of G with length at most length_bound.
+       Otherwise, generate all simple cycles of G.
+
+    Yields
+    ------
+    list of nodes
+       Each cycle is represented by a list of nodes along the cycle.
+    """
+
+    scc = nx.strongly_connected_components
+    components = [c for c in scc(G) if len(c) >= 2]
+    while components:
+        c = components.pop()
+        Gc = G.subgraph(c)
+        v = next(iter(c))
+        if length_bound is None:
+            yield from _johnson_cycle_search(Gc, [v])
+        else:
+            yield from _bounded_cycle_search(Gc, [v], length_bound)
+        # delete v after searching G, to make sure we can find v
+        G.remove_node(v)
+        components.extend(c for c in scc(Gc) if len(c) >= 2)
+
+
+def _undirected_cycle_search(G, length_bound):
+    """A dispatch function for `simple_cycles` for undirected graphs.
+
+    We generate all cycles of G through binary partition.
+
+        1. Pick an edge (u, v) in G which belongs to at least one cycle
+            a. Generate all cycles of G which contain the edge (u, v)
+            b. Recursively generate all cycles of G \\ (u, v)
+
+    This is accomplished through the following:
+
+        1. Compute the biconnected components BCC of G.
+        2. Select and remove a biconnected component C from BCC.  Select a
+           non-tree edge (u, v) of a depth-first search of G[C].
+        3. For each (v -> u) path P remaining in G[C] \\ (u, v), yield P.
+        4. Add the biconnected components of G[C] \\ (u, v) to BCC.
+
+    If the parameter length_bound is not None, then step 3 will be limited to simple paths
+    of length at most length_bound.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+       An undirected graph
+
+    length_bound : int or None
+       If length_bound is an int, generate all simple cycles of G with length at most length_bound.
+       Otherwise, generate all simple cycles of G.
+
+    Yields
+    ------
+    list of nodes
+       Each cycle is represented by a list of nodes along the cycle.
+    """
+
+    bcc = nx.biconnected_components
+    components = [c for c in bcc(G) if len(c) >= 3]
+    while components:
+        c = components.pop()
+        Gc = G.subgraph(c)
+        uv = list(next(iter(Gc.edges)))
+        G.remove_edge(*uv)
+        # delete (u, v) before searching G, to avoid fake 3-cycles [u, v, u]
+        if length_bound is None:
+            yield from _johnson_cycle_search(Gc, uv)
+        else:
+            yield from _bounded_cycle_search(Gc, uv, length_bound)
+        components.extend(c for c in bcc(Gc) if len(c) >= 3)
+
+
+class _NeighborhoodCache(dict):
+    """Very lightweight graph wrapper which caches neighborhoods as list.
+
+    This dict subclass uses the __missing__ functionality to query graphs for
+    their neighborhoods, and store the result as a list.  This is used to avoid
+    the performance penalty incurred by subgraph views.
+    """
+
+    def __init__(self, G):
+        self.G = G
+
+    def __missing__(self, v):
+        Gv = self[v] = list(self.G[v])
+        return Gv
+
+
+def _johnson_cycle_search(G, path):
+    """The main loop of the cycle-enumeration algorithm of Johnson.
+
+    Parameters
+    ----------
+    G : NetworkX Graph or DiGraph
+       A graph
+
+    path : list
+       A cycle prefix.  All cycles generated will begin with this prefix.
+
+    Yields
+    ------
+    list of nodes
+       Each cycle is represented by a list of nodes along the cycle.
+
+    References
+    ----------
+        .. [1] Finding all the elementary circuits of a directed graph.
+       D. B. Johnson, SIAM Journal on Computing 4, no. 1, 77-84, 1975.
+       https://doi.org/10.1137/0204007
+
+    """
+
+    G = _NeighborhoodCache(G)
+    blocked = set(path)
+    B = defaultdict(set)  # graph portions that yield no elementary circuit
+    start = path[0]
+    stack = [iter(G[path[-1]])]
+    closed = [False]
+    while stack:
+        nbrs = stack[-1]
+        for w in nbrs:
+            if w == start:
+                yield path[:]
+                closed[-1] = True
+            elif w not in blocked:
+                path.append(w)
+                closed.append(False)
+                stack.append(iter(G[w]))
+                blocked.add(w)
+                break
+        else:  # no more nbrs
+            stack.pop()
+            v = path.pop()
+            if closed.pop():
+                if closed:
+                    closed[-1] = True
+                unblock_stack = {v}
+                while unblock_stack:
+                    u = unblock_stack.pop()
+                    if u in blocked:
+                        blocked.remove(u)
+                        unblock_stack.update(B[u])
+                        B[u].clear()
+            else:
+                for w in G[v]:
+                    B[w].add(v)
+
+
+def _bounded_cycle_search(G, path, length_bound):
+    """The main loop of the cycle-enumeration algorithm of Gupta and Suzumura.
+
+    Parameters
+    ----------
+    G : NetworkX Graph or DiGraph
+       A graph
+
+    path : list
+       A cycle prefix.  All cycles generated will begin with this prefix.
+
+    length_bound: int
+        A length bound.  All cycles generated will have length at most length_bound.
+
+    Yields
+    ------
+    list of nodes
+       Each cycle is represented by a list of nodes along the cycle.
+
+    References
+    ----------
+    .. [1] Finding All Bounded-Length Simple Cycles in a Directed Graph
+       A. Gupta and T. Suzumura https://arxiv.org/abs/2105.10094
+
+    """
+    G = _NeighborhoodCache(G)
+    lock = dict.fromkeys(path, 0)
+    B = defaultdict(set)
+    start = path[0]
+    stack = [iter(G[path[-1]])]
+    blen = [length_bound]
+    while stack:
+        nbrs = stack[-1]
+        for w in nbrs:
+            if w == start:
+                yield path[:]
+                blen[-1] = 1
+            elif len(path) < lock.get(w, length_bound):
+                path.append(w)
+                blen.append(length_bound)
+                lock[w] = len(path)
+                stack.append(iter(G[w]))
+                break
+        else:
+            stack.pop()
+            v = path.pop()
+            bl = blen.pop()
+            if blen:
+                blen[-1] = min(blen[-1], bl)
+            if bl < length_bound:
+                relax_stack = [(bl, v)]
+                while relax_stack:
+                    bl, u = relax_stack.pop()
+                    if lock.get(u, length_bound) < length_bound - bl + 1:
+                        lock[u] = length_bound - bl + 1
+                        relax_stack.extend((bl + 1, w) for w in B[u].difference(path))
+            else:
+                for w in G[v]:
+                    B[w].add(v)
+
+
+@nx._dispatchable
+def chordless_cycles(G, length_bound=None):
+    """Find simple chordless cycles of a graph.
+
+    A `simple cycle` is a closed path where no node appears twice.  In a simple
+    cycle, a `chord` is an additional edge between two nodes in the cycle.  A
+    `chordless cycle` is a simple cycle without chords.  Said differently, a
+    chordless cycle is a cycle C in a graph G where the number of edges in the
+    induced graph G[C] is equal to the length of `C`.
+
+    Note that some care must be taken in the case that G is not a simple graph
+    nor a simple digraph.  Some authors limit the definition of chordless cycles
+    to have a prescribed minimum length; we do not.
+
+        1. We interpret self-loops to be chordless cycles, except in multigraphs
+           with multiple loops in parallel.  Likewise, in a chordless cycle of
+           length greater than 1, there can be no nodes with self-loops.
+
+        2. We interpret directed two-cycles to be chordless cycles, except in
+           multi-digraphs when any edge in a two-cycle has a parallel copy.
+
+        3. We interpret parallel pairs of undirected edges as two-cycles, except
+           when a third (or more) parallel edge exists between the two nodes.
+
+        4. Generalizing the above, edges with parallel clones may not occur in
+           chordless cycles.
+
+    In a directed graph, two chordless cycles are distinct if they are not
+    cyclic permutations of each other.  In an undirected graph, two chordless
+    cycles are distinct if they are not cyclic permutations of each other nor of
+    the other's reversal.
+
+    Optionally, the cycles are bounded in length.
+
+    We use an algorithm strongly inspired by that of Dias et al [1]_.  It has
+    been modified in the following ways:
+
+        1. Recursion is avoided, per Python's limitations
+
+        2. The labeling function is not necessary, because the starting paths
+            are chosen (and deleted from the host graph) to prevent multiple
+            occurrences of the same path
+
+        3. The search is optionally bounded at a specified length
+
+        4. Support for directed graphs is provided by extending cycles along
+            forward edges, and blocking nodes along forward and reverse edges
+
+        5. Support for multigraphs is provided by omitting digons from the set
+            of forward edges
+
+    Parameters
+    ----------
+    G : NetworkX DiGraph
+       A directed graph
+
+    length_bound : int or None, optional (default=None)
+       If length_bound is an int, generate all simple cycles of G with length at
+       most length_bound.  Otherwise, generate all simple cycles of G.
+
+    Yields
+    ------
+    list of nodes
+       Each cycle is represented by a list of nodes along the cycle.
+
+    Examples
+    --------
+    >>> sorted(list(nx.chordless_cycles(nx.complete_graph(4))))
+    [[1, 0, 2], [1, 0, 3], [2, 0, 3], [2, 1, 3]]
+
+    Notes
+    -----
+    When length_bound is None, and the graph is simple, the time complexity is
+    $O((n+e)(c+1))$ for $n$ nodes, $e$ edges and $c$ chordless cycles.
+
+    Raises
+    ------
+    ValueError
+        when length_bound < 0.
+
+    References
+    ----------
+    .. [1] Efficient enumeration of chordless cycles
+       E. Dias and D. Castonguay and H. Longo and W.A.R. Jradi
+       https://arxiv.org/abs/1309.1051
+
+    See Also
+    --------
+    simple_cycles
+    """
+
+    if length_bound is not None:
+        if length_bound == 0:
+            return
+        elif length_bound < 0:
+            raise ValueError("length bound must be non-negative")
+
+    directed = G.is_directed()
+    multigraph = G.is_multigraph()
+
+    if multigraph:
+        yield from ([v] for v, Gv in G.adj.items() if len(Gv.get(v, ())) == 1)
+    else:
+        yield from ([v] for v, Gv in G.adj.items() if v in Gv)
+
+    if length_bound is not None and length_bound == 1:
+        return
+
+    # Nodes with loops cannot belong to longer cycles.  Let's delete them here.
+    # also, we implicitly reduce the multiplicity of edges down to 1 in the case
+    # of multiedges.
+    loops = set(nx.nodes_with_selfloops(G))
+    edges = ((u, v) for u in G if u not in loops for v in G._adj[u] if v not in loops)
+    if directed:
+        F = nx.DiGraph(edges)
+        B = F.to_undirected(as_view=False)
+    else:
+        F = nx.Graph(edges)
+        B = None
+
+    # If we're given a multigraph, we have a few cases to consider with parallel
+    # edges.
+    #
+    # 1. If we have 2 or more edges in parallel between the nodes (u, v), we
+    #    must not construct longer cycles along (u, v).
+    # 2. If G is not directed, then a pair of parallel edges between (u, v) is a
+    #    chordless cycle unless there exists a third (or more) parallel edge.
+    # 3. If G is directed, then parallel edges do not form cycles, but do
+    #    preclude back-edges from forming cycles (handled in the next section),
+    #    Thus, if an edge (u, v) is duplicated and the reverse (v, u) is also
+    #    present, then we remove both from F.
+    #
+    # In directed graphs, we need to consider both directions that edges can
+    # take, so iterate over all edges (u, v) and possibly (v, u).  In undirected
+    # graphs, we need to be a little careful to only consider every edge once,
+    # so we use a "visited" set to emulate node-order comparisons.
+
+    if multigraph:
+        if not directed:
+            B = F.copy()
+            visited = set()
+        for u, Gu in G.adj.items():
+            if u in loops:
+                continue
+            if directed:
+                multiplicity = ((v, len(Guv)) for v, Guv in Gu.items())
+                for v, m in multiplicity:
+                    if m > 1:
+                        F.remove_edges_from(((u, v), (v, u)))
+            else:
+                multiplicity = ((v, len(Guv)) for v, Guv in Gu.items() if v in visited)
+                for v, m in multiplicity:
+                    if m == 2:
+                        yield [u, v]
+                    if m > 1:
+                        F.remove_edge(u, v)
+                visited.add(u)
+
+    # If we're given a directed graphs, we need to think about digons.  If we
+    # have two edges (u, v) and (v, u), then that's a two-cycle.  If either edge
+    # was duplicated above, then we removed both from F.  So, any digons we find
+    # here are chordless.  After finding digons, we remove their edges from F
+    # to avoid traversing them in the search for chordless cycles.
+    if directed:
+        for u, Fu in F.adj.items():
+            digons = [[u, v] for v in Fu if F.has_edge(v, u)]
+            yield from digons
+            F.remove_edges_from(digons)
+            F.remove_edges_from(e[::-1] for e in digons)
+
+    if length_bound is not None and length_bound == 2:
+        return
+
+    # Now, we prepare to search for cycles.  We have removed all cycles of
+    # lengths 1 and 2, so F is a simple graph or simple digraph.  We repeatedly
+    # separate digraphs into their strongly connected components, and undirected
+    # graphs into their biconnected components.  For each component, we pick a
+    # node v, search for chordless cycles based at each "stem" (u, v, w), and
+    # then remove v from that component before separating the graph again.
+    if directed:
+        separate = nx.strongly_connected_components
+
+        # Directed stems look like (u -> v -> w), so we use the product of
+        # predecessors of v with successors of v.
+        def stems(C, v):
+            for u, w in product(C.pred[v], C.succ[v]):
+                if not G.has_edge(u, w):  # omit stems with acyclic chords
+                    yield [u, v, w], F.has_edge(w, u)
+
+    else:
+        separate = nx.biconnected_components
+
+        # Undirected stems look like (u ~ v ~ w), but we must not also search
+        # (w ~ v ~ u), so we use combinations of v's neighbors of length 2.
+        def stems(C, v):
+            yield from (([u, v, w], F.has_edge(w, u)) for u, w in combinations(C[v], 2))
+
+    components = [c for c in separate(F) if len(c) > 2]
+    while components:
+        c = components.pop()
+        v = next(iter(c))
+        Fc = F.subgraph(c)
+        Fcc = Bcc = None
+        for S, is_triangle in stems(Fc, v):
+            if is_triangle:
+                yield S
+            else:
+                if Fcc is None:
+                    Fcc = _NeighborhoodCache(Fc)
+                    Bcc = Fcc if B is None else _NeighborhoodCache(B.subgraph(c))
+                yield from _chordless_cycle_search(Fcc, Bcc, S, length_bound)
+
+        components.extend(c for c in separate(F.subgraph(c - {v})) if len(c) > 2)
+
+
+def _chordless_cycle_search(F, B, path, length_bound):
+    """The main loop for chordless cycle enumeration.
+
+    This algorithm is strongly inspired by that of Dias et al [1]_.  It has been
+    modified in the following ways:
+
+        1. Recursion is avoided, per Python's limitations
+
+        2. The labeling function is not necessary, because the starting paths
+            are chosen (and deleted from the host graph) to prevent multiple
+            occurrences of the same path
+
+        3. The search is optionally bounded at a specified length
+
+        4. Support for directed graphs is provided by extending cycles along
+            forward edges, and blocking nodes along forward and reverse edges
+
+        5. Support for multigraphs is provided by omitting digons from the set
+            of forward edges
+
+    Parameters
+    ----------
+    F : _NeighborhoodCache
+       A graph of forward edges to follow in constructing cycles
+
+    B : _NeighborhoodCache
+       A graph of blocking edges to prevent the production of chordless cycles
+
+    path : list
+       A cycle prefix.  All cycles generated will begin with this prefix.
+
+    length_bound : int
+       A length bound.  All cycles generated will have length at most length_bound.
+
+
+    Yields
+    ------
+    list of nodes
+       Each cycle is represented by a list of nodes along the cycle.
+
+    References
+    ----------
+    .. [1] Efficient enumeration of chordless cycles
+       E. Dias and D. Castonguay and H. Longo and W.A.R. Jradi
+       https://arxiv.org/abs/1309.1051
+
+    """
+    blocked = defaultdict(int)
+    target = path[0]
+    blocked[path[1]] = 1
+    for w in path[1:]:
+        for v in B[w]:
+            blocked[v] += 1
+
+    stack = [iter(F[path[2]])]
+    while stack:
+        nbrs = stack[-1]
+        for w in nbrs:
+            if blocked[w] == 1 and (length_bound is None or len(path) < length_bound):
+                Fw = F[w]
+                if target in Fw:
+                    yield path + [w]
+                else:
+                    Bw = B[w]
+                    if target in Bw:
+                        continue
+                    for v in Bw:
+                        blocked[v] += 1
+                    path.append(w)
+                    stack.append(iter(Fw))
+                    break
+        else:
+            stack.pop()
+            for v in B[path.pop()]:
+                blocked[v] -= 1
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable(mutates_input=True)
+def recursive_simple_cycles(G):
+    """Find simple cycles (elementary circuits) of a directed graph.
+
+    A `simple cycle`, or `elementary circuit`, is a closed path where
+    no node appears twice. Two elementary circuits are distinct if they
+    are not cyclic permutations of each other.
+
+    This version uses a recursive algorithm to build a list of cycles.
+    You should probably use the iterator version called simple_cycles().
+    Warning: This recursive version uses lots of RAM!
+    It appears in NetworkX for pedagogical value.
+
+    Parameters
+    ----------
+    G : NetworkX DiGraph
+       A directed graph
+
+    Returns
+    -------
+    A list of cycles, where each cycle is represented by a list of nodes
+    along the cycle.
+
+    Example:
+
+    >>> edges = [(0, 0), (0, 1), (0, 2), (1, 2), (2, 0), (2, 1), (2, 2)]
+    >>> G = nx.DiGraph(edges)
+    >>> nx.recursive_simple_cycles(G)
+    [[0], [2], [0, 1, 2], [0, 2], [1, 2]]
+
+    Notes
+    -----
+    The implementation follows pp. 79-80 in [1]_.
+
+    The time complexity is $O((n+e)(c+1))$ for $n$ nodes, $e$ edges and $c$
+    elementary circuits.
+
+    References
+    ----------
+    .. [1] Finding all the elementary circuits of a directed graph.
+       D. B. Johnson, SIAM Journal on Computing 4, no. 1, 77-84, 1975.
+       https://doi.org/10.1137/0204007
+
+    See Also
+    --------
+    simple_cycles, cycle_basis
+    """
+
+    # Jon Olav Vik, 2010-08-09
+    def _unblock(thisnode):
+        """Recursively unblock and remove nodes from B[thisnode]."""
+        if blocked[thisnode]:
+            blocked[thisnode] = False
+            while B[thisnode]:
+                _unblock(B[thisnode].pop())
+
+    def circuit(thisnode, startnode, component):
+        closed = False  # set to True if elementary path is closed
+        path.append(thisnode)
+        blocked[thisnode] = True
+        for nextnode in component[thisnode]:  # direct successors of thisnode
+            if nextnode == startnode:
+                result.append(path[:])
+                closed = True
+            elif not blocked[nextnode]:
+                if circuit(nextnode, startnode, component):
+                    closed = True
+        if closed:
+            _unblock(thisnode)
+        else:
+            for nextnode in component[thisnode]:
+                if thisnode not in B[nextnode]:  # TODO: use set for speedup?
+                    B[nextnode].append(thisnode)
+        path.pop()  # remove thisnode from path
+        return closed
+
+    path = []  # stack of nodes in current path
+    blocked = defaultdict(bool)  # vertex: blocked from search?
+    B = defaultdict(list)  # graph portions that yield no elementary circuit
+    result = []  # list to accumulate the circuits found
+
+    # Johnson's algorithm exclude self cycle edges like (v, v)
+    # To be backward compatible, we record those cycles in advance
+    # and then remove from subG
+    for v in G:
+        if G.has_edge(v, v):
+            result.append([v])
+            G.remove_edge(v, v)
+
+    # Johnson's algorithm requires some ordering of the nodes.
+    # They might not be sortable so we assign an arbitrary ordering.
+    ordering = dict(zip(G, range(len(G))))
+    for s in ordering:
+        # Build the subgraph induced by s and following nodes in the ordering
+        subgraph = G.subgraph(node for node in G if ordering[node] >= ordering[s])
+        # Find the strongly connected component in the subgraph
+        # that contains the least node according to the ordering
+        strongcomp = nx.strongly_connected_components(subgraph)
+        mincomp = min(strongcomp, key=lambda ns: min(ordering[n] for n in ns))
+        component = G.subgraph(mincomp)
+        if len(component) > 1:
+            # smallest node in the component according to the ordering
+            startnode = min(component, key=ordering.__getitem__)
+            for node in component:
+                blocked[node] = False
+                B[node][:] = []
+            dummy = circuit(startnode, startnode, component)
+    return result
+
+
+@nx._dispatchable
+def find_cycle(G, source=None, orientation=None):
+    """Returns a cycle found via depth-first traversal.
+
+    The cycle is a list of edges indicating the cyclic path.
+    Orientation of directed edges is controlled by `orientation`.
+
+    Parameters
+    ----------
+    G : graph
+        A directed/undirected graph/multigraph.
+
+    source : node, list of nodes
+        The node from which the traversal begins. If None, then a source
+        is chosen arbitrarily and repeatedly until all edges from each node in
+        the graph are searched.
+
+    orientation : None | 'original' | 'reverse' | 'ignore' (default: None)
+        For directed graphs and directed multigraphs, edge traversals need not
+        respect the original orientation of the edges.
+        When set to 'reverse' every edge is traversed in the reverse direction.
+        When set to 'ignore', every edge is treated as undirected.
+        When set to 'original', every edge is treated as directed.
+        In all three cases, the yielded edge tuples add a last entry to
+        indicate the direction in which that edge was traversed.
+        If orientation is None, the yielded edge has no direction indicated.
+        The direction is respected, but not reported.
+
+    Returns
+    -------
+    edges : directed edges
+        A list of directed edges indicating the path taken for the loop.
+        If no cycle is found, then an exception is raised.
+        For graphs, an edge is of the form `(u, v)` where `u` and `v`
+        are the tail and head of the edge as determined by the traversal.
+        For multigraphs, an edge is of the form `(u, v, key)`, where `key` is
+        the key of the edge. When the graph is directed, then `u` and `v`
+        are always in the order of the actual directed edge.
+        If orientation is not None then the edge tuple is extended to include
+        the direction of traversal ('forward' or 'reverse') on that edge.
+
+    Raises
+    ------
+    NetworkXNoCycle
+        If no cycle was found.
+
+    Examples
+    --------
+    In this example, we construct a DAG and find, in the first call, that there
+    are no directed cycles, and so an exception is raised. In the second call,
+    we ignore edge orientations and find that there is an undirected cycle.
+    Note that the second call finds a directed cycle while effectively
+    traversing an undirected graph, and so, we found an "undirected cycle".
+    This means that this DAG structure does not form a directed tree (which
+    is also known as a polytree).
+
+    >>> G = nx.DiGraph([(0, 1), (0, 2), (1, 2)])
+    >>> nx.find_cycle(G, orientation="original")
+    Traceback (most recent call last):
+        ...
+    networkx.exception.NetworkXNoCycle: No cycle found.
+    >>> list(nx.find_cycle(G, orientation="ignore"))
+    [(0, 1, 'forward'), (1, 2, 'forward'), (0, 2, 'reverse')]
+
+    See Also
+    --------
+    simple_cycles
+    """
+    if not G.is_directed() or orientation in (None, "original"):
+
+        def tailhead(edge):
+            return edge[:2]
+
+    elif orientation == "reverse":
+
+        def tailhead(edge):
+            return edge[1], edge[0]
+
+    elif orientation == "ignore":
+
+        def tailhead(edge):
+            if edge[-1] == "reverse":
+                return edge[1], edge[0]
+            return edge[:2]
+
+    explored = set()
+    cycle = []
+    final_node = None
+    for start_node in G.nbunch_iter(source):
+        if start_node in explored:
+            # No loop is possible.
+            continue
+
+        edges = []
+        # All nodes seen in this iteration of edge_dfs
+        seen = {start_node}
+        # Nodes in active path.
+        active_nodes = {start_node}
+        previous_head = None
+
+        for edge in nx.edge_dfs(G, start_node, orientation):
+            # Determine if this edge is a continuation of the active path.
+            tail, head = tailhead(edge)
+            if head in explored:
+                # Then we've already explored it. No loop is possible.
+                continue
+            if previous_head is not None and tail != previous_head:
+                # This edge results from backtracking.
+                # Pop until we get a node whose head equals the current tail.
+                # So for example, we might have:
+                #  (0, 1), (1, 2), (2, 3), (1, 4)
+                # which must become:
+                #  (0, 1), (1, 4)
+                while True:
+                    try:
+                        popped_edge = edges.pop()
+                    except IndexError:
+                        edges = []
+                        active_nodes = {tail}
+                        break
+                    else:
+                        popped_head = tailhead(popped_edge)[1]
+                        active_nodes.remove(popped_head)
+
+                    if edges:
+                        last_head = tailhead(edges[-1])[1]
+                        if tail == last_head:
+                            break
+            edges.append(edge)
+
+            if head in active_nodes:
+                # We have a loop!
+                cycle.extend(edges)
+                final_node = head
+                break
+            else:
+                seen.add(head)
+                active_nodes.add(head)
+                previous_head = head
+
+        if cycle:
+            break
+        else:
+            explored.update(seen)
+
+    else:
+        assert len(cycle) == 0
+        raise nx.exception.NetworkXNoCycle("No cycle found.")
+
+    # We now have a list of edges which ends on a cycle.
+    # So we need to remove from the beginning edges that are not relevant.
+
+    for i, edge in enumerate(cycle):
+        tail, head = tailhead(edge)
+        if tail == final_node:
+            break
+
+    return cycle[i:]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(edge_attrs="weight")
+def minimum_cycle_basis(G, weight=None):
+    """Returns a minimum weight cycle basis for G
+
+    Minimum weight means a cycle basis for which the total weight
+    (length for unweighted graphs) of all the cycles is minimum.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    weight: string
+        name of the edge attribute to use for edge weights
+
+    Returns
+    -------
+    A list of cycle lists.  Each cycle list is a list of nodes
+    which forms a cycle (loop) in G. Note that the nodes are not
+    necessarily returned in a order by which they appear in the cycle
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_cycle(G, [0, 1, 2, 3])
+    >>> nx.add_cycle(G, [0, 3, 4, 5])
+    >>> nx.minimum_cycle_basis(G)
+    [[5, 4, 3, 0], [3, 2, 1, 0]]
+
+    References:
+        [1] Kavitha, Telikepalli, et al. "An O(m^2n) Algorithm for
+        Minimum Cycle Basis of Graphs."
+        http://link.springer.com/article/10.1007/s00453-007-9064-z
+        [2] de Pina, J. 1995. Applications of shortest path methods.
+        Ph.D. thesis, University of Amsterdam, Netherlands
+
+    See Also
+    --------
+    simple_cycles, cycle_basis
+    """
+    # We first split the graph in connected subgraphs
+    return sum(
+        (_min_cycle_basis(G.subgraph(c), weight) for c in nx.connected_components(G)),
+        [],
+    )
+
+
+def _min_cycle_basis(G, weight):
+    cb = []
+    # We  extract the edges not in a spanning tree. We do not really need a
+    # *minimum* spanning tree. That is why we call the next function with
+    # weight=None. Depending on implementation, it may be faster as well
+    tree_edges = list(nx.minimum_spanning_edges(G, weight=None, data=False))
+    chords = G.edges - tree_edges - {(v, u) for u, v in tree_edges}
+
+    # We maintain a set of vectors orthogonal to sofar found cycles
+    set_orth = [{edge} for edge in chords]
+    while set_orth:
+        base = set_orth.pop()
+        # kth cycle is "parallel" to kth vector in set_orth
+        cycle_edges = _min_cycle(G, base, weight)
+        cb.append([v for u, v in cycle_edges])
+
+        # now update set_orth so that k+1,k+2... th elements are
+        # orthogonal to the newly found cycle, as per [p. 336, 1]
+        set_orth = [
+            (
+                {e for e in orth if e not in base if e[::-1] not in base}
+                | {e for e in base if e not in orth if e[::-1] not in orth}
+            )
+            if sum((e in orth or e[::-1] in orth) for e in cycle_edges) % 2
+            else orth
+            for orth in set_orth
+        ]
+    return cb
+
+
+def _min_cycle(G, orth, weight):
+    """
+    Computes the minimum weight cycle in G,
+    orthogonal to the vector orth as per [p. 338, 1]
+    Use (u, 1) to indicate the lifted copy of u (denoted u' in paper).
+    """
+    Gi = nx.Graph()
+
+    # Add 2 copies of each edge in G to Gi.
+    # If edge is in orth, add cross edge; otherwise in-plane edge
+    for u, v, wt in G.edges(data=weight, default=1):
+        if (u, v) in orth or (v, u) in orth:
+            Gi.add_edges_from([(u, (v, 1)), ((u, 1), v)], Gi_weight=wt)
+        else:
+            Gi.add_edges_from([(u, v), ((u, 1), (v, 1))], Gi_weight=wt)
+
+    # find the shortest length in Gi between n and (n, 1) for each n
+    # Note: Use "Gi_weight" for name of weight attribute
+    spl = nx.shortest_path_length
+    lift = {n: spl(Gi, source=n, target=(n, 1), weight="Gi_weight") for n in G}
+
+    # Now compute that short path in Gi, which translates to a cycle in G
+    start = min(lift, key=lift.get)
+    end = (start, 1)
+    min_path_i = nx.shortest_path(Gi, source=start, target=end, weight="Gi_weight")
+
+    # Now we obtain the actual path, re-map nodes in Gi to those in G
+    min_path = [n if n in G else n[0] for n in min_path_i]
+
+    # Now remove the edges that occur two times
+    # two passes: flag which edges get kept, then build it
+    edgelist = list(pairwise(min_path))
+    edgeset = set()
+    for e in edgelist:
+        if e in edgeset:
+            edgeset.remove(e)
+        elif e[::-1] in edgeset:
+            edgeset.remove(e[::-1])
+        else:
+            edgeset.add(e)
+
+    min_edgelist = []
+    for e in edgelist:
+        if e in edgeset:
+            min_edgelist.append(e)
+            edgeset.remove(e)
+        elif e[::-1] in edgeset:
+            min_edgelist.append(e[::-1])
+            edgeset.remove(e[::-1])
+
+    return min_edgelist
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def girth(G):
+    """Returns the girth of the graph.
+
+    The girth of a graph is the length of its shortest cycle, or infinity if
+    the graph is acyclic. The algorithm follows the description given on the
+    Wikipedia page [1]_, and runs in time O(mn) on a graph with m edges and n
+    nodes.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    Returns
+    -------
+    int or math.inf
+
+    Examples
+    --------
+    All examples below (except P_5) can easily be checked using Wikipedia,
+    which has a page for each of these famous graphs.
+
+    >>> nx.girth(nx.chvatal_graph())
+    4
+    >>> nx.girth(nx.tutte_graph())
+    4
+    >>> nx.girth(nx.petersen_graph())
+    5
+    >>> nx.girth(nx.heawood_graph())
+    6
+    >>> nx.girth(nx.pappus_graph())
+    6
+    >>> nx.girth(nx.path_graph(5))
+    inf
+
+    References
+    ----------
+    .. [1] `Wikipedia: Girth <https://en.wikipedia.org/wiki/Girth_(graph_theory)>`_
+
+    """
+    girth = depth_limit = inf
+    tree_edge = nx.algorithms.traversal.breadth_first_search.TREE_EDGE
+    level_edge = nx.algorithms.traversal.breadth_first_search.LEVEL_EDGE
+    for n in G:
+        # run a BFS from source n, keeping track of distances; since we want
+        # the shortest cycle, no need to explore beyond the current minimum length
+        depth = {n: 0}
+        for u, v, label in nx.bfs_labeled_edges(G, n):
+            du = depth[u]
+            if du > depth_limit:
+                break
+            if label is tree_edge:
+                depth[v] = du + 1
+            else:
+                # if (u, v) is a level edge, the length is du + du + 1 (odd)
+                # otherwise, it's a forward edge; length is du + (du + 1) + 1 (even)
+                delta = label is level_edge
+                length = du + du + 2 - delta
+                if length < girth:
+                    girth = length
+                    depth_limit = du - delta
+
+    return girth
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/d_separation.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/d_separation.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d85a2c724fa0a1e14ab99db2be7a3c46b404b4a
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/d_separation.py
@@ -0,0 +1,677 @@
+"""
+Algorithm for testing d-separation in DAGs.
+
+*d-separation* is a test for conditional independence in probability
+distributions that can be factorized using DAGs.  It is a purely
+graphical test that uses the underlying graph and makes no reference
+to the actual distribution parameters.  See [1]_ for a formal
+definition.
+
+The implementation is based on the conceptually simple linear time
+algorithm presented in [2]_.  Refer to [3]_, [4]_ for a couple of
+alternative algorithms.
+
+The functional interface in NetworkX consists of three functions:
+
+- `find_minimal_d_separator` returns a minimal d-separator set ``z``.
+  That is, removing any node or nodes from it makes it no longer a d-separator.
+- `is_d_separator` checks if a given set is a d-separator.
+- `is_minimal_d_separator` checks if a given set is a minimal d-separator.
+
+D-separators
+------------
+
+Here, we provide a brief overview of d-separation and related concepts that
+are relevant for understanding it:
+
+The ideas of d-separation and d-connection relate to paths being open or blocked.
+
+- A "path" is a sequence of nodes connected in order by edges. Unlike for most
+  graph theory analysis, the direction of the edges is ignored. Thus the path
+  can be thought of as a traditional path on the undirected version of the graph.
+- A "candidate d-separator" ``z`` is a set of nodes being considered as
+  possibly blocking all paths between two prescribed sets ``x`` and ``y`` of nodes.
+  We refer to each node in the candidate d-separator as "known".
+- A "collider" node on a path is a node that is a successor of its two neighbor
+  nodes on the path. That is, ``c`` is a collider if the edge directions
+  along the path look like ``... u -> c <- v ...``.
+- If a collider node or any of its descendants are "known", the collider
+  is called an "open collider". Otherwise it is a "blocking collider".
+- Any path can be "blocked" in two ways. If the path contains a "known" node
+  that is not a collider, the path is blocked. Also, if the path contains a
+  collider that is not a "known" node, the path is blocked.
+- A path is "open" if it is not blocked. That is, it is open if every node is
+  either an open collider or not a "known". Said another way, every
+  "known" in the path is a collider and every collider is open (has a
+  "known" as a inclusive descendant). The concept of "open path" is meant to
+  demonstrate a probabilistic conditional dependence between two nodes given
+  prescribed knowledge ("known" nodes).
+- Two sets ``x`` and ``y`` of nodes are "d-separated" by a set of nodes ``z``
+  if all paths between nodes in ``x`` and nodes in ``y`` are blocked. That is,
+  if there are no open paths from any node in ``x`` to any node in ``y``.
+  Such a set ``z`` is a "d-separator" of ``x`` and ``y``.
+- A "minimal d-separator" is a d-separator ``z`` for which no node or subset
+  of nodes can be removed with it still being a d-separator.
+
+The d-separator blocks some paths between ``x`` and ``y`` but opens others.
+Nodes in the d-separator block paths if the nodes are not colliders.
+But if a collider or its descendant nodes are in the d-separation set, the
+colliders are open, allowing a path through that collider.
+
+Illustration of D-separation with examples
+------------------------------------------
+
+A pair of two nodes, ``u`` and ``v``, are d-connected if there is a path
+from ``u`` to ``v`` that is not blocked. That means, there is an open
+path from ``u`` to ``v``.
+
+For example, if the d-separating set is the empty set, then the following paths are
+open between ``u`` and ``v``:
+
+- u <- n -> v
+- u -> w -> ... -> n -> v
+
+If  on the other hand, ``n`` is in the d-separating set, then ``n`` blocks
+those paths between ``u`` and ``v``.
+
+Colliders block a path if they and their descendants are not included
+in the d-separating set. An example of a path that is blocked when the
+d-separating set is empty is:
+
+- u -> w -> ... -> n <- v
+
+The node ``n`` is a collider in this path and is not in the d-separating set.
+So ``n`` blocks this path. However, if ``n`` or a descendant of ``n`` is
+included in the d-separating set, then the path through the collider
+at ``n`` (... -> n <- ...) is "open".
+
+D-separation is concerned with blocking all paths between nodes from ``x`` to ``y``.
+A d-separating set between ``x`` and ``y`` is one where all paths are blocked.
+
+D-separation and its applications in probability
+------------------------------------------------
+
+D-separation is commonly used in probabilistic causal-graph models. D-separation
+connects the idea of probabilistic "dependence" with separation in a graph. If
+one assumes the causal Markov condition [5]_, (every node is conditionally
+independent of its non-descendants, given its parents) then d-separation implies
+conditional independence in probability distributions.
+Symmetrically, d-connection implies dependence.
+
+The intuition is as follows. The edges on a causal graph indicate which nodes
+influence the outcome of other nodes directly. An edge from u to v
+implies that the outcome of event ``u`` influences the probabilities for
+the outcome of event ``v``. Certainly knowing ``u`` changes predictions for ``v``.
+But also knowing ``v`` changes predictions for ``u``. The outcomes are dependent.
+Furthermore, an edge from ``v`` to ``w`` would mean that ``w`` and ``v`` are dependent
+and thus that ``u`` could indirectly influence ``w``.
+
+Without any knowledge about the system (candidate d-separating set is empty)
+a causal graph ``u -> v -> w`` allows all three nodes to be dependent. But
+if we know the outcome of ``v``, the conditional probabilities of outcomes for
+``u`` and ``w`` are independent of each other. That is, once we know the outcome
+for ``v``, the probabilities for ``w`` do not depend on the outcome for ``u``.
+This is the idea behind ``v`` blocking the path if it is "known" (in the candidate
+d-separating set).
+
+The same argument works whether the direction of the edges are both
+left-going and when both arrows head out from the middle. Having a "known"
+node on a path blocks the collider-free path because those relationships
+make the conditional probabilities independent.
+
+The direction of the causal edges does impact dependence precisely in the
+case of a collider e.g. ``u -> v <- w``. In that situation, both ``u`` and ``w``
+influence ``v``. But they do not directly influence each other. So without any
+knowledge of any outcomes, ``u`` and ``w`` are independent. That is the idea behind
+colliders blocking the path. But, if ``v`` is known, the conditional probabilities
+of ``u`` and ``w`` can be dependent. This is the heart of Berkson's Paradox [6]_.
+For example, suppose ``u`` and ``w`` are boolean events (they either happen or do not)
+and ``v`` represents the outcome "at least one of ``u`` and ``w`` occur". Then knowing
+``v`` is true makes the conditional probabilities of ``u`` and ``w`` dependent.
+Essentially, knowing that at least one of them is true raises the probability of
+each. But further knowledge that ``w`` is true (or false) change the conditional
+probability of ``u`` to either the original value or 1. So the conditional
+probability of ``u`` depends on the outcome of ``w`` even though there is no
+causal relationship between them. When a collider is known, dependence can
+occur across paths through that collider. This is the reason open colliders
+do not block paths.
+
+Furthermore, even if ``v`` is not "known", if one of its descendants is "known"
+we can use that information to know more about ``v`` which again makes
+``u`` and ``w`` potentially dependent. Suppose the chance of ``n`` occurring
+is much higher when ``v`` occurs ("at least one of ``u`` and ``w`` occur").
+Then if we know ``n`` occurred, it is more likely that ``v`` occurred and that
+makes the chance of ``u`` and ``w`` dependent. This is the idea behind why
+a collider does no block a path if any descendant of the collider is "known".
+
+When two sets of nodes ``x`` and ``y`` are d-separated by a set ``z``,
+it means that given the outcomes of the nodes in ``z``, the probabilities
+of outcomes of the nodes in ``x`` are independent of the outcomes of the
+nodes in ``y`` and vice versa.
+
+Examples
+--------
+A Hidden Markov Model with 5 observed states and 5 hidden states
+where the hidden states have causal relationships resulting in
+a path results in the following causal network. We check that
+early states along the path are separated from late state in
+the path by the d-separator of the middle hidden state.
+Thus if we condition on the middle hidden state, the early
+state probabilities are independent of the late state outcomes.
+
+>>> G = nx.DiGraph()
+>>> G.add_edges_from(
+...     [
+...         ("H1", "H2"),
+...         ("H2", "H3"),
+...         ("H3", "H4"),
+...         ("H4", "H5"),
+...         ("H1", "O1"),
+...         ("H2", "O2"),
+...         ("H3", "O3"),
+...         ("H4", "O4"),
+...         ("H5", "O5"),
+...     ]
+... )
+>>> x, y, z = ({"H1", "O1"}, {"H5", "O5"}, {"H3"})
+>>> nx.is_d_separator(G, x, y, z)
+True
+>>> nx.is_minimal_d_separator(G, x, y, z)
+True
+>>> nx.is_minimal_d_separator(G, x, y, z | {"O3"})
+False
+>>> z = nx.find_minimal_d_separator(G, x | y, {"O2", "O3", "O4"})
+>>> z == {"H2", "H4"}
+True
+
+If no minimal_d_separator exists, `None` is returned
+
+>>> other_z = nx.find_minimal_d_separator(G, x | y, {"H2", "H3"})
+>>> other_z is None
+True
+
+
+References
+----------
+
+.. [1] Pearl, J.  (2009).  Causality.  Cambridge: Cambridge University Press.
+
+.. [2] Darwiche, A.  (2009).  Modeling and reasoning with Bayesian networks.
+   Cambridge: Cambridge University Press.
+
+.. [3] Shachter, Ross D. "Bayes-ball: The rational pastime (for
+   determining irrelevance and requisite information in belief networks
+   and influence diagrams)." In Proceedings of the Fourteenth Conference
+   on Uncertainty in Artificial Intelligence (UAI), (pp. 480–487). 1998.
+
+.. [4] Koller, D., & Friedman, N. (2009).
+   Probabilistic graphical models: principles and techniques. The MIT Press.
+
+.. [5] https://en.wikipedia.org/wiki/Causal_Markov_condition
+
+.. [6] https://en.wikipedia.org/wiki/Berkson%27s_paradox
+
+"""
+
+from collections import deque
+from itertools import chain
+
+import networkx as nx
+from networkx.utils import UnionFind, not_implemented_for
+
+__all__ = [
+    "is_d_separator",
+    "is_minimal_d_separator",
+    "find_minimal_d_separator",
+]
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def is_d_separator(G, x, y, z):
+    """Return whether node sets `x` and `y` are d-separated by `z`.
+
+    Parameters
+    ----------
+    G : nx.DiGraph
+        A NetworkX DAG.
+
+    x : node or set of nodes
+        First node or set of nodes in `G`.
+
+    y : node or set of nodes
+        Second node or set of nodes in `G`.
+
+    z : node or set of nodes
+        Potential separator (set of conditioning nodes in `G`). Can be empty set.
+
+    Returns
+    -------
+    b : bool
+        A boolean that is true if `x` is d-separated from `y` given `z` in `G`.
+
+    Raises
+    ------
+    NetworkXError
+        The *d-separation* test is commonly used on disjoint sets of
+        nodes in acyclic directed graphs.  Accordingly, the algorithm
+        raises a :exc:`NetworkXError` if the node sets are not
+        disjoint or if the input graph is not a DAG.
+
+    NodeNotFound
+        If any of the input nodes are not found in the graph,
+        a :exc:`NodeNotFound` exception is raised
+
+    Notes
+    -----
+    A d-separating set in a DAG is a set of nodes that
+    blocks all paths between the two sets. Nodes in `z`
+    block a path if they are part of the path and are not a collider,
+    or a descendant of a collider. Also colliders that are not in `z`
+    block a path. A collider structure along a path
+    is ``... -> c <- ...`` where ``c`` is the collider node.
+
+    https://en.wikipedia.org/wiki/Bayesian_network#d-separation
+    """
+    try:
+        x = {x} if x in G else x
+        y = {y} if y in G else y
+        z = {z} if z in G else z
+
+        intersection = x & y or x & z or y & z
+        if intersection:
+            raise nx.NetworkXError(
+                f"The sets are not disjoint, with intersection {intersection}"
+            )
+
+        set_v = x | y | z
+        if set_v - G.nodes:
+            raise nx.NodeNotFound(f"The node(s) {set_v - G.nodes} are not found in G")
+    except TypeError:
+        raise nx.NodeNotFound("One of x, y, or z is not a node or a set of nodes in G")
+
+    if not nx.is_directed_acyclic_graph(G):
+        raise nx.NetworkXError("graph should be directed acyclic")
+
+    # contains -> and <-> edges from starting node T
+    forward_deque = deque([])
+    forward_visited = set()
+
+    # contains <- and - edges from starting node T
+    backward_deque = deque(x)
+    backward_visited = set()
+
+    ancestors_or_z = set().union(*[nx.ancestors(G, node) for node in x]) | z | x
+
+    while forward_deque or backward_deque:
+        if backward_deque:
+            node = backward_deque.popleft()
+            backward_visited.add(node)
+            if node in y:
+                return False
+            if node in z:
+                continue
+
+            # add <- edges to backward deque
+            backward_deque.extend(G.pred[node].keys() - backward_visited)
+            # add -> edges to forward deque
+            forward_deque.extend(G.succ[node].keys() - forward_visited)
+
+        if forward_deque:
+            node = forward_deque.popleft()
+            forward_visited.add(node)
+            if node in y:
+                return False
+
+            # Consider if -> node <- is opened due to ancestor of node in z
+            if node in ancestors_or_z:
+                # add <- edges to backward deque
+                backward_deque.extend(G.pred[node].keys() - backward_visited)
+            if node not in z:
+                # add -> edges to forward deque
+                forward_deque.extend(G.succ[node].keys() - forward_visited)
+
+    return True
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def find_minimal_d_separator(G, x, y, *, included=None, restricted=None):
+    """Returns a minimal d-separating set between `x` and `y` if possible
+
+    A d-separating set in a DAG is a set of nodes that blocks all
+    paths between the two sets of nodes, `x` and `y`. This function
+    constructs a d-separating set that is "minimal", meaning no nodes can
+    be removed without it losing the d-separating property for `x` and `y`.
+    If no d-separating sets exist for `x` and `y`, this returns `None`.
+
+    In a DAG there may be more than one minimal d-separator between two
+    sets of nodes. Minimal d-separators are not always unique. This function
+    returns one minimal d-separator, or `None` if no d-separator exists.
+
+    Uses the algorithm presented in [1]_. The complexity of the algorithm
+    is :math:`O(m)`, where :math:`m` stands for the number of edges in
+    the subgraph of G consisting of only the ancestors of `x` and `y`.
+    For full details, see [1]_.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx DAG.
+    x : set | node
+        A node or set of nodes in the graph.
+    y : set | node
+        A node or set of nodes in the graph.
+    included : set | node | None
+        A node or set of nodes which must be included in the found separating set,
+        default is None, which means the empty set.
+    restricted : set | node | None
+        Restricted node or set of nodes to consider. Only these nodes can be in
+        the found separating set, default is None meaning all nodes in ``G``.
+
+    Returns
+    -------
+    z : set | None
+        The minimal d-separating set, if at least one d-separating set exists,
+        otherwise None.
+
+    Raises
+    ------
+    NetworkXError
+        Raises a :exc:`NetworkXError` if the input graph is not a DAG
+        or if node sets `x`, `y`, and `included` are not disjoint.
+
+    NodeNotFound
+        If any of the input nodes are not found in the graph,
+        a :exc:`NodeNotFound` exception is raised.
+
+    References
+    ----------
+    .. [1] van der Zander, Benito, and Maciej Liśkiewicz. "Finding
+        minimal d-separators in linear time and applications." In
+        Uncertainty in Artificial Intelligence, pp. 637-647. PMLR, 2020.
+    """
+    if not nx.is_directed_acyclic_graph(G):
+        raise nx.NetworkXError("graph should be directed acyclic")
+
+    try:
+        x = {x} if x in G else x
+        y = {y} if y in G else y
+
+        if included is None:
+            included = set()
+        elif included in G:
+            included = {included}
+
+        if restricted is None:
+            restricted = set(G)
+        elif restricted in G:
+            restricted = {restricted}
+
+        set_y = x | y | included | restricted
+        if set_y - G.nodes:
+            raise nx.NodeNotFound(f"The node(s) {set_y - G.nodes} are not found in G")
+    except TypeError:
+        raise nx.NodeNotFound(
+            "One of x, y, included or restricted is not a node or set of nodes in G"
+        )
+
+    if not included <= restricted:
+        raise nx.NetworkXError(
+            f"Included nodes {included} must be in restricted nodes {restricted}"
+        )
+
+    intersection = x & y or x & included or y & included
+    if intersection:
+        raise nx.NetworkXError(
+            f"The sets x, y, included are not disjoint. Overlap: {intersection}"
+        )
+
+    nodeset = x | y | included
+    ancestors_x_y_included = nodeset.union(*[nx.ancestors(G, node) for node in nodeset])
+
+    z_init = restricted & (ancestors_x_y_included - (x | y))
+
+    x_closure = _reachable(G, x, ancestors_x_y_included, z_init)
+    if x_closure & y:
+        return None
+
+    z_updated = z_init & (x_closure | included)
+    y_closure = _reachable(G, y, ancestors_x_y_included, z_updated)
+    return z_updated & (y_closure | included)
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def is_minimal_d_separator(G, x, y, z, *, included=None, restricted=None):
+    """Determine if `z` is a minimal d-separator for `x` and `y`.
+
+    A d-separator, `z`, in a DAG is a set of nodes that blocks
+    all paths from nodes in set `x` to nodes in set `y`.
+    A minimal d-separator is a d-separator `z` such that removing
+    any subset of nodes makes it no longer a d-separator.
+
+    Note: This function checks whether `z` is a d-separator AND is
+    minimal. One can use the function `is_d_separator` to only check if
+    `z` is a d-separator. See examples below.
+
+    Parameters
+    ----------
+    G : nx.DiGraph
+        A NetworkX DAG.
+    x : node | set
+        A node or set of nodes in the graph.
+    y : node | set
+        A node or set of nodes in the graph.
+    z : node | set
+        The node or set of nodes to check if it is a minimal d-separating set.
+        The function :func:`is_d_separator` is called inside this function
+        to verify that `z` is in fact a d-separator.
+    included : set | node | None
+        A node or set of nodes which must be included in the found separating set,
+        default is ``None``, which means the empty set.
+    restricted : set | node | None
+        Restricted node or set of nodes to consider. Only these nodes can be in
+        the found separating set, default is ``None`` meaning all nodes in ``G``.
+
+    Returns
+    -------
+    bool
+        Whether or not the set `z` is a minimal d-separator subject to
+        `restricted` nodes and `included` node constraints.
+
+    Examples
+    --------
+    >>> G = nx.path_graph([0, 1, 2, 3], create_using=nx.DiGraph)
+    >>> G.add_node(4)
+    >>> nx.is_minimal_d_separator(G, 0, 2, {1})
+    True
+    >>> # since {1} is the minimal d-separator, {1, 3, 4} is not minimal
+    >>> nx.is_minimal_d_separator(G, 0, 2, {1, 3, 4})
+    False
+    >>> # alternatively, if we only want to check that {1, 3, 4} is a d-separator
+    >>> nx.is_d_separator(G, 0, 2, {1, 3, 4})
+    True
+
+    Raises
+    ------
+    NetworkXError
+        Raises a :exc:`NetworkXError` if the input graph is not a DAG.
+
+    NodeNotFound
+        If any of the input nodes are not found in the graph,
+        a :exc:`NodeNotFound` exception is raised.
+
+    References
+    ----------
+    .. [1] van der Zander, Benito, and Maciej Liśkiewicz. "Finding
+        minimal d-separators in linear time and applications." In
+        Uncertainty in Artificial Intelligence, pp. 637-647. PMLR, 2020.
+
+    Notes
+    -----
+    This function works on verifying that a set is minimal and
+    d-separating between two nodes. Uses criterion (a), (b), (c) on
+    page 4 of [1]_. a) closure(`x`) and `y` are disjoint. b) `z` contains
+    all nodes from `included` and is contained in the `restricted`
+    nodes and in the union of ancestors of `x`, `y`, and `included`.
+    c) the nodes in `z` not in `included` are contained in both
+    closure(x) and closure(y). The closure of a set is the set of nodes
+    connected to the set by a directed path in G.
+
+    The complexity is :math:`O(m)`, where :math:`m` stands for the
+    number of edges in the subgraph of G consisting of only the
+    ancestors of `x` and `y`.
+
+    For full details, see [1]_.
+    """
+    if not nx.is_directed_acyclic_graph(G):
+        raise nx.NetworkXError("graph should be directed acyclic")
+
+    try:
+        x = {x} if x in G else x
+        y = {y} if y in G else y
+        z = {z} if z in G else z
+
+        if included is None:
+            included = set()
+        elif included in G:
+            included = {included}
+
+        if restricted is None:
+            restricted = set(G)
+        elif restricted in G:
+            restricted = {restricted}
+
+        set_y = x | y | included | restricted
+        if set_y - G.nodes:
+            raise nx.NodeNotFound(f"The node(s) {set_y - G.nodes} are not found in G")
+    except TypeError:
+        raise nx.NodeNotFound(
+            "One of x, y, z, included or restricted is not a node or set of nodes in G"
+        )
+
+    if not included <= z:
+        raise nx.NetworkXError(
+            f"Included nodes {included} must be in proposed separating set z {x}"
+        )
+    if not z <= restricted:
+        raise nx.NetworkXError(
+            f"Separating set {z} must be contained in restricted set {restricted}"
+        )
+
+    intersection = x.intersection(y) or x.intersection(z) or y.intersection(z)
+    if intersection:
+        raise nx.NetworkXError(
+            f"The sets are not disjoint, with intersection {intersection}"
+        )
+
+    nodeset = x | y | included
+    ancestors_x_y_included = nodeset.union(*[nx.ancestors(G, n) for n in nodeset])
+
+    # criterion (a) -- check that z is actually a separator
+    x_closure = _reachable(G, x, ancestors_x_y_included, z)
+    if x_closure & y:
+        return False
+
+    # criterion (b) -- basic constraint; included and restricted already checked above
+    if not (z <= ancestors_x_y_included):
+        return False
+
+    # criterion (c) -- check that z is minimal
+    y_closure = _reachable(G, y, ancestors_x_y_included, z)
+    if not ((z - included) <= (x_closure & y_closure)):
+        return False
+    return True
+
+
+@not_implemented_for("undirected")
+def _reachable(G, x, a, z):
+    """Modified Bayes-Ball algorithm for finding d-connected nodes.
+
+    Find all nodes in `a` that are d-connected to those in `x` by
+    those in `z`. This is an implementation of the function
+    `REACHABLE` in [1]_ (which is itself a modification of the
+    Bayes-Ball algorithm [2]_) when restricted to DAGs.
+
+    Parameters
+    ----------
+    G : nx.DiGraph
+        A NetworkX DAG.
+    x : node | set
+        A node in the DAG, or a set of nodes.
+    a : node | set
+        A (set of) node(s) in the DAG containing the ancestors of `x`.
+    z : node | set
+        The node or set of nodes conditioned on when checking d-connectedness.
+
+    Returns
+    -------
+    w : set
+        The closure of `x` in `a` with respect to d-connectedness
+        given `z`.
+
+    References
+    ----------
+    .. [1] van der Zander, Benito, and Maciej Liśkiewicz. "Finding
+        minimal d-separators in linear time and applications." In
+        Uncertainty in Artificial Intelligence, pp. 637-647. PMLR, 2020.
+
+    .. [2] Shachter, Ross D. "Bayes-ball: The rational pastime
+       (for determining irrelevance and requisite information in
+       belief networks and influence diagrams)." In Proceedings of the
+       Fourteenth Conference on Uncertainty in Artificial Intelligence
+       (UAI), (pp. 480–487). 1998.
+    """
+
+    def _pass(e, v, f, n):
+        """Whether a ball entering node `v` along edge `e` passes to `n` along `f`.
+
+        Boolean function defined on page 6 of [1]_.
+
+        Parameters
+        ----------
+        e : bool
+            Directed edge by which the ball got to node `v`; `True` iff directed into `v`.
+        v : node
+            Node where the ball is.
+        f : bool
+            Directed edge connecting nodes `v` and `n`; `True` iff directed `n`.
+        n : node
+            Checking whether the ball passes to this node.
+
+        Returns
+        -------
+        b : bool
+            Whether the ball passes or not.
+
+        References
+        ----------
+        .. [1] van der Zander, Benito, and Maciej Liśkiewicz. "Finding
+           minimal d-separators in linear time and applications." In
+           Uncertainty in Artificial Intelligence, pp. 637-647. PMLR, 2020.
+        """
+        is_element_of_A = n in a
+        # almost_definite_status = True  # always true for DAGs; not so for RCGs
+        collider_if_in_Z = v not in z or (e and not f)
+        return is_element_of_A and collider_if_in_Z  # and almost_definite_status
+
+    queue = deque([])
+    for node in x:
+        if bool(G.pred[node]):
+            queue.append((True, node))
+        if bool(G.succ[node]):
+            queue.append((False, node))
+    processed = queue.copy()
+
+    while any(queue):
+        e, v = queue.popleft()
+        preds = ((False, n) for n in G.pred[v])
+        succs = ((True, n) for n in G.succ[v])
+        f_n_pairs = chain(preds, succs)
+        for f, n in f_n_pairs:
+            if (f, n) not in processed and _pass(e, v, f, n):
+                queue.append((f, n))
+                processed.append((f, n))
+
+    return {w for (_, w) in processed}
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/dag.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/dag.py
new file mode 100644
index 0000000000000000000000000000000000000000..130b4dca518ec8dd25aa2065d3dc8b33ccad30a5
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/dag.py
@@ -0,0 +1,1468 @@
+"""Algorithms for directed acyclic graphs (DAGs).
+
+Note that most of these functions are only guaranteed to work for DAGs.
+In general, these functions do not check for acyclic-ness, so it is up
+to the user to check for that.
+"""
+
+import heapq
+from collections import deque
+from functools import partial
+from itertools import chain, combinations, product, starmap
+from math import gcd
+
+import networkx as nx
+from networkx.utils import arbitrary_element, not_implemented_for, pairwise
+
+__all__ = [
+    "descendants",
+    "ancestors",
+    "topological_sort",
+    "lexicographical_topological_sort",
+    "all_topological_sorts",
+    "topological_generations",
+    "is_directed_acyclic_graph",
+    "is_aperiodic",
+    "transitive_closure",
+    "transitive_closure_dag",
+    "transitive_reduction",
+    "antichains",
+    "dag_longest_path",
+    "dag_longest_path_length",
+    "dag_to_branching",
+    "compute_v_structures",
+]
+
+chaini = chain.from_iterable
+
+
+@nx._dispatchable
+def descendants(G, source):
+    """Returns all nodes reachable from `source` in `G`.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    source : node in `G`
+
+    Returns
+    -------
+    set()
+        The descendants of `source` in `G`
+
+    Raises
+    ------
+    NetworkXError
+        If node `source` is not in `G`.
+
+    Examples
+    --------
+    >>> DG = nx.path_graph(5, create_using=nx.DiGraph)
+    >>> sorted(nx.descendants(DG, 2))
+    [3, 4]
+
+    The `source` node is not a descendant of itself, but can be included manually:
+
+    >>> sorted(nx.descendants(DG, 2) | {2})
+    [2, 3, 4]
+
+    See also
+    --------
+    ancestors
+    """
+    return {child for parent, child in nx.bfs_edges(G, source)}
+
+
+@nx._dispatchable
+def ancestors(G, source):
+    """Returns all nodes having a path to `source` in `G`.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    source : node in `G`
+
+    Returns
+    -------
+    set()
+        The ancestors of `source` in `G`
+
+    Raises
+    ------
+    NetworkXError
+        If node `source` is not in `G`.
+
+    Examples
+    --------
+    >>> DG = nx.path_graph(5, create_using=nx.DiGraph)
+    >>> sorted(nx.ancestors(DG, 2))
+    [0, 1]
+
+    The `source` node is not an ancestor of itself, but can be included manually:
+
+    >>> sorted(nx.ancestors(DG, 2) | {2})
+    [0, 1, 2]
+
+    See also
+    --------
+    descendants
+    """
+    return {child for parent, child in nx.bfs_edges(G, source, reverse=True)}
+
+
+@nx._dispatchable
+def has_cycle(G):
+    """Decides whether the directed graph has a cycle."""
+    try:
+        # Feed the entire iterator into a zero-length deque.
+        deque(topological_sort(G), maxlen=0)
+    except nx.NetworkXUnfeasible:
+        return True
+    else:
+        return False
+
+
+@nx._dispatchable
+def is_directed_acyclic_graph(G):
+    """Returns True if the graph `G` is a directed acyclic graph (DAG) or
+    False if not.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    bool
+        True if `G` is a DAG, False otherwise
+
+    Examples
+    --------
+    Undirected graph::
+
+        >>> G = nx.Graph([(1, 2), (2, 3)])
+        >>> nx.is_directed_acyclic_graph(G)
+        False
+
+    Directed graph with cycle::
+
+        >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
+        >>> nx.is_directed_acyclic_graph(G)
+        False
+
+    Directed acyclic graph::
+
+        >>> G = nx.DiGraph([(1, 2), (2, 3)])
+        >>> nx.is_directed_acyclic_graph(G)
+        True
+
+    See also
+    --------
+    topological_sort
+    """
+    return G.is_directed() and not has_cycle(G)
+
+
+@nx._dispatchable
+def topological_generations(G):
+    """Stratifies a DAG into generations.
+
+    A topological generation is node collection in which ancestors of a node in each
+    generation are guaranteed to be in a previous generation, and any descendants of
+    a node are guaranteed to be in a following generation. Nodes are guaranteed to
+    be in the earliest possible generation that they can belong to.
+
+    Parameters
+    ----------
+    G : NetworkX digraph
+        A directed acyclic graph (DAG)
+
+    Yields
+    ------
+    sets of nodes
+        Yields sets of nodes representing each generation.
+
+    Raises
+    ------
+    NetworkXError
+        Generations are defined for directed graphs only. If the graph
+        `G` is undirected, a :exc:`NetworkXError` is raised.
+
+    NetworkXUnfeasible
+        If `G` is not a directed acyclic graph (DAG) no topological generations
+        exist and a :exc:`NetworkXUnfeasible` exception is raised.  This can also
+        be raised if `G` is changed while the returned iterator is being processed
+
+    RuntimeError
+        If `G` is changed while the returned iterator is being processed.
+
+    Examples
+    --------
+    >>> DG = nx.DiGraph([(2, 1), (3, 1)])
+    >>> [sorted(generation) for generation in nx.topological_generations(DG)]
+    [[2, 3], [1]]
+
+    Notes
+    -----
+    The generation in which a node resides can also be determined by taking the
+    max-path-distance from the node to the farthest leaf node. That value can
+    be obtained with this function using `enumerate(topological_generations(G))`.
+
+    See also
+    --------
+    topological_sort
+    """
+    if not G.is_directed():
+        raise nx.NetworkXError("Topological sort not defined on undirected graphs.")
+
+    multigraph = G.is_multigraph()
+    indegree_map = {v: d for v, d in G.in_degree() if d > 0}
+    zero_indegree = [v for v, d in G.in_degree() if d == 0]
+
+    while zero_indegree:
+        this_generation = zero_indegree
+        zero_indegree = []
+        for node in this_generation:
+            if node not in G:
+                raise RuntimeError("Graph changed during iteration")
+            for child in G.neighbors(node):
+                try:
+                    indegree_map[child] -= len(G[node][child]) if multigraph else 1
+                except KeyError as err:
+                    raise RuntimeError("Graph changed during iteration") from err
+                if indegree_map[child] == 0:
+                    zero_indegree.append(child)
+                    del indegree_map[child]
+        yield this_generation
+
+    if indegree_map:
+        raise nx.NetworkXUnfeasible(
+            "Graph contains a cycle or graph changed during iteration"
+        )
+
+
+@nx._dispatchable
+def topological_sort(G):
+    """Returns a generator of nodes in topologically sorted order.
+
+    A topological sort is a nonunique permutation of the nodes of a
+    directed graph such that an edge from u to v implies that u
+    appears before v in the topological sort order. This ordering is
+    valid only if the graph has no directed cycles.
+
+    Parameters
+    ----------
+    G : NetworkX digraph
+        A directed acyclic graph (DAG)
+
+    Yields
+    ------
+    nodes
+        Yields the nodes in topological sorted order.
+
+    Raises
+    ------
+    NetworkXError
+        Topological sort is defined for directed graphs only. If the graph `G`
+        is undirected, a :exc:`NetworkXError` is raised.
+
+    NetworkXUnfeasible
+        If `G` is not a directed acyclic graph (DAG) no topological sort exists
+        and a :exc:`NetworkXUnfeasible` exception is raised.  This can also be
+        raised if `G` is changed while the returned iterator is being processed
+
+    RuntimeError
+        If `G` is changed while the returned iterator is being processed.
+
+    Examples
+    --------
+    To get the reverse order of the topological sort:
+
+    >>> DG = nx.DiGraph([(1, 2), (2, 3)])
+    >>> list(reversed(list(nx.topological_sort(DG))))
+    [3, 2, 1]
+
+    If your DiGraph naturally has the edges representing tasks/inputs
+    and nodes representing people/processes that initiate tasks, then
+    topological_sort is not quite what you need. You will have to change
+    the tasks to nodes with dependence reflected by edges. The result is
+    a kind of topological sort of the edges. This can be done
+    with :func:`networkx.line_graph` as follows:
+
+    >>> list(nx.topological_sort(nx.line_graph(DG)))
+    [(1, 2), (2, 3)]
+
+    Notes
+    -----
+    This algorithm is based on a description and proof in
+    "Introduction to Algorithms: A Creative Approach" [1]_ .
+
+    See also
+    --------
+    is_directed_acyclic_graph, lexicographical_topological_sort
+
+    References
+    ----------
+    .. [1] Manber, U. (1989).
+       *Introduction to Algorithms - A Creative Approach.* Addison-Wesley.
+    """
+    for generation in nx.topological_generations(G):
+        yield from generation
+
+
+@nx._dispatchable
+def lexicographical_topological_sort(G, key=None):
+    """Generate the nodes in the unique lexicographical topological sort order.
+
+    Generates a unique ordering of nodes by first sorting topologically (for which there are often
+    multiple valid orderings) and then additionally by sorting lexicographically.
+
+    A topological sort arranges the nodes of a directed graph so that the
+    upstream node of each directed edge precedes the downstream node.
+    It is always possible to find a solution for directed graphs that have no cycles.
+    There may be more than one valid solution.
+
+    Lexicographical sorting is just sorting alphabetically. It is used here to break ties in the
+    topological sort and to determine a single, unique ordering.  This can be useful in comparing
+    sort results.
+
+    The lexicographical order can be customized by providing a function to the `key=` parameter.
+    The definition of the key function is the same as used in python's built-in `sort()`.
+    The function takes a single argument and returns a key to use for sorting purposes.
+
+    Lexicographical sorting can fail if the node names are un-sortable. See the example below.
+    The solution is to provide a function to the `key=` argument that returns sortable keys.
+
+
+    Parameters
+    ----------
+    G : NetworkX digraph
+        A directed acyclic graph (DAG)
+
+    key : function, optional
+        A function of one argument that converts a node name to a comparison key.
+        It defines and resolves ambiguities in the sort order.  Defaults to the identity function.
+
+    Yields
+    ------
+    nodes
+        Yields the nodes of G in lexicographical topological sort order.
+
+    Raises
+    ------
+    NetworkXError
+        Topological sort is defined for directed graphs only. If the graph `G`
+        is undirected, a :exc:`NetworkXError` is raised.
+
+    NetworkXUnfeasible
+        If `G` is not a directed acyclic graph (DAG) no topological sort exists
+        and a :exc:`NetworkXUnfeasible` exception is raised.  This can also be
+        raised if `G` is changed while the returned iterator is being processed
+
+    RuntimeError
+        If `G` is changed while the returned iterator is being processed.
+
+    TypeError
+        Results from un-sortable node names.
+        Consider using `key=` parameter to resolve ambiguities in the sort order.
+
+    Examples
+    --------
+    >>> DG = nx.DiGraph([(2, 1), (2, 5), (1, 3), (1, 4), (5, 4)])
+    >>> list(nx.lexicographical_topological_sort(DG))
+    [2, 1, 3, 5, 4]
+    >>> list(nx.lexicographical_topological_sort(DG, key=lambda x: -x))
+    [2, 5, 1, 4, 3]
+
+    The sort will fail for any graph with integer and string nodes. Comparison of integer to strings
+    is not defined in python.  Is 3 greater or less than 'red'?
+
+    >>> DG = nx.DiGraph([(1, "red"), (3, "red"), (1, "green"), (2, "blue")])
+    >>> list(nx.lexicographical_topological_sort(DG))
+    Traceback (most recent call last):
+    ...
+    TypeError: '<' not supported between instances of 'str' and 'int'
+    ...
+
+    Incomparable nodes can be resolved using a `key` function. This example function
+    allows comparison of integers and strings by returning a tuple where the first
+    element is True for `str`, False otherwise. The second element is the node name.
+    This groups the strings and integers separately so they can be compared only among themselves.
+
+    >>> key = lambda node: (isinstance(node, str), node)
+    >>> list(nx.lexicographical_topological_sort(DG, key=key))
+    [1, 2, 3, 'blue', 'green', 'red']
+
+    Notes
+    -----
+    This algorithm is based on a description and proof in
+    "Introduction to Algorithms: A Creative Approach" [1]_ .
+
+    See also
+    --------
+    topological_sort
+
+    References
+    ----------
+    .. [1] Manber, U. (1989).
+       *Introduction to Algorithms - A Creative Approach.* Addison-Wesley.
+    """
+    if not G.is_directed():
+        msg = "Topological sort not defined on undirected graphs."
+        raise nx.NetworkXError(msg)
+
+    if key is None:
+
+        def key(node):
+            return node
+
+    nodeid_map = {n: i for i, n in enumerate(G)}
+
+    def create_tuple(node):
+        return key(node), nodeid_map[node], node
+
+    indegree_map = {v: d for v, d in G.in_degree() if d > 0}
+    # These nodes have zero indegree and ready to be returned.
+    zero_indegree = [create_tuple(v) for v, d in G.in_degree() if d == 0]
+    heapq.heapify(zero_indegree)
+
+    while zero_indegree:
+        _, _, node = heapq.heappop(zero_indegree)
+
+        if node not in G:
+            raise RuntimeError("Graph changed during iteration")
+        for _, child in G.edges(node):
+            try:
+                indegree_map[child] -= 1
+            except KeyError as err:
+                raise RuntimeError("Graph changed during iteration") from err
+            if indegree_map[child] == 0:
+                try:
+                    heapq.heappush(zero_indegree, create_tuple(child))
+                except TypeError as err:
+                    raise TypeError(
+                        f"{err}\nConsider using `key=` parameter to resolve ambiguities in the sort order."
+                    )
+                del indegree_map[child]
+
+        yield node
+
+    if indegree_map:
+        msg = "Graph contains a cycle or graph changed during iteration"
+        raise nx.NetworkXUnfeasible(msg)
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def all_topological_sorts(G):
+    """Returns a generator of _all_ topological sorts of the directed graph G.
+
+    A topological sort is a nonunique permutation of the nodes such that an
+    edge from u to v implies that u appears before v in the topological sort
+    order.
+
+    Parameters
+    ----------
+    G : NetworkX DiGraph
+        A directed graph
+
+    Yields
+    ------
+    topological_sort_order : list
+        a list of nodes in `G`, representing one of the topological sort orders
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is not directed
+    NetworkXUnfeasible
+        If `G` is not acyclic
+
+    Examples
+    --------
+    To enumerate all topological sorts of directed graph:
+
+    >>> DG = nx.DiGraph([(1, 2), (2, 3), (2, 4)])
+    >>> list(nx.all_topological_sorts(DG))
+    [[1, 2, 4, 3], [1, 2, 3, 4]]
+
+    Notes
+    -----
+    Implements an iterative version of the algorithm given in [1].
+
+    References
+    ----------
+    .. [1] Knuth, Donald E., Szwarcfiter, Jayme L. (1974).
+       "A Structured Program to Generate All Topological Sorting Arrangements"
+       Information Processing Letters, Volume 2, Issue 6, 1974, Pages 153-157,
+       ISSN 0020-0190,
+       https://doi.org/10.1016/0020-0190(74)90001-5.
+       Elsevier (North-Holland), Amsterdam
+    """
+    if not G.is_directed():
+        raise nx.NetworkXError("Topological sort not defined on undirected graphs.")
+
+    # the names of count and D are chosen to match the global variables in [1]
+    # number of edges originating in a vertex v
+    count = dict(G.in_degree())
+    # vertices with indegree 0
+    D = deque([v for v, d in G.in_degree() if d == 0])
+    # stack of first value chosen at a position k in the topological sort
+    bases = []
+    current_sort = []
+
+    # do-while construct
+    while True:
+        assert all(count[v] == 0 for v in D)
+
+        if len(current_sort) == len(G):
+            yield list(current_sort)
+
+            # clean-up stack
+            while len(current_sort) > 0:
+                assert len(bases) == len(current_sort)
+                q = current_sort.pop()
+
+                # "restores" all edges (q, x)
+                # NOTE: it is important to iterate over edges instead
+                # of successors, so count is updated correctly in multigraphs
+                for _, j in G.out_edges(q):
+                    count[j] += 1
+                    assert count[j] >= 0
+                # remove entries from D
+                while len(D) > 0 and count[D[-1]] > 0:
+                    D.pop()
+
+                # corresponds to a circular shift of the values in D
+                # if the first value chosen (the base) is in the first
+                # position of D again, we are done and need to consider the
+                # previous condition
+                D.appendleft(q)
+                if D[-1] == bases[-1]:
+                    # all possible values have been chosen at current position
+                    # remove corresponding marker
+                    bases.pop()
+                else:
+                    # there are still elements that have not been fixed
+                    # at the current position in the topological sort
+                    # stop removing elements, escape inner loop
+                    break
+
+        else:
+            if len(D) == 0:
+                raise nx.NetworkXUnfeasible("Graph contains a cycle.")
+
+            # choose next node
+            q = D.pop()
+            # "erase" all edges (q, x)
+            # NOTE: it is important to iterate over edges instead
+            # of successors, so count is updated correctly in multigraphs
+            for _, j in G.out_edges(q):
+                count[j] -= 1
+                assert count[j] >= 0
+                if count[j] == 0:
+                    D.append(j)
+            current_sort.append(q)
+
+            # base for current position might _not_ be fixed yet
+            if len(bases) < len(current_sort):
+                bases.append(q)
+
+        if len(bases) == 0:
+            break
+
+
+@nx._dispatchable
+def is_aperiodic(G):
+    """Returns True if `G` is aperiodic.
+
+    A strongly connected directed graph is aperiodic if there is no integer ``k > 1``
+    that divides the length of every cycle in the graph.
+
+    This function requires the graph `G` to be strongly connected and will raise
+    an error if it's not. For graphs that are not strongly connected, you should
+    first identify their strongly connected components
+    (using :func:`~networkx.algorithms.components.strongly_connected_components`)
+    or attracting components
+    (using :func:`~networkx.algorithms.components.attracting_components`),
+    and then apply this function to those individual components.
+
+    Parameters
+    ----------
+    G : NetworkX DiGraph
+        A directed graph
+
+    Returns
+    -------
+    bool
+        True if the graph is aperiodic False otherwise
+
+    Raises
+    ------
+    NetworkXError
+        If `G` is not directed
+    NetworkXError
+        If `G` is not strongly connected
+    NetworkXPointlessConcept
+        If `G` has no nodes
+
+    Examples
+    --------
+    A graph consisting of one cycle, the length of which is 2. Therefore ``k = 2``
+    divides the length of every cycle in the graph and thus the graph
+    is *not aperiodic*::
+
+        >>> DG = nx.DiGraph([(1, 2), (2, 1)])
+        >>> nx.is_aperiodic(DG)
+        False
+
+    A graph consisting of two cycles: one of length 2 and the other of length 3.
+    The cycle lengths are coprime, so there is no single value of k where ``k > 1``
+    that divides each cycle length and therefore the graph is *aperiodic*::
+
+        >>> DG = nx.DiGraph([(1, 2), (2, 3), (3, 1), (1, 4), (4, 1)])
+        >>> nx.is_aperiodic(DG)
+        True
+
+    A graph created from cycles of the same length can still be aperiodic since
+    the cycles can overlap and form new cycles of different lengths. For example,
+    the following graph contains a cycle ``[4, 2, 3, 1]`` of length 4, which is coprime
+    with the explicitly added cycles of length 3, so the graph is aperiodic::
+
+        >>> DG = nx.DiGraph()
+        >>> nx.add_cycle(DG, [1, 2, 3])
+        >>> nx.add_cycle(DG, [2, 1, 4])
+        >>> nx.is_aperiodic(DG)
+        True
+
+    A single-node graph's aperiodicity depends on whether it has a self-loop:
+    it is aperiodic if a self-loop exists, and periodic otherwise::
+
+        >>> G = nx.DiGraph()
+        >>> G.add_node(1)
+        >>> nx.is_aperiodic(G)
+        False
+        >>> G.add_edge(1, 1)
+        >>> nx.is_aperiodic(G)
+        True
+
+    A Markov chain can be modeled as a directed graph, with nodes representing
+    states and edges representing transitions with non-zero probability.
+    Aperiodicity is typically considered for irreducible Markov chains,
+    which are those that are *strongly connected* as graphs.
+
+    The following Markov chain is irreducible and aperiodic, and thus
+    ergodic. It is guaranteed to have a unique stationary distribution::
+
+        >>> G = nx.DiGraph()
+        >>> nx.add_cycle(G, [1, 2, 3, 4])
+        >>> G.add_edge(1, 3)
+        >>> nx.is_aperiodic(G)
+        True
+
+    Reducible Markov chains can sometimes have a unique stationary distribution.
+    This occurs if the chain has exactly one closed communicating class and
+    that class itself is aperiodic (see [1]_). You can use
+    :func:`~networkx.algorithms.components.attracting_components`
+    to find these closed communicating classes::
+
+        >>> G = nx.DiGraph([(1, 3), (2, 3)])
+        >>> nx.add_cycle(G, [3, 4, 5, 6])
+        >>> nx.add_cycle(G, [3, 5, 6])
+        >>> communicating_classes = list(nx.strongly_connected_components(G))
+        >>> len(communicating_classes)
+        3
+        >>> closed_communicating_classes = list(nx.attracting_components(G))
+        >>> len(closed_communicating_classes)
+        1
+        >>> nx.is_aperiodic(G.subgraph(closed_communicating_classes[0]))
+        True
+
+    Notes
+    -----
+    This uses the method outlined in [1]_, which runs in $O(m)$ time
+    given $m$ edges in `G`.
+
+    References
+    ----------
+    .. [1] Jarvis, J. P.; Shier, D. R. (1996),
+       "Graph-theoretic analysis of finite Markov chains,"
+       in Shier, D. R.; Wallenius, K. T., Applied Mathematical Modeling:
+       A Multidisciplinary Approach, CRC Press.
+    """
+    if not G.is_directed():
+        raise nx.NetworkXError("is_aperiodic not defined for undirected graphs")
+    if len(G) == 0:
+        raise nx.NetworkXPointlessConcept("Graph has no nodes.")
+    if not nx.is_strongly_connected(G):
+        raise nx.NetworkXError("Graph is not strongly connected.")
+    s = arbitrary_element(G)
+    levels = {s: 0}
+    this_level = [s]
+    g = 0
+    lev = 1
+    while this_level:
+        next_level = []
+        for u in this_level:
+            for v in G[u]:
+                if v in levels:  # Non-Tree Edge
+                    g = gcd(g, levels[u] - levels[v] + 1)
+                else:  # Tree Edge
+                    next_level.append(v)
+                    levels[v] = lev
+        this_level = next_level
+        lev += 1
+    return g == 1
+
+
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def transitive_closure(G, reflexive=False):
+    """Returns transitive closure of a graph
+
+    The transitive closure of G = (V,E) is a graph G+ = (V,E+) such that
+    for all v, w in V there is an edge (v, w) in E+ if and only if there
+    is a path from v to w in G.
+
+    Handling of paths from v to v has some flexibility within this definition.
+    A reflexive transitive closure creates a self-loop for the path
+    from v to v of length 0. The usual transitive closure creates a
+    self-loop only if a cycle exists (a path from v to v with length > 0).
+    We also allow an option for no self-loops.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        A directed/undirected graph/multigraph.
+    reflexive : Bool or None, optional (default: False)
+        Determines when cycles create self-loops in the Transitive Closure.
+        If True, trivial cycles (length 0) create self-loops. The result
+        is a reflexive transitive closure of G.
+        If False (the default) non-trivial cycles create self-loops.
+        If None, self-loops are not created.
+
+    Returns
+    -------
+    NetworkX graph
+        The transitive closure of `G`
+
+    Raises
+    ------
+    NetworkXError
+        If `reflexive` not in `{None, True, False}`
+
+    Examples
+    --------
+    The treatment of trivial (i.e. length 0) cycles is controlled by the
+    `reflexive` parameter.
+
+    Trivial (i.e. length 0) cycles do not create self-loops when
+    ``reflexive=False`` (the default)::
+
+        >>> DG = nx.DiGraph([(1, 2), (2, 3)])
+        >>> TC = nx.transitive_closure(DG, reflexive=False)
+        >>> TC.edges()
+        OutEdgeView([(1, 2), (1, 3), (2, 3)])
+
+    However, nontrivial (i.e. length greater than 0) cycles create self-loops
+    when ``reflexive=False`` (the default)::
+
+        >>> DG = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
+        >>> TC = nx.transitive_closure(DG, reflexive=False)
+        >>> TC.edges()
+        OutEdgeView([(1, 2), (1, 3), (1, 1), (2, 3), (2, 1), (2, 2), (3, 1), (3, 2), (3, 3)])
+
+    Trivial cycles (length 0) create self-loops when ``reflexive=True``::
+
+        >>> DG = nx.DiGraph([(1, 2), (2, 3)])
+        >>> TC = nx.transitive_closure(DG, reflexive=True)
+        >>> TC.edges()
+        OutEdgeView([(1, 2), (1, 1), (1, 3), (2, 3), (2, 2), (3, 3)])
+
+    And the third option is not to create self-loops at all when ``reflexive=None``::
+
+        >>> DG = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
+        >>> TC = nx.transitive_closure(DG, reflexive=None)
+        >>> TC.edges()
+        OutEdgeView([(1, 2), (1, 3), (2, 3), (2, 1), (3, 1), (3, 2)])
+
+    References
+    ----------
+    .. [1] https://www.ics.uci.edu/~eppstein/PADS/PartialOrder.py
+    """
+    TC = G.copy()
+
+    if reflexive not in {None, True, False}:
+        raise nx.NetworkXError("Incorrect value for the parameter `reflexive`")
+
+    for v in G:
+        if reflexive is None:
+            TC.add_edges_from((v, u) for u in nx.descendants(G, v) if u not in TC[v])
+        elif reflexive is True:
+            TC.add_edges_from(
+                (v, u) for u in nx.descendants(G, v) | {v} if u not in TC[v]
+            )
+        elif reflexive is False:
+            TC.add_edges_from((v, e[1]) for e in nx.edge_bfs(G, v) if e[1] not in TC[v])
+
+    return TC
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def transitive_closure_dag(G, topo_order=None):
+    """Returns the transitive closure of a directed acyclic graph.
+
+    This function is faster than the function `transitive_closure`, but fails
+    if the graph has a cycle.
+
+    The transitive closure of G = (V,E) is a graph G+ = (V,E+) such that
+    for all v, w in V there is an edge (v, w) in E+ if and only if there
+    is a non-null path from v to w in G.
+
+    Parameters
+    ----------
+    G : NetworkX DiGraph
+        A directed acyclic graph (DAG)
+
+    topo_order: list or tuple, optional
+        A topological order for G (if None, the function will compute one)
+
+    Returns
+    -------
+    NetworkX DiGraph
+        The transitive closure of `G`
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is not directed
+    NetworkXUnfeasible
+        If `G` has a cycle
+
+    Examples
+    --------
+    >>> DG = nx.DiGraph([(1, 2), (2, 3)])
+    >>> TC = nx.transitive_closure_dag(DG)
+    >>> TC.edges()
+    OutEdgeView([(1, 2), (1, 3), (2, 3)])
+
+    Notes
+    -----
+    This algorithm is probably simple enough to be well-known but I didn't find
+    a mention in the literature.
+    """
+    if topo_order is None:
+        topo_order = list(topological_sort(G))
+
+    TC = G.copy()
+
+    # idea: traverse vertices following a reverse topological order, connecting
+    # each vertex to its descendants at distance 2 as we go
+    for v in reversed(topo_order):
+        TC.add_edges_from((v, u) for u in nx.descendants_at_distance(TC, v, 2))
+
+    return TC
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable(returns_graph=True)
+def transitive_reduction(G):
+    """Returns transitive reduction of a directed graph
+
+    The transitive reduction of G = (V,E) is a graph G- = (V,E-) such that
+    for all v,w in V there is an edge (v,w) in E- if and only if (v,w) is
+    in E and there is no path from v to w in G with length greater than 1.
+
+    Parameters
+    ----------
+    G : NetworkX DiGraph
+        A directed acyclic graph (DAG)
+
+    Returns
+    -------
+    NetworkX DiGraph
+        The transitive reduction of `G`
+
+    Raises
+    ------
+    NetworkXError
+        If `G` is not a directed acyclic graph (DAG) transitive reduction is
+        not uniquely defined and a :exc:`NetworkXError` exception is raised.
+
+    Examples
+    --------
+    To perform transitive reduction on a DiGraph:
+
+    >>> DG = nx.DiGraph([(1, 2), (2, 3), (1, 3)])
+    >>> TR = nx.transitive_reduction(DG)
+    >>> list(TR.edges)
+    [(1, 2), (2, 3)]
+
+    To avoid unnecessary data copies, this implementation does not return a
+    DiGraph with node/edge data.
+    To perform transitive reduction on a DiGraph and transfer node/edge data:
+
+    >>> DG = nx.DiGraph()
+    >>> DG.add_edges_from([(1, 2), (2, 3), (1, 3)], color="red")
+    >>> TR = nx.transitive_reduction(DG)
+    >>> TR.add_nodes_from(DG.nodes(data=True))
+    >>> TR.add_edges_from((u, v, DG.edges[u, v]) for u, v in TR.edges)
+    >>> list(TR.edges(data=True))
+    [(1, 2, {'color': 'red'}), (2, 3, {'color': 'red'})]
+
+    References
+    ----------
+    https://en.wikipedia.org/wiki/Transitive_reduction
+
+    """
+    if not is_directed_acyclic_graph(G):
+        msg = "Directed Acyclic Graph required for transitive_reduction"
+        raise nx.NetworkXError(msg)
+    TR = nx.DiGraph()
+    TR.add_nodes_from(G.nodes())
+    descendants = {}
+    # count before removing set stored in descendants
+    check_count = dict(G.in_degree)
+    for u in G:
+        u_nbrs = set(G[u])
+        for v in G[u]:
+            if v in u_nbrs:
+                if v not in descendants:
+                    descendants[v] = {y for x, y in nx.dfs_edges(G, v)}
+                u_nbrs -= descendants[v]
+            check_count[v] -= 1
+            if check_count[v] == 0:
+                del descendants[v]
+        TR.add_edges_from((u, v) for v in u_nbrs)
+    return TR
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def antichains(G, topo_order=None):
+    """Generates antichains from a directed acyclic graph (DAG).
+
+    An antichain is a subset of a partially ordered set such that any
+    two elements in the subset are incomparable.
+
+    Parameters
+    ----------
+    G : NetworkX DiGraph
+        A directed acyclic graph (DAG)
+
+    topo_order: list or tuple, optional
+        A topological order for G (if None, the function will compute one)
+
+    Yields
+    ------
+    antichain : list
+        a list of nodes in `G` representing an antichain
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is not directed
+
+    NetworkXUnfeasible
+        If `G` contains a cycle
+
+    Examples
+    --------
+    >>> DG = nx.DiGraph([(1, 2), (1, 3)])
+    >>> list(nx.antichains(DG))
+    [[], [3], [2], [2, 3], [1]]
+
+    Notes
+    -----
+    This function was originally developed by Peter Jipsen and Franco Saliola
+    for the SAGE project. It's included in NetworkX with permission from the
+    authors. Original SAGE code at:
+
+    https://github.com/sagemath/sage/blob/master/src/sage/combinat/posets/hasse_diagram.py
+
+    References
+    ----------
+    .. [1] Free Lattices, by R. Freese, J. Jezek and J. B. Nation,
+       AMS, Vol 42, 1995, p. 226.
+    """
+    if topo_order is None:
+        topo_order = list(nx.topological_sort(G))
+
+    TC = nx.transitive_closure_dag(G, topo_order)
+    antichains_stacks = [([], list(reversed(topo_order)))]
+
+    while antichains_stacks:
+        (antichain, stack) = antichains_stacks.pop()
+        # Invariant:
+        #  - the elements of antichain are independent
+        #  - the elements of stack are independent from those of antichain
+        yield antichain
+        while stack:
+            x = stack.pop()
+            new_antichain = antichain + [x]
+            new_stack = [t for t in stack if not ((t in TC[x]) or (x in TC[t]))]
+            antichains_stacks.append((new_antichain, new_stack))
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable(edge_attrs={"weight": "default_weight"})
+def dag_longest_path(G, weight="weight", default_weight=1, topo_order=None):
+    """Returns the longest path in a directed acyclic graph (DAG).
+
+    If `G` has edges with `weight` attribute the edge data are used as
+    weight values.
+
+    Parameters
+    ----------
+    G : NetworkX DiGraph
+        A directed acyclic graph (DAG)
+
+    weight : str, optional
+        Edge data key to use for weight
+
+    default_weight : int, optional
+        The weight of edges that do not have a weight attribute
+
+    topo_order: list or tuple, optional
+        A topological order for `G` (if None, the function will compute one)
+
+    Returns
+    -------
+    list
+        Longest path
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is not directed
+
+    Examples
+    --------
+    >>> DG = nx.DiGraph(
+    ...     [(0, 1, {"cost": 1}), (1, 2, {"cost": 1}), (0, 2, {"cost": 42})]
+    ... )
+    >>> list(nx.all_simple_paths(DG, 0, 2))
+    [[0, 1, 2], [0, 2]]
+    >>> nx.dag_longest_path(DG)
+    [0, 1, 2]
+    >>> nx.dag_longest_path(DG, weight="cost")
+    [0, 2]
+
+    In the case where multiple valid topological orderings exist, `topo_order`
+    can be used to specify a specific ordering:
+
+    >>> DG = nx.DiGraph([(0, 1), (0, 2)])
+    >>> sorted(nx.all_topological_sorts(DG))  # Valid topological orderings
+    [[0, 1, 2], [0, 2, 1]]
+    >>> nx.dag_longest_path(DG, topo_order=[0, 1, 2])
+    [0, 1]
+    >>> nx.dag_longest_path(DG, topo_order=[0, 2, 1])
+    [0, 2]
+
+    See also
+    --------
+    dag_longest_path_length
+
+    """
+    if not G:
+        return []
+
+    if topo_order is None:
+        topo_order = nx.topological_sort(G)
+
+    dist = {}  # stores {v : (length, u)}
+    for v in topo_order:
+        us = [
+            (
+                dist[u][0]
+                + (
+                    max(data.values(), key=lambda x: x.get(weight, default_weight))
+                    if G.is_multigraph()
+                    else data
+                ).get(weight, default_weight),
+                u,
+            )
+            for u, data in G.pred[v].items()
+        ]
+
+        # Use the best predecessor if there is one and its distance is
+        # non-negative, otherwise terminate.
+        maxu = max(us, key=lambda x: x[0]) if us else (0, v)
+        dist[v] = maxu if maxu[0] >= 0 else (0, v)
+
+    u = None
+    v = max(dist, key=lambda x: dist[x][0])
+    path = []
+    while u != v:
+        path.append(v)
+        u = v
+        v = dist[v][1]
+
+    path.reverse()
+    return path
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable(edge_attrs={"weight": "default_weight"})
+def dag_longest_path_length(G, weight="weight", default_weight=1):
+    """Returns the longest path length in a DAG
+
+    Parameters
+    ----------
+    G : NetworkX DiGraph
+        A directed acyclic graph (DAG)
+
+    weight : string, optional
+        Edge data key to use for weight
+
+    default_weight : int, optional
+        The weight of edges that do not have a weight attribute
+
+    Returns
+    -------
+    int
+        Longest path length
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is not directed
+
+    Examples
+    --------
+    >>> DG = nx.DiGraph(
+    ...     [(0, 1, {"cost": 1}), (1, 2, {"cost": 1}), (0, 2, {"cost": 42})]
+    ... )
+    >>> list(nx.all_simple_paths(DG, 0, 2))
+    [[0, 1, 2], [0, 2]]
+    >>> nx.dag_longest_path_length(DG)
+    2
+    >>> nx.dag_longest_path_length(DG, weight="cost")
+    42
+
+    See also
+    --------
+    dag_longest_path
+    """
+    path = nx.dag_longest_path(G, weight, default_weight)
+    path_length = 0
+    if G.is_multigraph():
+        for u, v in pairwise(path):
+            i = max(G[u][v], key=lambda x: G[u][v][x].get(weight, default_weight))
+            path_length += G[u][v][i].get(weight, default_weight)
+    else:
+        for u, v in pairwise(path):
+            path_length += G[u][v].get(weight, default_weight)
+
+    return path_length
+
+
+@nx._dispatchable
+def root_to_leaf_paths(G):
+    """Yields root-to-leaf paths in a directed acyclic graph.
+
+    `G` must be a directed acyclic graph. If not, the behavior of this
+    function is undefined. A "root" in this graph is a node of in-degree
+    zero and a "leaf" a node of out-degree zero.
+
+    When invoked, this function iterates over each path from any root to
+    any leaf. A path is a list of nodes.
+
+    """
+    roots = (v for v, d in G.in_degree() if d == 0)
+    leaves = (v for v, d in G.out_degree() if d == 0)
+    all_paths = partial(nx.all_simple_paths, G)
+    # TODO In Python 3, this would be better as `yield from ...`.
+    return chaini(starmap(all_paths, product(roots, leaves)))
+
+
+@not_implemented_for("multigraph")
+@not_implemented_for("undirected")
+@nx._dispatchable(returns_graph=True)
+def dag_to_branching(G):
+    """Returns a branching representing all (overlapping) paths from
+    root nodes to leaf nodes in the given directed acyclic graph.
+
+    As described in :mod:`networkx.algorithms.tree.recognition`, a
+    *branching* is a directed forest in which each node has at most one
+    parent. In other words, a branching is a disjoint union of
+    *arborescences*. For this function, each node of in-degree zero in
+    `G` becomes a root of one of the arborescences, and there will be
+    one leaf node for each distinct path from that root to a leaf node
+    in `G`.
+
+    Each node `v` in `G` with *k* parents becomes *k* distinct nodes in
+    the returned branching, one for each parent, and the sub-DAG rooted
+    at `v` is duplicated for each copy. The algorithm then recurses on
+    the children of each copy of `v`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A directed acyclic graph.
+
+    Returns
+    -------
+    DiGraph
+        The branching in which there is a bijection between root-to-leaf
+        paths in `G` (in which multiple paths may share the same leaf)
+        and root-to-leaf paths in the branching (in which there is a
+        unique path from a root to a leaf).
+
+        Each node has an attribute 'source' whose value is the original
+        node to which this node corresponds. No other graph, node, or
+        edge attributes are copied into this new graph.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is not directed, or if `G` is a multigraph.
+
+    HasACycle
+        If `G` is not acyclic.
+
+    Examples
+    --------
+    To examine which nodes in the returned branching were produced by
+    which original node in the directed acyclic graph, we can collect
+    the mapping from source node to new nodes into a dictionary. For
+    example, consider the directed diamond graph::
+
+        >>> from collections import defaultdict
+        >>> from operator import itemgetter
+        >>>
+        >>> G = nx.DiGraph(nx.utils.pairwise("abd"))
+        >>> G.add_edges_from(nx.utils.pairwise("acd"))
+        >>> B = nx.dag_to_branching(G)
+        >>>
+        >>> sources = defaultdict(set)
+        >>> for v, source in B.nodes(data="source"):
+        ...     sources[source].add(v)
+        >>> len(sources["a"])
+        1
+        >>> len(sources["d"])
+        2
+
+    To copy node attributes from the original graph to the new graph,
+    you can use a dictionary like the one constructed in the above
+    example::
+
+        >>> for source, nodes in sources.items():
+        ...     for v in nodes:
+        ...         B.nodes[v].update(G.nodes[source])
+
+    Notes
+    -----
+    This function is not idempotent in the sense that the node labels in
+    the returned branching may be uniquely generated each time the
+    function is invoked. In fact, the node labels may not be integers;
+    in order to relabel the nodes to be more readable, you can use the
+    :func:`networkx.convert_node_labels_to_integers` function.
+
+    The current implementation of this function uses
+    :func:`networkx.prefix_tree`, so it is subject to the limitations of
+    that function.
+
+    """
+    if has_cycle(G):
+        msg = "dag_to_branching is only defined for acyclic graphs"
+        raise nx.HasACycle(msg)
+    paths = root_to_leaf_paths(G)
+    B = nx.prefix_tree(paths)
+    # Remove the synthetic `root`(0) and `NIL`(-1) nodes from the tree
+    B.remove_node(0)
+    B.remove_node(-1)
+    return B
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def compute_v_structures(G):
+    """Yields 3-node tuples that represent the v-structures in `G`.
+
+    .. deprecated:: 3.4
+
+       `compute_v_structures` actually yields colliders. It will be removed in
+       version 3.6. Use `nx.dag.v_structures` or `nx.dag.colliders` instead.
+
+    Colliders are triples in the directed acyclic graph (DAG) where two parent nodes
+    point to the same child node. V-structures are colliders where the two parent
+    nodes are not adjacent. In a causal graph setting, the parents do not directly
+    depend on each other, but conditioning on the child node provides an association.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx `~networkx.DiGraph`.
+
+    Yields
+    ------
+    A 3-tuple representation of a v-structure
+        Each v-structure is a 3-tuple with the parent, collider, and other parent.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is an undirected graph.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 2), (0, 4), (3, 1), (2, 4), (0, 5), (4, 5), (1, 5)])
+    >>> nx.is_directed_acyclic_graph(G)
+    True
+    >>> list(nx.compute_v_structures(G))
+    [(0, 4, 2), (0, 5, 4), (0, 5, 1), (4, 5, 1)]
+
+    See Also
+    --------
+    v_structures
+    colliders
+
+    Notes
+    -----
+    This function was written to be used on DAGs, however it works on cyclic graphs
+    too. Since colliders are referred to in the cyclic causal graph literature
+    [2]_ we allow cyclic graphs in this function. It is suggested that you test if
+    your input graph is acyclic as in the example if you want that property.
+
+    References
+    ----------
+    .. [1]  `Pearl's PRIMER <https://bayes.cs.ucla.edu/PRIMER/primer-ch2.pdf>`_
+            Ch-2 page 50: v-structures def.
+    .. [2] A Hyttinen, P.O. Hoyer, F. Eberhardt, M J ̈arvisalo, (2013)
+           "Discovering cyclic causal models with latent variables:
+           a general SAT-based procedure", UAI'13: Proceedings of the Twenty-Ninth
+           Conference on Uncertainty in Artificial Intelligence, pg 301–310,
+           `doi:10.5555/3023638.3023669 <https://dl.acm.org/doi/10.5555/3023638.3023669>`_
+    """
+    import warnings
+
+    warnings.warn(
+        (
+            "\n\n`compute_v_structures` actually yields colliders. It will be\n"
+            "removed in version 3.6. Use `nx.dag.v_structures` or `nx.dag.colliders`\n"
+            "instead.\n"
+        ),
+        category=DeprecationWarning,
+        stacklevel=5,
+    )
+
+    return colliders(G)
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def v_structures(G):
+    """Yields 3-node tuples that represent the v-structures in `G`.
+
+    Colliders are triples in the directed acyclic graph (DAG) where two parent nodes
+    point to the same child node. V-structures are colliders where the two parent
+    nodes are not adjacent. In a causal graph setting, the parents do not directly
+    depend on each other, but conditioning on the child node provides an association.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx `~networkx.DiGraph`.
+
+    Yields
+    ------
+    A 3-tuple representation of a v-structure
+        Each v-structure is a 3-tuple with the parent, collider, and other parent.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is an undirected graph.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 2), (0, 4), (3, 1), (2, 4), (0, 5), (4, 5), (1, 5)])
+    >>> nx.is_directed_acyclic_graph(G)
+    True
+    >>> list(nx.dag.v_structures(G))
+    [(0, 4, 2), (0, 5, 1), (4, 5, 1)]
+
+    See Also
+    --------
+    colliders
+
+    Notes
+    -----
+    This function was written to be used on DAGs, however it works on cyclic graphs
+    too. Since colliders are referred to in the cyclic causal graph literature
+    [2]_ we allow cyclic graphs in this function. It is suggested that you test if
+    your input graph is acyclic as in the example if you want that property.
+
+    References
+    ----------
+    .. [1]  `Pearl's PRIMER <https://bayes.cs.ucla.edu/PRIMER/primer-ch2.pdf>`_
+            Ch-2 page 50: v-structures def.
+    .. [2] A Hyttinen, P.O. Hoyer, F. Eberhardt, M J ̈arvisalo, (2013)
+           "Discovering cyclic causal models with latent variables:
+           a general SAT-based procedure", UAI'13: Proceedings of the Twenty-Ninth
+           Conference on Uncertainty in Artificial Intelligence, pg 301–310,
+           `doi:10.5555/3023638.3023669 <https://dl.acm.org/doi/10.5555/3023638.3023669>`_
+    """
+    for p1, c, p2 in colliders(G):
+        if not (G.has_edge(p1, p2) or G.has_edge(p2, p1)):
+            yield (p1, c, p2)
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def colliders(G):
+    """Yields 3-node tuples that represent the colliders in `G`.
+
+    In a Directed Acyclic Graph (DAG), if you have three nodes A, B, and C, and
+    there are edges from A to C and from B to C, then C is a collider [1]_ . In
+    a causal graph setting, this means that both events A and B are "causing" C,
+    and conditioning on C provide an association between A and B even if
+    no direct causal relationship exists between A and B.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx `~networkx.DiGraph`.
+
+    Yields
+    ------
+    A 3-tuple representation of a collider
+        Each collider is a 3-tuple with the parent, collider, and other parent.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is an undirected graph.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 2), (0, 4), (3, 1), (2, 4), (0, 5), (4, 5), (1, 5)])
+    >>> nx.is_directed_acyclic_graph(G)
+    True
+    >>> list(nx.dag.colliders(G))
+    [(0, 4, 2), (0, 5, 4), (0, 5, 1), (4, 5, 1)]
+
+    See Also
+    --------
+    v_structures
+
+    Notes
+    -----
+    This function was written to be used on DAGs, however it works on cyclic graphs
+    too. Since colliders are referred to in the cyclic causal graph literature
+    [2]_ we allow cyclic graphs in this function. It is suggested that you test if
+    your input graph is acyclic as in the example if you want that property.
+
+    References
+    ----------
+    .. [1] `Wikipedia: Collider in causal graphs <https://en.wikipedia.org/wiki/Collider_(statistics)>`_
+    .. [2] A Hyttinen, P.O. Hoyer, F. Eberhardt, M J ̈arvisalo, (2013)
+           "Discovering cyclic causal models with latent variables:
+           a general SAT-based procedure", UAI'13: Proceedings of the Twenty-Ninth
+           Conference on Uncertainty in Artificial Intelligence, pg 301–310,
+           `doi:10.5555/3023638.3023669 <https://dl.acm.org/doi/10.5555/3023638.3023669>`_
+    """
+    for node in G.nodes:
+        for p1, p2 in combinations(G.predecessors(node), 2):
+            yield (p1, node, p2)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/distance_measures.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/distance_measures.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ce3fb02e21f5d601c949d39c53fc7d8e631b408
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/distance_measures.py
@@ -0,0 +1,1159 @@
+"""Graph diameter, radius, eccentricity and other properties."""
+
+import math
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = [
+    "eccentricity",
+    "diameter",
+    "harmonic_diameter",
+    "radius",
+    "periphery",
+    "center",
+    "barycenter",
+    "resistance_distance",
+    "kemeny_constant",
+    "effective_graph_resistance",
+]
+
+
+@not_implemented_for("directed")
+def _tree_center(G):
+    """Returns the center of an undirected tree graph.
+
+    The center of a tree consists of nodes that minimize the maximum eccentricity.
+    That is, these nodes minimize the maximum distance to all other nodes.
+    This implementation currently only works for unweighted edges.
+
+    If the input graph is not a tree, results are not guaranteed to be correct and while
+    some non-trees will raise a ``NetworkXError`` not all non-trees will be discovered.
+    Thus, this function should not be used if caller is unsure whether the input graph
+    is a tree. Use ``networkx.is_tree(G)`` to check.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A tree graph (undirected, acyclic graph).
+
+    Returns
+    -------
+    center : list
+        A list of nodes in the center of the tree. This could be one or two nodes.
+
+    Raises
+    ------
+    NetworkXError
+        If algorithm detects input graph is not a tree. There is no guarantee
+        this error will always raise if a non-tree is passed.
+
+    Notes
+    -----
+    This algorithm iteratively removes leaves (nodes with degree 1) from the tree until
+    there are only 1 or 2 nodes left. The remaining nodes form the center of the tree.
+
+    This algorithm's time complexity is O(N) where N is the number of nodes in the tree.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (2, 4), (2, 5)])
+    >>> _tree_center(G)
+    [1, 2]
+
+    >>> G = nx.Graph([(1, 2), (2, 3), (3, 4), (4, 5)])
+    >>> _tree_center(G)
+    [3]
+    """
+    center_candidates_degree = dict(G.degree)
+    leaves = {node for node, degree in center_candidates_degree.items() if degree == 1}
+
+    # It's better to fail than an infinite loop, so check leaves to ensure progress
+    while len(center_candidates_degree) > 2 and leaves:
+        new_leaves = set()
+        for leaf in leaves:
+            del center_candidates_degree[leaf]
+            for neighbor in G.neighbors(leaf):
+                if neighbor not in center_candidates_degree:
+                    continue
+                center_candidates_degree[neighbor] -= 1
+                if center_candidates_degree[neighbor] == 1:
+                    new_leaves.add(neighbor)
+        leaves = new_leaves
+
+    if not leaves and len(center_candidates_degree) >= 2:
+        # We detected graph is not a tree. This check does not cover all cases.
+        # For example, it does not cover the case where we have two islands (A-B) and (B-C)
+        # where we might eliminate (B-C) leaves and return [A, B] as centers.
+        raise nx.NetworkXError("Input graph is not a tree")
+
+    return list(center_candidates_degree)
+
+
+def _extrema_bounding(G, compute="diameter", weight=None):
+    """Compute requested extreme distance metric of undirected graph G
+
+    Computation is based on smart lower and upper bounds, and in practice
+    linear in the number of nodes, rather than quadratic (except for some
+    border cases such as complete graphs or circle shaped graphs).
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       An undirected graph
+
+    compute : string denoting the requesting metric
+       "diameter" for the maximal eccentricity value,
+       "radius" for the minimal eccentricity value,
+       "periphery" for the set of nodes with eccentricity equal to the diameter,
+       "center" for the set of nodes with eccentricity equal to the radius,
+       "eccentricities" for the maximum distance from each node to all other nodes in G
+
+    weight : string, function, or None
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+        If this is None, every edge has weight/distance/cost 1.
+
+        Weights stored as floating point values can lead to small round-off
+        errors in distances. Use integer weights to avoid this.
+
+        Weights should be positive, since they are distances.
+
+    Returns
+    -------
+    value : value of the requested metric
+       int for "diameter" and "radius" or
+       list of nodes for "center" and "periphery" or
+       dictionary of eccentricity values keyed by node for "eccentricities"
+
+    Raises
+    ------
+    NetworkXError
+        If the graph consists of multiple components
+    ValueError
+        If `compute` is not one of "diameter", "radius", "periphery", "center", or "eccentricities".
+
+    Notes
+    -----
+    This algorithm was proposed in [1]_ and discussed further in [2]_ and [3]_.
+
+    References
+    ----------
+    .. [1] F. W. Takes, W. A. Kosters,
+       "Determining the diameter of small world networks."
+       Proceedings of the 20th ACM international conference on Information and
+       knowledge management, 2011
+       https://dl.acm.org/doi/abs/10.1145/2063576.2063748
+    .. [2] F. W. Takes, W. A. Kosters,
+       "Computing the Eccentricity Distribution of Large Graphs."
+       Algorithms, 2013
+       https://www.mdpi.com/1999-4893/6/1/100
+    .. [3] M. Borassi, P. Crescenzi, M. Habib, W. A. Kosters, A. Marino, F. W. Takes,
+       "Fast diameter and radius BFS-based computation in (weakly connected)
+       real-world graphs: With an application to the six degrees of separation
+       games."
+       Theoretical Computer Science, 2015
+       https://www.sciencedirect.com/science/article/pii/S0304397515001644
+    """
+    # init variables
+    degrees = dict(G.degree())  # start with the highest degree node
+    minlowernode = max(degrees, key=degrees.get)
+    N = len(degrees)  # number of nodes
+    # alternate between smallest lower and largest upper bound
+    high = False
+    # status variables
+    ecc_lower = dict.fromkeys(G, 0)
+    ecc_upper = dict.fromkeys(G, math.inf)
+    candidates = set(G)
+
+    # (re)set bound extremes
+    minlower = math.inf
+    maxlower = 0
+    minupper = math.inf
+    maxupper = 0
+
+    # repeat the following until there are no more candidates
+    while candidates:
+        if high:
+            current = maxuppernode  # select node with largest upper bound
+        else:
+            current = minlowernode  # select node with smallest lower bound
+        high = not high
+
+        # get distances from/to current node and derive eccentricity
+        dist = nx.shortest_path_length(G, source=current, weight=weight)
+
+        if len(dist) != N:
+            msg = "Cannot compute metric because graph is not connected."
+            raise nx.NetworkXError(msg)
+        current_ecc = max(dist.values())
+
+        # print status update
+        #        print ("ecc of " + str(current) + " (" + str(ecc_lower[current]) + "/"
+        #        + str(ecc_upper[current]) + ", deg: " + str(dist[current]) + ") is "
+        #        + str(current_ecc))
+        #        print(ecc_upper)
+
+        # (re)set bound extremes
+        maxuppernode = None
+        minlowernode = None
+
+        # update node bounds
+        for i in candidates:
+            # update eccentricity bounds
+            d = dist[i]
+            ecc_lower[i] = low = max(ecc_lower[i], max(d, (current_ecc - d)))
+            ecc_upper[i] = upp = min(ecc_upper[i], current_ecc + d)
+
+            # update min/max values of lower and upper bounds
+            minlower = min(ecc_lower[i], minlower)
+            maxlower = max(ecc_lower[i], maxlower)
+            minupper = min(ecc_upper[i], minupper)
+            maxupper = max(ecc_upper[i], maxupper)
+
+        # update candidate set
+        if compute == "diameter":
+            ruled_out = {
+                i
+                for i in candidates
+                if ecc_upper[i] <= maxlower and 2 * ecc_lower[i] >= maxupper
+            }
+        elif compute == "radius":
+            ruled_out = {
+                i
+                for i in candidates
+                if ecc_lower[i] >= minupper and ecc_upper[i] + 1 <= 2 * minlower
+            }
+        elif compute == "periphery":
+            ruled_out = {
+                i
+                for i in candidates
+                if ecc_upper[i] < maxlower
+                and (maxlower == maxupper or ecc_lower[i] > maxupper)
+            }
+        elif compute == "center":
+            ruled_out = {
+                i
+                for i in candidates
+                if ecc_lower[i] > minupper
+                and (minlower == minupper or ecc_upper[i] + 1 < 2 * minlower)
+            }
+        elif compute == "eccentricities":
+            ruled_out = set()
+        else:
+            msg = "compute must be one of 'diameter', 'radius', 'periphery', 'center', 'eccentricities'"
+            raise ValueError(msg)
+
+        ruled_out.update(i for i in candidates if ecc_lower[i] == ecc_upper[i])
+        candidates -= ruled_out
+
+        #        for i in ruled_out:
+        #            print("removing %g: ecc_u: %g maxl: %g ecc_l: %g maxu: %g"%
+        #                    (i,ecc_upper[i],maxlower,ecc_lower[i],maxupper))
+        #        print("node %g: ecc_u: %g maxl: %g ecc_l: %g maxu: %g"%
+        #                    (4,ecc_upper[4],maxlower,ecc_lower[4],maxupper))
+        #        print("NODE 4: %g"%(ecc_upper[4] <= maxlower))
+        #        print("NODE 4: %g"%(2 * ecc_lower[4] >= maxupper))
+        #        print("NODE 4: %g"%(ecc_upper[4] <= maxlower
+        #                            and 2 * ecc_lower[4] >= maxupper))
+
+        # updating maxuppernode and minlowernode for selection in next round
+        for i in candidates:
+            if (
+                minlowernode is None
+                or (
+                    ecc_lower[i] == ecc_lower[minlowernode]
+                    and degrees[i] > degrees[minlowernode]
+                )
+                or (ecc_lower[i] < ecc_lower[minlowernode])
+            ):
+                minlowernode = i
+
+            if (
+                maxuppernode is None
+                or (
+                    ecc_upper[i] == ecc_upper[maxuppernode]
+                    and degrees[i] > degrees[maxuppernode]
+                )
+                or (ecc_upper[i] > ecc_upper[maxuppernode])
+            ):
+                maxuppernode = i
+
+        # print status update
+    #        print (" min=" + str(minlower) + "/" + str(minupper) +
+    #        " max=" + str(maxlower) + "/" + str(maxupper) +
+    #        " candidates: " + str(len(candidates)))
+    #        print("cand:",candidates)
+    #        print("ecc_l",ecc_lower)
+    #        print("ecc_u",ecc_upper)
+    #        wait = input("press Enter to continue")
+
+    # return the correct value of the requested metric
+    if compute == "diameter":
+        return maxlower
+    if compute == "radius":
+        return minupper
+    if compute == "periphery":
+        p = [v for v in G if ecc_lower[v] == maxlower]
+        return p
+    if compute == "center":
+        c = [v for v in G if ecc_upper[v] == minupper]
+        return c
+    if compute == "eccentricities":
+        return ecc_lower
+    return None
+
+
+@nx._dispatchable(edge_attrs="weight")
+def eccentricity(G, v=None, sp=None, weight=None):
+    """Returns the eccentricity of nodes in G.
+
+    The eccentricity of a node v is the maximum distance from v to
+    all other nodes in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       A graph
+
+    v : node, optional
+       Return value of specified node
+
+    sp : dict of dicts, optional
+       All pairs shortest path lengths as a dictionary of dictionaries
+
+    weight : string, function, or None (default=None)
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+        If this is None, every edge has weight/distance/cost 1.
+
+        Weights stored as floating point values can lead to small round-off
+        errors in distances. Use integer weights to avoid this.
+
+        Weights should be positive, since they are distances.
+
+    Returns
+    -------
+    ecc : dictionary
+       A dictionary of eccentricity values keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)])
+    >>> dict(nx.eccentricity(G))
+    {1: 2, 2: 3, 3: 2, 4: 2, 5: 3}
+
+    >>> dict(
+    ...     nx.eccentricity(G, v=[1, 5])
+    ... )  # This returns the eccentricity of node 1 & 5
+    {1: 2, 5: 3}
+
+    """
+    #    if v is None:                # none, use entire graph
+    #        nodes=G.nodes()
+    #    elif v in G:               # is v a single node
+    #        nodes=[v]
+    #    else:                      # assume v is a container of nodes
+    #        nodes=v
+    order = G.order()
+    e = {}
+    for n in G.nbunch_iter(v):
+        if sp is None:
+            length = nx.shortest_path_length(G, source=n, weight=weight)
+
+            L = len(length)
+        else:
+            try:
+                length = sp[n]
+                L = len(length)
+            except TypeError as err:
+                raise nx.NetworkXError('Format of "sp" is invalid.') from err
+        if L != order:
+            if G.is_directed():
+                msg = (
+                    "Found infinite path length because the digraph is not"
+                    " strongly connected"
+                )
+            else:
+                msg = "Found infinite path length because the graph is not connected"
+            raise nx.NetworkXError(msg)
+
+        e[n] = max(length.values())
+
+    if v in G:
+        return e[v]  # return single value
+    return e
+
+
+@nx._dispatchable(edge_attrs="weight")
+def diameter(G, e=None, usebounds=False, weight=None):
+    """Returns the diameter of the graph G.
+
+    The diameter is the maximum eccentricity.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       A graph
+
+    e : eccentricity dictionary, optional
+      A precomputed dictionary of eccentricities.
+
+    usebounds : bool, optional
+        If `True`, use extrema bounding (see Notes) when computing the diameter
+        for undirected graphs. Extrema bounding may accelerate the
+        distance calculation for some graphs. `usebounds` is ignored if `G` is
+        directed or if `e` is not `None`. Default is `False`.
+
+    weight : string, function, or None
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+        If this is None, every edge has weight/distance/cost 1.
+
+        Weights stored as floating point values can lead to small round-off
+        errors in distances. Use integer weights to avoid this.
+
+        Weights should be positive, since they are distances.
+
+    Returns
+    -------
+    d : integer
+       Diameter of graph
+
+    Notes
+    -----
+    When ``usebounds=True``, the computation makes use of smart lower
+    and upper bounds and is often linear in the number of nodes, rather than
+    quadratic (except for some border cases such as complete graphs or circle
+    shaped-graphs).
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)])
+    >>> nx.diameter(G)
+    3
+
+    See Also
+    --------
+    eccentricity
+    """
+    if usebounds is True and e is None and not G.is_directed():
+        return _extrema_bounding(G, compute="diameter", weight=weight)
+    if e is None:
+        e = eccentricity(G, weight=weight)
+    return max(e.values())
+
+
+@nx._dispatchable(edge_attrs="weight")
+def harmonic_diameter(G, sp=None, *, weight=None):
+    """Returns the harmonic diameter of the graph G.
+
+    The harmonic diameter of a graph is the harmonic mean of the distances
+    between all pairs of distinct vertices. Graphs that are not strongly
+    connected have infinite diameter and mean distance, making such
+    measures not useful. Restricting the diameter or mean distance to
+    finite distances yields paradoxical values (e.g., a perfect match
+    would have diameter one). The harmonic mean handles gracefully
+    infinite distances (e.g., a perfect match has harmonic diameter equal
+    to the number of vertices minus one), making it possible to assign a
+    meaningful value to all graphs.
+
+    Note that in [1] the harmonic diameter is called "connectivity length":
+    however, "harmonic diameter" is a more standard name from the
+    theory of metric spaces. The name "harmonic mean distance" is perhaps
+    a more descriptive name, but is not used in the literature, so we use the
+    name "harmonic diameter" here.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       A graph
+
+    sp : dict of dicts, optional
+       All-pairs shortest path lengths as a dictionary of dictionaries
+
+    weight : string, function, or None (default=None)
+        If None, every edge has weight/distance 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        If a function, the weight of an edge is the value returned by the function.
+        The function must accept exactly three positional arguments:
+        the two endpoints of an edge and the dictionary of edge attributes for
+        that edge. The function must return a number.
+
+    Returns
+    -------
+    hd : float
+       Harmonic diameter of graph
+
+    References
+    ----------
+    .. [1] Massimo Marchiori and Vito Latora, "Harmony in the small-world".
+           *Physica A: Statistical Mechanics and Its Applications*
+           285(3-4), pages 539-546, 2000.
+           <https://doi.org/10.1016/S0378-4371(00)00311-3>
+    """
+    order = G.order()
+
+    sum_invd = 0
+    for n in G:
+        if sp is None:
+            length = nx.single_source_dijkstra_path_length(G, n, weight=weight)
+        else:
+            try:
+                length = sp[n]
+                L = len(length)
+            except TypeError as err:
+                raise nx.NetworkXError('Format of "sp" is invalid.') from err
+
+        for d in length.values():
+            # Note that this will skip the zero distance from n to itself,
+            # as it should be, but also zero-weight paths in weighted graphs.
+            if d != 0:
+                sum_invd += 1 / d
+
+    if sum_invd != 0:
+        return order * (order - 1) / sum_invd
+    if order > 1:
+        return math.inf
+    return math.nan
+
+
+@nx._dispatchable(edge_attrs="weight")
+def periphery(G, e=None, usebounds=False, weight=None):
+    """Returns the periphery of the graph G.
+
+    The periphery is the set of nodes with eccentricity equal to the diameter.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       A graph
+
+    e : eccentricity dictionary, optional
+      A precomputed dictionary of eccentricities.
+
+    usebounds : bool, optional
+        If `True`, use extrema bounding (see Notes) when computing the periphery
+        for undirected graphs. Extrema bounding may accelerate the
+        distance calculation for some graphs. `usebounds` is ignored if `G` is
+        directed or if `e` is not `None`. Default is `False`.
+
+    weight : string, function, or None
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+        If this is None, every edge has weight/distance/cost 1.
+
+        Weights stored as floating point values can lead to small round-off
+        errors in distances. Use integer weights to avoid this.
+
+        Weights should be positive, since they are distances.
+
+    Returns
+    -------
+    p : list
+       List of nodes in periphery
+
+    Notes
+    -----
+    When ``usebounds=True``, the computation makes use of smart lower
+    and upper bounds and is often linear in the number of nodes, rather than
+    quadratic (except for some border cases such as complete graphs or circle
+    shaped-graphs).
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)])
+    >>> nx.periphery(G)
+    [2, 5]
+
+    See Also
+    --------
+    barycenter
+    center
+    """
+    if usebounds is True and e is None and not G.is_directed():
+        return _extrema_bounding(G, compute="periphery", weight=weight)
+    if e is None:
+        e = eccentricity(G, weight=weight)
+    diameter = max(e.values())
+    p = [v for v in e if e[v] == diameter]
+    return p
+
+
+@nx._dispatchable(edge_attrs="weight")
+def radius(G, e=None, usebounds=False, weight=None):
+    """Returns the radius of the graph G.
+
+    The radius is the minimum eccentricity.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       A graph
+
+    e : eccentricity dictionary, optional
+      A precomputed dictionary of eccentricities.
+
+    usebounds : bool, optional
+        If `True`, use extrema bounding (see Notes) when computing the radius
+        for undirected graphs. Extrema bounding may accelerate the
+        distance calculation for some graphs. `usebounds` is ignored if `G` is
+        directed or if `e` is not `None`. Default is `False`.
+
+    weight : string, function, or None
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+        If this is None, every edge has weight/distance/cost 1.
+
+        Weights stored as floating point values can lead to small round-off
+        errors in distances. Use integer weights to avoid this.
+
+        Weights should be positive, since they are distances.
+
+    Returns
+    -------
+    r : integer
+       Radius of graph
+
+    Notes
+    -----
+    When ``usebounds=True``, the computation makes use of smart lower
+    and upper bounds and is often linear in the number of nodes, rather than
+    quadratic (except for some border cases such as complete graphs or circle
+    shaped-graphs).
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)])
+    >>> nx.radius(G)
+    2
+
+    """
+    if usebounds is True and e is None and not G.is_directed():
+        return _extrema_bounding(G, compute="radius", weight=weight)
+    if e is None:
+        e = eccentricity(G, weight=weight)
+    return min(e.values())
+
+
+@nx._dispatchable(edge_attrs="weight")
+def center(G, e=None, usebounds=False, weight=None):
+    """Returns the center of the graph G.
+
+    The center is the set of nodes with eccentricity equal to radius.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       A graph
+
+    e : eccentricity dictionary, optional
+      A precomputed dictionary of eccentricities.
+
+    usebounds : bool, optional
+        If `True`, use extrema bounding (see Notes) when computing the center
+        for undirected graphs. Extrema bounding may accelerate the
+        distance calculation for some graphs. `usebounds` is ignored if `G` is
+        directed or if `e` is not `None`. Default is `False`.
+
+    weight : string, function, or None
+        If this is a string, then edge weights will be accessed via the
+        edge attribute with this key (that is, the weight of the edge
+        joining `u` to `v` will be ``G.edges[u, v][weight]``). If no
+        such edge attribute exists, the weight of the edge is assumed to
+        be one.
+
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number.
+
+        If this is None, every edge has weight/distance/cost 1.
+
+        Weights stored as floating point values can lead to small round-off
+        errors in distances. Use integer weights to avoid this.
+
+        Weights should be positive, since they are distances.
+
+    Returns
+    -------
+    c : list
+       List of nodes in center
+
+    Notes
+    -----
+    When ``usebounds=True``, the computation makes use of smart lower
+    and upper bounds and is often linear in the number of nodes, rather than
+    quadratic (except for some border cases such as complete graphs or circle
+    shaped-graphs).
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)])
+    >>> list(nx.center(G))
+    [1, 3, 4]
+
+    See Also
+    --------
+    barycenter
+    periphery
+    """
+    if usebounds is True and e is None and not G.is_directed():
+        return _extrema_bounding(G, compute="center", weight=weight)
+    if e is None and weight is None and not G.is_directed() and nx.is_tree(G):
+        return _tree_center(G)
+    if e is None:
+        e = eccentricity(G, weight=weight)
+    radius = min(e.values())
+    p = [v for v in e if e[v] == radius]
+    return p
+
+
+@nx._dispatchable(edge_attrs="weight", mutates_input={"attr": 2})
+def barycenter(G, weight=None, attr=None, sp=None):
+    r"""Calculate barycenter of a connected graph, optionally with edge weights.
+
+    The :dfn:`barycenter` a
+    :func:`connected <networkx.algorithms.components.is_connected>` graph
+    :math:`G` is the subgraph induced by the set of its nodes :math:`v`
+    minimizing the objective function
+
+    .. math::
+
+        \sum_{u \in V(G)} d_G(u, v),
+
+    where :math:`d_G` is the (possibly weighted) :func:`path length
+    <networkx.algorithms.shortest_paths.generic.shortest_path_length>`.
+    The barycenter is also called the :dfn:`median`. See [West01]_, p. 78.
+
+    Parameters
+    ----------
+    G : :class:`networkx.Graph`
+        The connected graph :math:`G`.
+    weight : :class:`str`, optional
+        Passed through to
+        :func:`~networkx.algorithms.shortest_paths.generic.shortest_path_length`.
+    attr : :class:`str`, optional
+        If given, write the value of the objective function to each node's
+        `attr` attribute. Otherwise do not store the value.
+    sp : dict of dicts, optional
+       All pairs shortest path lengths as a dictionary of dictionaries
+
+    Returns
+    -------
+    list
+        Nodes of `G` that induce the barycenter of `G`.
+
+    Raises
+    ------
+    NetworkXNoPath
+        If `G` is disconnected. `G` may appear disconnected to
+        :func:`barycenter` if `sp` is given but is missing shortest path
+        lengths for any pairs.
+    ValueError
+        If `sp` and `weight` are both given.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)])
+    >>> nx.barycenter(G)
+    [1, 3, 4]
+
+    See Also
+    --------
+    center
+    periphery
+    """
+    if sp is None:
+        sp = nx.shortest_path_length(G, weight=weight)
+    else:
+        sp = sp.items()
+        if weight is not None:
+            raise ValueError("Cannot use both sp, weight arguments together")
+    smallest, barycenter_vertices, n = float("inf"), [], len(G)
+    for v, dists in sp:
+        if len(dists) < n:
+            raise nx.NetworkXNoPath(
+                f"Input graph {G} is disconnected, so every induced subgraph "
+                "has infinite barycentricity."
+            )
+        barycentricity = sum(dists.values())
+        if attr is not None:
+            G.nodes[v][attr] = barycentricity
+        if barycentricity < smallest:
+            smallest = barycentricity
+            barycenter_vertices = [v]
+        elif barycentricity == smallest:
+            barycenter_vertices.append(v)
+    if attr is not None:
+        nx._clear_cache(G)
+    return barycenter_vertices
+
+
+@not_implemented_for("directed")
+@nx._dispatchable(edge_attrs="weight")
+def resistance_distance(G, nodeA=None, nodeB=None, weight=None, invert_weight=True):
+    """Returns the resistance distance between pairs of nodes in graph G.
+
+    The resistance distance between two nodes of a graph is akin to treating
+    the graph as a grid of resistors with a resistance equal to the provided
+    weight [1]_, [2]_.
+
+    If weight is not provided, then a weight of 1 is used for all edges.
+
+    If two nodes are the same, the resistance distance is zero.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       A graph
+
+    nodeA : node or None, optional (default=None)
+      A node within graph G.
+      If None, compute resistance distance using all nodes as source nodes.
+
+    nodeB : node or None, optional (default=None)
+      A node within graph G.
+      If None, compute resistance distance using all nodes as target nodes.
+
+    weight : string or None, optional (default=None)
+       The edge data key used to compute the resistance distance.
+       If None, then each edge has weight 1.
+
+    invert_weight : boolean (default=True)
+        Proper calculation of resistance distance requires building the
+        Laplacian matrix with the reciprocal of the weight. Not required
+        if the weight is already inverted. Weight cannot be zero.
+
+    Returns
+    -------
+    rd : dict or float
+       If `nodeA` and `nodeB` are given, resistance distance between `nodeA`
+       and `nodeB`. If `nodeA` or `nodeB` is unspecified (the default), a
+       dictionary of nodes with resistance distances as the value.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is a directed graph.
+
+    NetworkXError
+        If `G` is not connected, or contains no nodes,
+        or `nodeA` is not in `G` or `nodeB` is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)])
+    >>> round(nx.resistance_distance(G, 1, 3), 10)
+    0.625
+
+    Notes
+    -----
+    The implementation is based on Theorem A in [2]_. Self-loops are ignored.
+    Multi-edges are contracted in one edge with weight equal to the harmonic sum of the weights.
+
+    References
+    ----------
+    .. [1] Wikipedia
+       "Resistance distance."
+       https://en.wikipedia.org/wiki/Resistance_distance
+    .. [2] D. J. Klein and M. Randic.
+        Resistance distance.
+        J. of Math. Chem. 12:81-95, 1993.
+    """
+    import numpy as np
+
+    if len(G) == 0:
+        raise nx.NetworkXError("Graph G must contain at least one node.")
+    if not nx.is_connected(G):
+        raise nx.NetworkXError("Graph G must be strongly connected.")
+    if nodeA is not None and nodeA not in G:
+        raise nx.NetworkXError("Node A is not in graph G.")
+    if nodeB is not None and nodeB not in G:
+        raise nx.NetworkXError("Node B is not in graph G.")
+
+    G = G.copy()
+    node_list = list(G)
+
+    # Invert weights
+    if invert_weight and weight is not None:
+        if G.is_multigraph():
+            for u, v, k, d in G.edges(keys=True, data=True):
+                d[weight] = 1 / d[weight]
+        else:
+            for u, v, d in G.edges(data=True):
+                d[weight] = 1 / d[weight]
+
+    # Compute resistance distance using the Pseudo-inverse of the Laplacian
+    # Self-loops are ignored
+    L = nx.laplacian_matrix(G, weight=weight).todense()
+    Linv = np.linalg.pinv(L, hermitian=True)
+
+    # Return relevant distances
+    if nodeA is not None and nodeB is not None:
+        i = node_list.index(nodeA)
+        j = node_list.index(nodeB)
+        return Linv.item(i, i) + Linv.item(j, j) - Linv.item(i, j) - Linv.item(j, i)
+
+    elif nodeA is not None:
+        i = node_list.index(nodeA)
+        d = {}
+        for n in G:
+            j = node_list.index(n)
+            d[n] = Linv.item(i, i) + Linv.item(j, j) - Linv.item(i, j) - Linv.item(j, i)
+        return d
+
+    elif nodeB is not None:
+        j = node_list.index(nodeB)
+        d = {}
+        for n in G:
+            i = node_list.index(n)
+            d[n] = Linv.item(i, i) + Linv.item(j, j) - Linv.item(i, j) - Linv.item(j, i)
+        return d
+
+    else:
+        d = {}
+        for n in G:
+            i = node_list.index(n)
+            d[n] = {}
+            for n2 in G:
+                j = node_list.index(n2)
+                d[n][n2] = (
+                    Linv.item(i, i)
+                    + Linv.item(j, j)
+                    - Linv.item(i, j)
+                    - Linv.item(j, i)
+                )
+        return d
+
+
+@not_implemented_for("directed")
+@nx._dispatchable(edge_attrs="weight")
+def effective_graph_resistance(G, weight=None, invert_weight=True):
+    """Returns the Effective graph resistance of G.
+
+    Also known as the Kirchhoff index.
+
+    The effective graph resistance is defined as the sum
+    of the resistance distance of every node pair in G [1]_.
+
+    If weight is not provided, then a weight of 1 is used for all edges.
+
+    The effective graph resistance of a disconnected graph is infinite.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       A graph
+
+    weight : string or None, optional (default=None)
+       The edge data key used to compute the effective graph resistance.
+       If None, then each edge has weight 1.
+
+    invert_weight : boolean (default=True)
+        Proper calculation of resistance distance requires building the
+        Laplacian matrix with the reciprocal of the weight. Not required
+        if the weight is already inverted. Weight cannot be zero.
+
+    Returns
+    -------
+    RG : float
+        The effective graph resistance of `G`.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is a directed graph.
+
+    NetworkXError
+        If `G` does not contain any nodes.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (1, 4), (3, 4), (3, 5), (4, 5)])
+    >>> round(nx.effective_graph_resistance(G), 10)
+    10.25
+
+    Notes
+    -----
+    The implementation is based on Theorem 2.2 in [2]_. Self-loops are ignored.
+    Multi-edges are contracted in one edge with weight equal to the harmonic sum of the weights.
+
+    References
+    ----------
+    .. [1] Wolfram
+       "Kirchhoff Index."
+       https://mathworld.wolfram.com/KirchhoffIndex.html
+    .. [2] W. Ellens, F. M. Spieksma, P. Van Mieghem, A. Jamakovic, R. E. Kooij.
+        Effective graph resistance.
+        Lin. Alg. Appl. 435:2491-2506, 2011.
+    """
+    import numpy as np
+
+    if len(G) == 0:
+        raise nx.NetworkXError("Graph G must contain at least one node.")
+
+    # Disconnected graphs have infinite Effective graph resistance
+    if not nx.is_connected(G):
+        return float("inf")
+
+    # Invert weights
+    G = G.copy()
+    if invert_weight and weight is not None:
+        if G.is_multigraph():
+            for u, v, k, d in G.edges(keys=True, data=True):
+                d[weight] = 1 / d[weight]
+        else:
+            for u, v, d in G.edges(data=True):
+                d[weight] = 1 / d[weight]
+
+    # Get Laplacian eigenvalues
+    mu = np.sort(nx.laplacian_spectrum(G, weight=weight))
+
+    # Compute Effective graph resistance based on spectrum of the Laplacian
+    # Self-loops are ignored
+    return float(np.sum(1 / mu[1:]) * G.number_of_nodes())
+
+
+@nx.utils.not_implemented_for("directed")
+@nx._dispatchable(edge_attrs="weight")
+def kemeny_constant(G, *, weight=None):
+    """Returns the Kemeny constant of the given graph.
+
+    The *Kemeny constant* (or Kemeny's constant) of a graph `G`
+    can be computed by regarding the graph as a Markov chain.
+    The Kemeny constant is then the expected number of time steps
+    to transition from a starting state i to a random destination state
+    sampled from the Markov chain's stationary distribution.
+    The Kemeny constant is independent of the chosen initial state [1]_.
+
+    The Kemeny constant measures the time needed for spreading
+    across a graph. Low values indicate a closely connected graph
+    whereas high values indicate a spread-out graph.
+
+    If weight is not provided, then a weight of 1 is used for all edges.
+
+    Since `G` represents a Markov chain, the weights must be positive.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight : string or None, optional (default=None)
+       The edge data key used to compute the Kemeny constant.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    float
+        The Kemeny constant of the graph `G`.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph `G` is directed.
+
+    NetworkXError
+        If the graph `G` is not connected, or contains no nodes,
+        or has edges with negative weights.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> round(nx.kemeny_constant(G), 10)
+    3.2
+
+    Notes
+    -----
+    The implementation is based on equation (3.3) in [2]_.
+    Self-loops are allowed and indicate a Markov chain where
+    the state can remain the same. Multi-edges are contracted
+    in one edge with weight equal to the sum of the weights.
+
+    References
+    ----------
+    .. [1] Wikipedia
+       "Kemeny's constant."
+       https://en.wikipedia.org/wiki/Kemeny%27s_constant
+    .. [2] Lovász L.
+        Random walks on graphs: A survey.
+        Paul Erdös is Eighty, vol. 2, Bolyai Society,
+        Mathematical Studies, Keszthely, Hungary (1993), pp. 1-46
+    """
+    import numpy as np
+    import scipy as sp
+
+    if len(G) == 0:
+        raise nx.NetworkXError("Graph G must contain at least one node.")
+    if not nx.is_connected(G):
+        raise nx.NetworkXError("Graph G must be connected.")
+    if nx.is_negatively_weighted(G, weight=weight):
+        raise nx.NetworkXError("The weights of graph G must be nonnegative.")
+
+    # Compute matrix H = D^-1/2 A D^-1/2
+    A = nx.adjacency_matrix(G, weight=weight)
+    n, m = A.shape
+    diags = A.sum(axis=1)
+    with np.errstate(divide="ignore"):
+        diags_sqrt = 1.0 / np.sqrt(diags)
+    diags_sqrt[np.isinf(diags_sqrt)] = 0
+    DH = sp.sparse.csr_array(sp.sparse.spdiags(diags_sqrt, 0, m, n, format="csr"))
+    H = DH @ (A @ DH)
+
+    # Compute eigenvalues of H
+    eig = np.sort(sp.linalg.eigvalsh(H.todense()))
+
+    # Compute the Kemeny constant
+    return float(np.sum(1 / (1 - eig[:-1])))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/distance_regular.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/distance_regular.py
new file mode 100644
index 0000000000000000000000000000000000000000..27b4d0216e427a03f6cc0b90d15f4debb2d52b56
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/distance_regular.py
@@ -0,0 +1,238 @@
+"""
+=======================
+Distance-regular graphs
+=======================
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+from .distance_measures import diameter
+
+__all__ = [
+    "is_distance_regular",
+    "is_strongly_regular",
+    "intersection_array",
+    "global_parameters",
+]
+
+
+@nx._dispatchable
+def is_distance_regular(G):
+    """Returns True if the graph is distance regular, False otherwise.
+
+    A connected graph G is distance-regular if for any nodes x,y
+    and any integers i,j=0,1,...,d (where d is the graph
+    diameter), the number of vertices at distance i from x and
+    distance j from y depends only on i,j and the graph distance
+    between x and y, independently of the choice of x and y.
+
+    Parameters
+    ----------
+    G: Networkx graph (undirected)
+
+    Returns
+    -------
+    bool
+      True if the graph is Distance Regular, False otherwise
+
+    Examples
+    --------
+    >>> G = nx.hypercube_graph(6)
+    >>> nx.is_distance_regular(G)
+    True
+
+    See Also
+    --------
+    intersection_array, global_parameters
+
+    Notes
+    -----
+    For undirected and simple graphs only
+
+    References
+    ----------
+    .. [1] Brouwer, A. E.; Cohen, A. M.; and Neumaier, A.
+        Distance-Regular Graphs. New York: Springer-Verlag, 1989.
+    .. [2] Weisstein, Eric W. "Distance-Regular Graph."
+        http://mathworld.wolfram.com/Distance-RegularGraph.html
+
+    """
+    try:
+        intersection_array(G)
+        return True
+    except nx.NetworkXError:
+        return False
+
+
+def global_parameters(b, c):
+    """Returns global parameters for a given intersection array.
+
+    Given a distance-regular graph G with integers b_i, c_i,i = 0,....,d
+    such that for any 2 vertices x,y in G at a distance i=d(x,y), there
+    are exactly c_i neighbors of y at a distance of i-1 from x and b_i
+    neighbors of y at a distance of i+1 from x.
+
+    Thus, a distance regular graph has the global parameters,
+    [[c_0,a_0,b_0],[c_1,a_1,b_1],......,[c_d,a_d,b_d]] for the
+    intersection array  [b_0,b_1,.....b_{d-1};c_1,c_2,.....c_d]
+    where a_i+b_i+c_i=k , k= degree of every vertex.
+
+    Parameters
+    ----------
+    b : list
+
+    c : list
+
+    Returns
+    -------
+    iterable
+       An iterable over three tuples.
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> b, c = nx.intersection_array(G)
+    >>> list(nx.global_parameters(b, c))
+    [(0, 0, 3), (1, 0, 2), (1, 1, 1), (1, 1, 1), (2, 0, 1), (3, 0, 0)]
+
+    References
+    ----------
+    .. [1] Weisstein, Eric W. "Global Parameters."
+       From MathWorld--A Wolfram Web Resource.
+       http://mathworld.wolfram.com/GlobalParameters.html
+
+    See Also
+    --------
+    intersection_array
+    """
+    return ((y, b[0] - x - y, x) for x, y in zip(b + [0], [0] + c))
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def intersection_array(G):
+    """Returns the intersection array of a distance-regular graph.
+
+    Given a distance-regular graph G with integers b_i, c_i,i = 0,....,d
+    such that for any 2 vertices x,y in G at a distance i=d(x,y), there
+    are exactly c_i neighbors of y at a distance of i-1 from x and b_i
+    neighbors of y at a distance of i+1 from x.
+
+    A distance regular graph's intersection array is given by,
+    [b_0,b_1,.....b_{d-1};c_1,c_2,.....c_d]
+
+    Parameters
+    ----------
+    G: Networkx graph (undirected)
+
+    Returns
+    -------
+    b,c: tuple of lists
+
+    Examples
+    --------
+    >>> G = nx.icosahedral_graph()
+    >>> nx.intersection_array(G)
+    ([5, 2, 1], [1, 2, 5])
+
+    References
+    ----------
+    .. [1] Weisstein, Eric W. "Intersection Array."
+       From MathWorld--A Wolfram Web Resource.
+       http://mathworld.wolfram.com/IntersectionArray.html
+
+    See Also
+    --------
+    global_parameters
+    """
+    # test for regular graph (all degrees must be equal)
+    if len(G) == 0:
+        raise nx.NetworkXPointlessConcept("Graph has no nodes.")
+    degree = iter(G.degree())
+    (_, k) = next(degree)
+    for _, knext in degree:
+        if knext != k:
+            raise nx.NetworkXError("Graph is not distance regular.")
+        k = knext
+    path_length = dict(nx.all_pairs_shortest_path_length(G))
+    diameter = max(max(path_length[n].values()) for n in path_length)
+    bint = {}  # 'b' intersection array
+    cint = {}  # 'c' intersection array
+    for u in G:
+        for v in G:
+            try:
+                i = path_length[u][v]
+            except KeyError as err:  # graph must be connected
+                raise nx.NetworkXError("Graph is not distance regular.") from err
+            # number of neighbors of v at a distance of i-1 from u
+            c = len([n for n in G[v] if path_length[n][u] == i - 1])
+            # number of neighbors of v at a distance of i+1 from u
+            b = len([n for n in G[v] if path_length[n][u] == i + 1])
+            # b,c are independent of u and v
+            if cint.get(i, c) != c or bint.get(i, b) != b:
+                raise nx.NetworkXError("Graph is not distance regular")
+            bint[i] = b
+            cint[i] = c
+    return (
+        [bint.get(j, 0) for j in range(diameter)],
+        [cint.get(j + 1, 0) for j in range(diameter)],
+    )
+
+
+# TODO There is a definition for directed strongly regular graphs.
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def is_strongly_regular(G):
+    """Returns True if and only if the given graph is strongly
+    regular.
+
+    An undirected graph is *strongly regular* if
+
+    * it is regular,
+    * each pair of adjacent vertices has the same number of neighbors in
+      common,
+    * each pair of nonadjacent vertices has the same number of neighbors
+      in common.
+
+    Each strongly regular graph is a distance-regular graph.
+    Conversely, if a distance-regular graph has diameter two, then it is
+    a strongly regular graph. For more information on distance-regular
+    graphs, see :func:`is_distance_regular`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    Returns
+    -------
+    bool
+        Whether `G` is strongly regular.
+
+    Examples
+    --------
+
+    The cycle graph on five vertices is strongly regular. It is
+    two-regular, each pair of adjacent vertices has no shared neighbors,
+    and each pair of nonadjacent vertices has one shared neighbor::
+
+        >>> G = nx.cycle_graph(5)
+        >>> nx.is_strongly_regular(G)
+        True
+
+    """
+    # Here is an alternate implementation based directly on the
+    # definition of strongly regular graphs:
+    #
+    #     return (all_equal(G.degree().values())
+    #             and all_equal(len(common_neighbors(G, u, v))
+    #                           for u, v in G.edges())
+    #             and all_equal(len(common_neighbors(G, u, v))
+    #                           for u, v in non_edges(G)))
+    #
+    # We instead use the fact that a distance-regular graph of diameter
+    # two is strongly regular.
+    return is_distance_regular(G) and diameter(G) == 2
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/dominance.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/dominance.py
new file mode 100644
index 0000000000000000000000000000000000000000..30cb8115c3ad6924a523f015ea2161417d401679
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/dominance.py
@@ -0,0 +1,135 @@
+"""
+Dominance algorithms.
+"""
+
+from functools import reduce
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["immediate_dominators", "dominance_frontiers"]
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def immediate_dominators(G, start):
+    """Returns the immediate dominators of all nodes of a directed graph.
+
+    Parameters
+    ----------
+    G : a DiGraph or MultiDiGraph
+        The graph where dominance is to be computed.
+
+    start : node
+        The start node of dominance computation.
+
+    Returns
+    -------
+    idom : dict keyed by nodes
+        A dict containing the immediate dominators of each node reachable from
+        `start`.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is undirected.
+
+    NetworkXError
+        If `start` is not in `G`.
+
+    Notes
+    -----
+    Except for `start`, the immediate dominators are the parents of their
+    corresponding nodes in the dominator tree.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 2), (1, 3), (2, 5), (3, 4), (4, 5)])
+    >>> sorted(nx.immediate_dominators(G, 1).items())
+    [(1, 1), (2, 1), (3, 1), (4, 3), (5, 1)]
+
+    References
+    ----------
+    .. [1] Cooper, Keith D., Harvey, Timothy J. and Kennedy, Ken.
+           "A simple, fast dominance algorithm." (2006).
+           https://hdl.handle.net/1911/96345
+    """
+    if start not in G:
+        raise nx.NetworkXError("start is not in G")
+
+    idom = {start: start}
+
+    order = list(nx.dfs_postorder_nodes(G, start))
+    dfn = {u: i for i, u in enumerate(order)}
+    order.pop()
+    order.reverse()
+
+    def intersect(u, v):
+        while u != v:
+            while dfn[u] < dfn[v]:
+                u = idom[u]
+            while dfn[u] > dfn[v]:
+                v = idom[v]
+        return u
+
+    changed = True
+    while changed:
+        changed = False
+        for u in order:
+            new_idom = reduce(intersect, (v for v in G.pred[u] if v in idom))
+            if u not in idom or idom[u] != new_idom:
+                idom[u] = new_idom
+                changed = True
+
+    return idom
+
+
+@nx._dispatchable
+def dominance_frontiers(G, start):
+    """Returns the dominance frontiers of all nodes of a directed graph.
+
+    Parameters
+    ----------
+    G : a DiGraph or MultiDiGraph
+        The graph where dominance is to be computed.
+
+    start : node
+        The start node of dominance computation.
+
+    Returns
+    -------
+    df : dict keyed by nodes
+        A dict containing the dominance frontiers of each node reachable from
+        `start` as lists.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is undirected.
+
+    NetworkXError
+        If `start` is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 2), (1, 3), (2, 5), (3, 4), (4, 5)])
+    >>> sorted((u, sorted(df)) for u, df in nx.dominance_frontiers(G, 1).items())
+    [(1, []), (2, [5]), (3, [5]), (4, [5]), (5, [])]
+
+    References
+    ----------
+    .. [1] Cooper, Keith D., Harvey, Timothy J. and Kennedy, Ken.
+           "A simple, fast dominance algorithm." (2006).
+           https://hdl.handle.net/1911/96345
+    """
+    idom = nx.immediate_dominators(G, start)
+
+    df = {u: set() for u in idom}
+    for u in idom:
+        if len(G.pred[u]) >= 2:
+            for v in G.pred[u]:
+                if v in idom:
+                    while v != idom[u]:
+                        df[v].add(u)
+                        v = idom[v]
+    return df
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/dominating.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/dominating.py
new file mode 100644
index 0000000000000000000000000000000000000000..41c102a5bc10805a67e8bf26a8348b19abf7fea2
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/dominating.py
@@ -0,0 +1,268 @@
+"""Functions for computing dominating sets in a graph."""
+
+import math
+from heapq import heappop, heappush
+from itertools import chain, count
+
+import networkx as nx
+
+__all__ = [
+    "dominating_set",
+    "is_dominating_set",
+    "connected_dominating_set",
+    "is_connected_dominating_set",
+]
+
+
+@nx._dispatchable
+def dominating_set(G, start_with=None):
+    r"""Finds a dominating set for the graph G.
+
+    A *dominating set* for a graph with node set *V* is a subset *D* of
+    *V* such that every node not in *D* is adjacent to at least one
+    member of *D* [1]_.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    start_with : node (default=None)
+        Node to use as a starting point for the algorithm.
+
+    Returns
+    -------
+    D : set
+        A dominating set for G.
+
+    Notes
+    -----
+    This function is an implementation of algorithm 7 in [2]_ which
+    finds some dominating set, not necessarily the smallest one.
+
+    See also
+    --------
+    is_dominating_set
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Dominating_set
+
+    .. [2] Abdol-Hossein Esfahanian. Connectivity Algorithms.
+        http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf
+
+    """
+    all_nodes = set(G)
+    if start_with is None:
+        start_with = nx.utils.arbitrary_element(all_nodes)
+    if start_with not in G:
+        raise nx.NetworkXError(f"node {start_with} is not in G")
+    dominating_set = {start_with}
+    dominated_nodes = set(G[start_with])
+    remaining_nodes = all_nodes - dominated_nodes - dominating_set
+    while remaining_nodes:
+        # Choose an arbitrary node and determine its undominated neighbors.
+        v = remaining_nodes.pop()
+        undominated_nbrs = set(G[v]) - dominating_set
+        # Add the node to the dominating set and the neighbors to the
+        # dominated set. Finally, remove all of those nodes from the set
+        # of remaining nodes.
+        dominating_set.add(v)
+        dominated_nodes |= undominated_nbrs
+        remaining_nodes -= undominated_nbrs
+    return dominating_set
+
+
+@nx._dispatchable
+def is_dominating_set(G, nbunch):
+    """Checks if `nbunch` is a dominating set for `G`.
+
+    A *dominating set* for a graph with node set *V* is a subset *D* of
+    *V* such that every node not in *D* is adjacent to at least one
+    member of *D* [1]_.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    nbunch : iterable
+        An iterable of nodes in the graph `G`.
+
+    Returns
+    -------
+    dominating : bool
+        True if `nbunch` is a dominating set of `G`, false otherwise.
+
+    See also
+    --------
+    dominating_set
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Dominating_set
+
+    """
+    testset = {n for n in nbunch if n in G}
+    nbrs = set(chain.from_iterable(G[n] for n in testset))
+    return len(set(G) - testset - nbrs) == 0
+
+
+@nx.utils.not_implemented_for("directed")
+@nx._dispatchable
+def connected_dominating_set(G):
+    """Returns a connected dominating set.
+
+    A *dominating set* for a graph *G* with node set *V* is a subset *D* of *V*
+    such that every node not in *D* is adjacent to at least one member of *D*
+    [1]_. A *connected dominating set* is a dominating set *C* that induces a
+    connected subgraph of *G* [2]_.
+    Note that connected dominating sets are not unique in general and that there
+    may be other connected dominating sets.
+
+    Parameters
+    ----------
+    G : NewtorkX graph
+        Undirected connected graph.
+
+    Returns
+    -------
+    connected_dominating_set : set
+        A dominating set of nodes which induces a connected subgraph of G.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is directed.
+
+    NetworkXError
+        If G is disconnected.
+
+    Examples
+    ________
+    >>> G = nx.Graph(
+    ...     [
+    ...         (1, 2),
+    ...         (1, 3),
+    ...         (1, 4),
+    ...         (1, 5),
+    ...         (1, 6),
+    ...         (2, 7),
+    ...         (3, 8),
+    ...         (4, 9),
+    ...         (5, 10),
+    ...         (6, 11),
+    ...         (7, 12),
+    ...         (8, 12),
+    ...         (9, 12),
+    ...         (10, 12),
+    ...         (11, 12),
+    ...     ]
+    ... )
+    >>> nx.connected_dominating_set(G)
+    {1, 2, 3, 4, 5, 6, 7}
+
+    Notes
+    -----
+    This function implements Algorithm I in its basic version as described
+    in [3]_. The idea behind the algorithm is the following: grow a tree *T*,
+    starting from a node with maximum degree. Throughout the growing process,
+    nonleaf nodes in *T* are our connected dominating set (CDS), leaf nodes in
+    *T* are marked as "seen" and nodes in G that are not yet in *T* are marked as
+    "unseen". We maintain a max-heap of all "seen" nodes, and track the number
+    of "unseen" neighbors for each node. At each step we pop the heap top -- a
+    "seen" (leaf) node with maximal number of "unseen" neighbors, add it to the
+    CDS and mark all its "unseen" neighbors as "seen". For each one of the newly
+    created "seen" nodes, we also decrement the number of "unseen" neighbors for
+    all its neighbors. The algorithm terminates when there are no more "unseen"
+    nodes.
+    Runtime complexity of this implementation is $O(|E|*log|V|)$ (amortized).
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Dominating_set
+    .. [2] https://en.wikipedia.org/wiki/Connected_dominating_set
+    .. [3] Guha, S. and Khuller, S.
+           *Approximation Algorithms for Connected Dominating Sets*,
+           Algorithmica, 20, 374-387, 1998.
+
+    """
+    if len(G) == 0:
+        return set()
+
+    if not nx.is_connected(G):
+        raise nx.NetworkXError("G must be a connected graph")
+
+    if len(G) == 1:
+        return set(G)
+
+    G_succ = G._adj  # For speed-up
+
+    # Use the count c to avoid comparing nodes
+    c = count()
+
+    # Keep track of the number of unseen nodes adjacent to each node
+    unseen_degree = dict(G.degree)
+
+    # Find node with highest degree and update its neighbors
+    (max_deg_node, max_deg) = max(unseen_degree.items(), key=lambda x: x[1])
+    for nbr in G_succ[max_deg_node]:
+        unseen_degree[nbr] -= 1
+
+    # Initially all nodes except max_deg_node are unseen
+    unseen = set(G) - {max_deg_node}
+
+    # We want a max-heap of the unseen-degree using heapq, which is a min-heap
+    # So we store the negative of the unseen-degree
+    seen = [(-max_deg, next(c), max_deg_node)]
+
+    connected_dominating_set = set()
+
+    # Main loop
+    while unseen:
+        (neg_deg, cnt, u) = heappop(seen)
+        # Check if u's unseen-degree changed while in the heap
+        if -neg_deg > unseen_degree[u]:
+            heappush(seen, (-unseen_degree[u], cnt, u))
+            continue
+        # Mark all u's unseen neighbors as seen and add them to the heap
+        for v in G_succ[u]:
+            if v in unseen:
+                unseen.remove(v)
+                for nbr in G_succ[v]:
+                    unseen_degree[nbr] -= 1
+                heappush(seen, (-unseen_degree[v], next(c), v))
+        # Add u to the dominating set
+        connected_dominating_set.add(u)
+
+    return connected_dominating_set
+
+
+@nx.utils.not_implemented_for("directed")
+@nx._dispatchable
+def is_connected_dominating_set(G, nbunch):
+    """Checks if `nbunch` is a connected dominating set for `G`.
+
+    A *dominating set* for a graph *G* with node set *V* is a subset *D* of
+    *V* such that every node not in *D* is adjacent to at least one
+    member of *D* [1]_. A *connected dominating set* is a dominating
+    set *C* that induces a connected subgraph of *G* [2]_.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph.
+
+    nbunch : iterable
+        An iterable of nodes in the graph `G`.
+
+    Returns
+    -------
+    connected_dominating : bool
+        True if `nbunch` is connected dominating set of `G`, false otherwise.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Dominating_set
+    .. [2] https://en.wikipedia.org/wiki/Connected_dominating_set
+
+    """
+    return nx.is_dominating_set(G, nbunch) and nx.is_connected(nx.subgraph(G, nbunch))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/efficiency_measures.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/efficiency_measures.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8e9d7a9e680e7db5d61b87e067c03a6d603c3af
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/efficiency_measures.py
@@ -0,0 +1,167 @@
+"""Provides functions for computing the efficiency of nodes and graphs."""
+
+import networkx as nx
+from networkx.exception import NetworkXNoPath
+
+from ..utils import not_implemented_for
+
+__all__ = ["efficiency", "local_efficiency", "global_efficiency"]
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def efficiency(G, u, v):
+    """Returns the efficiency of a pair of nodes in a graph.
+
+    The *efficiency* of a pair of nodes is the multiplicative inverse of the
+    shortest path distance between the nodes [1]_. Returns 0 if no path
+    between nodes.
+
+    Parameters
+    ----------
+    G : :class:`networkx.Graph`
+        An undirected graph for which to compute the average local efficiency.
+    u, v : node
+        Nodes in the graph ``G``.
+
+    Returns
+    -------
+    float
+        Multiplicative inverse of the shortest path distance between the nodes.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
+    >>> nx.efficiency(G, 2, 3)  # this gives efficiency for node 2 and 3
+    0.5
+
+    Notes
+    -----
+    Edge weights are ignored when computing the shortest path distances.
+
+    See also
+    --------
+    local_efficiency
+    global_efficiency
+
+    References
+    ----------
+    .. [1] Latora, Vito, and Massimo Marchiori.
+           "Efficient behavior of small-world networks."
+           *Physical Review Letters* 87.19 (2001): 198701.
+           <https://doi.org/10.1103/PhysRevLett.87.198701>
+
+    """
+    try:
+        eff = 1 / nx.shortest_path_length(G, u, v)
+    except NetworkXNoPath:
+        eff = 0
+    return eff
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def global_efficiency(G):
+    """Returns the average global efficiency of the graph.
+
+    The *efficiency* of a pair of nodes in a graph is the multiplicative
+    inverse of the shortest path distance between the nodes. The *average
+    global efficiency* of a graph is the average efficiency of all pairs of
+    nodes [1]_.
+
+    Parameters
+    ----------
+    G : :class:`networkx.Graph`
+        An undirected graph for which to compute the average global efficiency.
+
+    Returns
+    -------
+    float
+        The average global efficiency of the graph.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
+    >>> round(nx.global_efficiency(G), 12)
+    0.916666666667
+
+    Notes
+    -----
+    Edge weights are ignored when computing the shortest path distances.
+
+    See also
+    --------
+    local_efficiency
+
+    References
+    ----------
+    .. [1] Latora, Vito, and Massimo Marchiori.
+           "Efficient behavior of small-world networks."
+           *Physical Review Letters* 87.19 (2001): 198701.
+           <https://doi.org/10.1103/PhysRevLett.87.198701>
+
+    """
+    n = len(G)
+    denom = n * (n - 1)
+    if denom != 0:
+        lengths = nx.all_pairs_shortest_path_length(G)
+        g_eff = 0
+        for source, targets in lengths:
+            for target, distance in targets.items():
+                if distance > 0:
+                    g_eff += 1 / distance
+        g_eff /= denom
+        # g_eff = sum(1 / d for s, tgts in lengths
+        #                   for t, d in tgts.items() if d > 0) / denom
+    else:
+        g_eff = 0
+    # TODO This can be made more efficient by computing all pairs shortest
+    # path lengths in parallel.
+    return g_eff
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def local_efficiency(G):
+    """Returns the average local efficiency of the graph.
+
+    The *efficiency* of a pair of nodes in a graph is the multiplicative
+    inverse of the shortest path distance between the nodes. The *local
+    efficiency* of a node in the graph is the average global efficiency of the
+    subgraph induced by the neighbors of the node. The *average local
+    efficiency* is the average of the local efficiencies of each node [1]_.
+
+    Parameters
+    ----------
+    G : :class:`networkx.Graph`
+        An undirected graph for which to compute the average local efficiency.
+
+    Returns
+    -------
+    float
+        The average local efficiency of the graph.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
+    >>> nx.local_efficiency(G)
+    0.9166666666666667
+
+    Notes
+    -----
+    Edge weights are ignored when computing the shortest path distances.
+
+    See also
+    --------
+    global_efficiency
+
+    References
+    ----------
+    .. [1] Latora, Vito, and Massimo Marchiori.
+           "Efficient behavior of small-world networks."
+           *Physical Review Letters* 87.19 (2001): 198701.
+           <https://doi.org/10.1103/PhysRevLett.87.198701>
+
+    """
+    efficiency_list = (global_efficiency(G.subgraph(G[v])) for v in G)
+    return sum(efficiency_list) / len(G)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/euler.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/euler.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c308e380c774a6450d4ce275118ccffd65defaa
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/euler.py
@@ -0,0 +1,470 @@
+"""
+Eulerian circuits and graphs.
+"""
+
+from itertools import combinations
+
+import networkx as nx
+
+from ..utils import arbitrary_element, not_implemented_for
+
+__all__ = [
+    "is_eulerian",
+    "eulerian_circuit",
+    "eulerize",
+    "is_semieulerian",
+    "has_eulerian_path",
+    "eulerian_path",
+]
+
+
+@nx._dispatchable
+def is_eulerian(G):
+    """Returns True if and only if `G` is Eulerian.
+
+    A graph is *Eulerian* if it has an Eulerian circuit. An *Eulerian
+    circuit* is a closed walk that includes each edge of a graph exactly
+    once.
+
+    Graphs with isolated vertices (i.e. vertices with zero degree) are not
+    considered to have Eulerian circuits. Therefore, if the graph is not
+    connected (or not strongly connected, for directed graphs), this function
+    returns False.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       A graph, either directed or undirected.
+
+    Examples
+    --------
+    >>> nx.is_eulerian(nx.DiGraph({0: [3], 1: [2], 2: [3], 3: [0, 1]}))
+    True
+    >>> nx.is_eulerian(nx.complete_graph(5))
+    True
+    >>> nx.is_eulerian(nx.petersen_graph())
+    False
+
+    If you prefer to allow graphs with isolated vertices to have Eulerian circuits,
+    you can first remove such vertices and then call `is_eulerian` as below example shows.
+
+    >>> G = nx.Graph([(0, 1), (1, 2), (0, 2)])
+    >>> G.add_node(3)
+    >>> nx.is_eulerian(G)
+    False
+
+    >>> G.remove_nodes_from(list(nx.isolates(G)))
+    >>> nx.is_eulerian(G)
+    True
+
+
+    """
+    if G.is_directed():
+        # Every node must have equal in degree and out degree and the
+        # graph must be strongly connected
+        return all(
+            G.in_degree(n) == G.out_degree(n) for n in G
+        ) and nx.is_strongly_connected(G)
+    # An undirected Eulerian graph has no vertices of odd degree and
+    # must be connected.
+    return all(d % 2 == 0 for v, d in G.degree()) and nx.is_connected(G)
+
+
+@nx._dispatchable
+def is_semieulerian(G):
+    """Return True iff `G` is semi-Eulerian.
+
+    G is semi-Eulerian if it has an Eulerian path but no Eulerian circuit.
+
+    See Also
+    --------
+    has_eulerian_path
+    is_eulerian
+    """
+    return has_eulerian_path(G) and not is_eulerian(G)
+
+
+def _find_path_start(G):
+    """Return a suitable starting vertex for an Eulerian path.
+
+    If no path exists, return None.
+    """
+    if not has_eulerian_path(G):
+        return None
+
+    if is_eulerian(G):
+        return arbitrary_element(G)
+
+    if G.is_directed():
+        v1, v2 = (v for v in G if G.in_degree(v) != G.out_degree(v))
+        # Determines which is the 'start' node (as opposed to the 'end')
+        if G.out_degree(v1) > G.in_degree(v1):
+            return v1
+        else:
+            return v2
+
+    else:
+        # In an undirected graph randomly choose one of the possibilities
+        start = [v for v in G if G.degree(v) % 2 != 0][0]
+        return start
+
+
+def _simplegraph_eulerian_circuit(G, source):
+    if G.is_directed():
+        degree = G.out_degree
+        edges = G.out_edges
+    else:
+        degree = G.degree
+        edges = G.edges
+    vertex_stack = [source]
+    last_vertex = None
+    while vertex_stack:
+        current_vertex = vertex_stack[-1]
+        if degree(current_vertex) == 0:
+            if last_vertex is not None:
+                yield (last_vertex, current_vertex)
+            last_vertex = current_vertex
+            vertex_stack.pop()
+        else:
+            _, next_vertex = arbitrary_element(edges(current_vertex))
+            vertex_stack.append(next_vertex)
+            G.remove_edge(current_vertex, next_vertex)
+
+
+def _multigraph_eulerian_circuit(G, source):
+    if G.is_directed():
+        degree = G.out_degree
+        edges = G.out_edges
+    else:
+        degree = G.degree
+        edges = G.edges
+    vertex_stack = [(source, None)]
+    last_vertex = None
+    last_key = None
+    while vertex_stack:
+        current_vertex, current_key = vertex_stack[-1]
+        if degree(current_vertex) == 0:
+            if last_vertex is not None:
+                yield (last_vertex, current_vertex, last_key)
+            last_vertex, last_key = current_vertex, current_key
+            vertex_stack.pop()
+        else:
+            triple = arbitrary_element(edges(current_vertex, keys=True))
+            _, next_vertex, next_key = triple
+            vertex_stack.append((next_vertex, next_key))
+            G.remove_edge(current_vertex, next_vertex, next_key)
+
+
+@nx._dispatchable
+def eulerian_circuit(G, source=None, keys=False):
+    """Returns an iterator over the edges of an Eulerian circuit in `G`.
+
+    An *Eulerian circuit* is a closed walk that includes each edge of a
+    graph exactly once.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       A graph, either directed or undirected.
+
+    source : node, optional
+       Starting node for circuit.
+
+    keys : bool
+       If False, edges generated by this function will be of the form
+       ``(u, v)``. Otherwise, edges will be of the form ``(u, v, k)``.
+       This option is ignored unless `G` is a multigraph.
+
+    Returns
+    -------
+    edges : iterator
+       An iterator over edges in the Eulerian circuit.
+
+    Raises
+    ------
+    NetworkXError
+       If the graph is not Eulerian.
+
+    See Also
+    --------
+    is_eulerian
+
+    Notes
+    -----
+    This is a linear time implementation of an algorithm adapted from [1]_.
+
+    For general information about Euler tours, see [2]_.
+
+    References
+    ----------
+    .. [1] J. Edmonds, E. L. Johnson.
+       Matching, Euler tours and the Chinese postman.
+       Mathematical programming, Volume 5, Issue 1 (1973), 111-114.
+    .. [2] https://en.wikipedia.org/wiki/Eulerian_path
+
+    Examples
+    --------
+    To get an Eulerian circuit in an undirected graph::
+
+        >>> G = nx.complete_graph(3)
+        >>> list(nx.eulerian_circuit(G))
+        [(0, 2), (2, 1), (1, 0)]
+        >>> list(nx.eulerian_circuit(G, source=1))
+        [(1, 2), (2, 0), (0, 1)]
+
+    To get the sequence of vertices in an Eulerian circuit::
+
+        >>> [u for u, v in nx.eulerian_circuit(G)]
+        [0, 2, 1]
+
+    """
+    if not is_eulerian(G):
+        raise nx.NetworkXError("G is not Eulerian.")
+    if G.is_directed():
+        G = G.reverse()
+    else:
+        G = G.copy()
+    if source is None:
+        source = arbitrary_element(G)
+    if G.is_multigraph():
+        for u, v, k in _multigraph_eulerian_circuit(G, source):
+            if keys:
+                yield u, v, k
+            else:
+                yield u, v
+    else:
+        yield from _simplegraph_eulerian_circuit(G, source)
+
+
+@nx._dispatchable
+def has_eulerian_path(G, source=None):
+    """Return True iff `G` has an Eulerian path.
+
+    An Eulerian path is a path in a graph which uses each edge of a graph
+    exactly once. If `source` is specified, then this function checks
+    whether an Eulerian path that starts at node `source` exists.
+
+    A directed graph has an Eulerian path iff:
+        - at most one vertex has out_degree - in_degree = 1,
+        - at most one vertex has in_degree - out_degree = 1,
+        - every other vertex has equal in_degree and out_degree,
+        - and all of its vertices belong to a single connected
+          component of the underlying undirected graph.
+
+    If `source` is not None, an Eulerian path starting at `source` exists if no
+    other node has out_degree - in_degree = 1. This is equivalent to either
+    there exists an Eulerian circuit or `source` has out_degree - in_degree = 1
+    and the conditions above hold.
+
+    An undirected graph has an Eulerian path iff:
+        - exactly zero or two vertices have odd degree,
+        - and all of its vertices belong to a single connected component.
+
+    If `source` is not None, an Eulerian path starting at `source` exists if
+    either there exists an Eulerian circuit or `source` has an odd degree and the
+    conditions above hold.
+
+    Graphs with isolated vertices (i.e. vertices with zero degree) are not considered
+    to have an Eulerian path. Therefore, if the graph is not connected (or not strongly
+    connected, for directed graphs), this function returns False.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        The graph to find an euler path in.
+
+    source : node, optional
+        Starting node for path.
+
+    Returns
+    -------
+    Bool : True if G has an Eulerian path.
+
+    Examples
+    --------
+    If you prefer to allow graphs with isolated vertices to have Eulerian path,
+    you can first remove such vertices and then call `has_eulerian_path` as below example shows.
+
+    >>> G = nx.Graph([(0, 1), (1, 2), (0, 2)])
+    >>> G.add_node(3)
+    >>> nx.has_eulerian_path(G)
+    False
+
+    >>> G.remove_nodes_from(list(nx.isolates(G)))
+    >>> nx.has_eulerian_path(G)
+    True
+
+    See Also
+    --------
+    is_eulerian
+    eulerian_path
+    """
+    if nx.is_eulerian(G):
+        return True
+
+    if G.is_directed():
+        ins = G.in_degree
+        outs = G.out_degree
+        # Since we know it is not eulerian, outs - ins must be 1 for source
+        if source is not None and outs[source] - ins[source] != 1:
+            return False
+
+        unbalanced_ins = 0
+        unbalanced_outs = 0
+        for v in G:
+            if ins[v] - outs[v] == 1:
+                unbalanced_ins += 1
+            elif outs[v] - ins[v] == 1:
+                unbalanced_outs += 1
+            elif ins[v] != outs[v]:
+                return False
+
+        return (
+            unbalanced_ins <= 1 and unbalanced_outs <= 1 and nx.is_weakly_connected(G)
+        )
+    else:
+        # We know it is not eulerian, so degree of source must be odd.
+        if source is not None and G.degree[source] % 2 != 1:
+            return False
+
+        # Sum is 2 since we know it is not eulerian (which implies sum is 0)
+        return sum(d % 2 == 1 for v, d in G.degree()) == 2 and nx.is_connected(G)
+
+
+@nx._dispatchable
+def eulerian_path(G, source=None, keys=False):
+    """Return an iterator over the edges of an Eulerian path in `G`.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        The graph in which to look for an eulerian path.
+    source : node or None (default: None)
+        The node at which to start the search. None means search over all
+        starting nodes.
+    keys : Bool (default: False)
+        Indicates whether to yield edge 3-tuples (u, v, edge_key).
+        The default yields edge 2-tuples
+
+    Yields
+    ------
+    Edge tuples along the eulerian path.
+
+    Warning: If `source` provided is not the start node of an Euler path
+    will raise error even if an Euler Path exists.
+    """
+    if not has_eulerian_path(G, source):
+        raise nx.NetworkXError("Graph has no Eulerian paths.")
+    if G.is_directed():
+        G = G.reverse()
+        if source is None or nx.is_eulerian(G) is False:
+            source = _find_path_start(G)
+        if G.is_multigraph():
+            for u, v, k in _multigraph_eulerian_circuit(G, source):
+                if keys:
+                    yield u, v, k
+                else:
+                    yield u, v
+        else:
+            yield from _simplegraph_eulerian_circuit(G, source)
+    else:
+        G = G.copy()
+        if source is None:
+            source = _find_path_start(G)
+        if G.is_multigraph():
+            if keys:
+                yield from reversed(
+                    [(v, u, k) for u, v, k in _multigraph_eulerian_circuit(G, source)]
+                )
+            else:
+                yield from reversed(
+                    [(v, u) for u, v, k in _multigraph_eulerian_circuit(G, source)]
+                )
+        else:
+            yield from reversed(
+                [(v, u) for u, v in _simplegraph_eulerian_circuit(G, source)]
+            )
+
+
+@not_implemented_for("directed")
+@nx._dispatchable(returns_graph=True)
+def eulerize(G):
+    """Transforms a graph into an Eulerian graph.
+
+    If `G` is Eulerian the result is `G` as a MultiGraph, otherwise the result is a smallest
+    (in terms of the number of edges) multigraph whose underlying simple graph is `G`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       An undirected graph
+
+    Returns
+    -------
+    G : NetworkX multigraph
+
+    Raises
+    ------
+    NetworkXError
+       If the graph is not connected.
+
+    See Also
+    --------
+    is_eulerian
+    eulerian_circuit
+
+    References
+    ----------
+    .. [1] J. Edmonds, E. L. Johnson.
+       Matching, Euler tours and the Chinese postman.
+       Mathematical programming, Volume 5, Issue 1 (1973), 111-114.
+    .. [2] https://en.wikipedia.org/wiki/Eulerian_path
+    .. [3] http://web.math.princeton.edu/math_alive/5/Notes1.pdf
+
+    Examples
+    --------
+        >>> G = nx.complete_graph(10)
+        >>> H = nx.eulerize(G)
+        >>> nx.is_eulerian(H)
+        True
+
+    """
+    if G.order() == 0:
+        raise nx.NetworkXPointlessConcept("Cannot Eulerize null graph")
+    if not nx.is_connected(G):
+        raise nx.NetworkXError("G is not connected")
+    odd_degree_nodes = [n for n, d in G.degree() if d % 2 == 1]
+    G = nx.MultiGraph(G)
+    if len(odd_degree_nodes) == 0:
+        return G
+
+    # get all shortest paths between vertices of odd degree
+    odd_deg_pairs_paths = [
+        (m, {n: nx.shortest_path(G, source=m, target=n)})
+        for m, n in combinations(odd_degree_nodes, 2)
+    ]
+
+    # use the number of vertices in a graph + 1 as an upper bound on
+    # the maximum length of a path in G
+    upper_bound_on_max_path_length = len(G) + 1
+
+    # use "len(G) + 1 - len(P)",
+    # where P is a shortest path between vertices n and m,
+    # as edge-weights in a new graph
+    # store the paths in the graph for easy indexing later
+    Gp = nx.Graph()
+    for n, Ps in odd_deg_pairs_paths:
+        for m, P in Ps.items():
+            if n != m:
+                Gp.add_edge(
+                    m, n, weight=upper_bound_on_max_path_length - len(P), path=P
+                )
+
+    # find the minimum weight matching of edges in the weighted graph
+    best_matching = nx.Graph(list(nx.max_weight_matching(Gp)))
+
+    # duplicate each edge along each path in the set of paths in Gp
+    for m, n in best_matching.edges():
+        path = Gp[m][n]["path"]
+        G.add_edges_from(nx.utils.pairwise(path))
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/graph_hashing.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/graph_hashing.py
new file mode 100644
index 0000000000000000000000000000000000000000..92a69cc988f3d1014b4a985207d479b57e257a08
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/graph_hashing.py
@@ -0,0 +1,435 @@
+"""
+Functions for hashing graphs to strings.
+Isomorphic graphs should be assigned identical hashes.
+For now, only Weisfeiler-Lehman hashing is implemented.
+"""
+
+import warnings
+from collections import Counter, defaultdict
+from hashlib import blake2b
+
+import networkx as nx
+
+__all__ = ["weisfeiler_lehman_graph_hash", "weisfeiler_lehman_subgraph_hashes"]
+
+
+def _hash_label(label, digest_size):
+    return blake2b(label.encode("ascii"), digest_size=digest_size).hexdigest()
+
+
+def _init_node_labels(G, edge_attr, node_attr):
+    if node_attr:
+        return {u: str(dd[node_attr]) for u, dd in G.nodes(data=True)}
+    elif edge_attr:
+        return dict.fromkeys(G, "")
+    else:
+        warnings.warn(
+            "The hashes produced for graphs without node or edge attributes"
+            "changed in v3.5 due to a bugfix (see documentation).",
+            UserWarning,
+            stacklevel=2,
+        )
+        if nx.is_directed(G):
+            return {u: str(G.in_degree(u)) + "_" + str(G.out_degree(u)) for u in G}
+        else:
+            return {u: str(deg) for u, deg in G.degree()}
+
+
+def _neighborhood_aggregate_undirected(G, node, node_labels, edge_attr=None):
+    """
+    Compute new labels for given node in an undirected graph by aggregating
+    the labels of each node's neighbors.
+    """
+    label_list = []
+    for nbr in G.neighbors(node):
+        prefix = "" if edge_attr is None else str(G[node][nbr][edge_attr])
+        label_list.append(prefix + node_labels[nbr])
+    return node_labels[node] + "".join(sorted(label_list))
+
+
+def _neighborhood_aggregate_directed(G, node, node_labels, edge_attr=None):
+    """
+    Compute new labels for given node in a directed graph by aggregating
+    the labels of each node's neighbors.
+    """
+    successor_labels = []
+    for nbr in G.successors(node):
+        prefix = "s_" + "" if edge_attr is None else str(G[node][nbr][edge_attr])
+        successor_labels.append(prefix + node_labels[nbr])
+
+    predecessor_labels = []
+    for nbr in G.predecessors(node):
+        prefix = "p_" + "" if edge_attr is None else str(G[nbr][node][edge_attr])
+        predecessor_labels.append(prefix + node_labels[nbr])
+    return (
+        node_labels[node]
+        + "".join(sorted(successor_labels))
+        + "".join(sorted(predecessor_labels))
+    )
+
+
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable(edge_attrs={"edge_attr": None}, node_attrs="node_attr")
+def weisfeiler_lehman_graph_hash(
+    G, edge_attr=None, node_attr=None, iterations=3, digest_size=16
+):
+    """Return Weisfeiler Lehman (WL) graph hash.
+
+    .. Warning:: Hash values for directed graphs and graphs without edge or
+        node attributes have changed in v3.5. In previous versions,
+        directed graphs did not distinguish in- and outgoing edges. Also,
+        graphs without attributes set initial states such that effectively
+        one extra iteration of WL occurred than indicated by `iterations`.
+        For undirected graphs without node or edge labels, the old
+        hashes can be obtained by increasing the iteration count by one.
+        For more details, see `issue #7806
+        <https://github.com/networkx/networkx/issues/7806>`_.
+
+    The function iteratively aggregates and hashes neighborhoods of each node.
+    After each node's neighbors are hashed to obtain updated node labels,
+    a hashed histogram of resulting labels is returned as the final hash.
+
+    Hashes are identical for isomorphic graphs and strong guarantees that
+    non-isomorphic graphs will get different hashes. See [1]_ for details.
+
+    If no node or edge attributes are provided, the degree of each node
+    is used as its initial label.
+    Otherwise, node and/or edge labels are used to compute the hash.
+
+    Parameters
+    ----------
+    G : graph
+        The graph to be hashed.
+        Can have node and/or edge attributes. Can also have no attributes.
+    edge_attr : string, optional (default=None)
+        The key in edge attribute dictionary to be used for hashing.
+        If None, edge labels are ignored.
+    node_attr: string, optional (default=None)
+        The key in node attribute dictionary to be used for hashing.
+        If None, and no edge_attr given, use the degrees of the nodes as labels.
+    iterations: int, optional (default=3)
+        Number of neighbor aggregations to perform.
+        Should be larger for larger graphs.
+    digest_size: int, optional (default=16)
+        Size (in bytes) of blake2b hash digest to use for hashing node labels.
+
+    Returns
+    -------
+    h : string
+        Hexadecimal string corresponding to hash of `G` (length ``2 * digest_size``).
+
+    Raises
+    ------
+    ValueError
+        If `iterations` is not a positve number.
+
+    Examples
+    --------
+    Two graphs with edge attributes that are isomorphic, except for
+    differences in the edge labels.
+
+    >>> G1 = nx.Graph()
+    >>> G1.add_edges_from(
+    ...     [
+    ...         (1, 2, {"label": "A"}),
+    ...         (2, 3, {"label": "A"}),
+    ...         (3, 1, {"label": "A"}),
+    ...         (1, 4, {"label": "B"}),
+    ...     ]
+    ... )
+    >>> G2 = nx.Graph()
+    >>> G2.add_edges_from(
+    ...     [
+    ...         (5, 6, {"label": "B"}),
+    ...         (6, 7, {"label": "A"}),
+    ...         (7, 5, {"label": "A"}),
+    ...         (7, 8, {"label": "A"}),
+    ...     ]
+    ... )
+
+    Omitting the `edge_attr` option, results in identical hashes.
+
+    >>> nx.weisfeiler_lehman_graph_hash(G1)
+    'c045439172215f49e0bef8c3d26c6b61'
+    >>> nx.weisfeiler_lehman_graph_hash(G2)
+    'c045439172215f49e0bef8c3d26c6b61'
+
+    With edge labels, the graphs are no longer assigned
+    the same hash digest.
+
+    >>> nx.weisfeiler_lehman_graph_hash(G1, edge_attr="label")
+    'c653d85538bcf041d88c011f4f905f10'
+    >>> nx.weisfeiler_lehman_graph_hash(G2, edge_attr="label")
+    '3dcd84af1ca855d0eff3c978d88e7ec7'
+
+    Notes
+    -----
+    To return the WL hashes of each subgraph of a graph, use
+    `weisfeiler_lehman_subgraph_hashes`
+
+    Similarity between hashes does not imply similarity between graphs.
+
+    References
+    ----------
+    .. [1] Shervashidze, Nino, Pascal Schweitzer, Erik Jan Van Leeuwen,
+       Kurt Mehlhorn, and Karsten M. Borgwardt. Weisfeiler Lehman
+       Graph Kernels. Journal of Machine Learning Research. 2011.
+       http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf
+
+    See also
+    --------
+    weisfeiler_lehman_subgraph_hashes
+    """
+
+    if G.is_directed():
+        _neighborhood_aggregate = _neighborhood_aggregate_directed
+        warnings.warn(
+            "The hashes produced for directed graphs changed in version v3.5"
+            " due to a bugfix to track in and out edges separately (see documentation).",
+            UserWarning,
+            stacklevel=2,
+        )
+    else:
+        _neighborhood_aggregate = _neighborhood_aggregate_undirected
+
+    def weisfeiler_lehman_step(G, labels, edge_attr=None):
+        """
+        Apply neighborhood aggregation to each node
+        in the graph.
+        Computes a dictionary with labels for each node.
+        """
+        new_labels = {}
+        for node in G.nodes():
+            label = _neighborhood_aggregate(G, node, labels, edge_attr=edge_attr)
+            new_labels[node] = _hash_label(label, digest_size)
+        return new_labels
+
+    if iterations <= 0:
+        raise ValueError("The WL algorithm requires that `iterations` be positive")
+
+    # set initial node labels
+    node_labels = _init_node_labels(G, edge_attr, node_attr)
+
+    # If the graph has no attributes, initial labels are the nodes' degrees.
+    # This is equivalent to doing the first iterations of WL.
+    if not edge_attr and not node_attr:
+        iterations -= 1
+
+    subgraph_hash_counts = []
+    for _ in range(iterations):
+        node_labels = weisfeiler_lehman_step(G, node_labels, edge_attr=edge_attr)
+        counter = Counter(node_labels.values())
+        # sort the counter, extend total counts
+        subgraph_hash_counts.extend(sorted(counter.items(), key=lambda x: x[0]))
+
+    # hash the final counter
+    return _hash_label(str(tuple(subgraph_hash_counts)), digest_size)
+
+
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable(edge_attrs={"edge_attr": None}, node_attrs="node_attr")
+def weisfeiler_lehman_subgraph_hashes(
+    G,
+    edge_attr=None,
+    node_attr=None,
+    iterations=3,
+    digest_size=16,
+    include_initial_labels=False,
+):
+    """
+    Return a dictionary of subgraph hashes by node.
+
+    .. Warning:: Hash values for directed graphs have changed in version
+        v3.5. In previous versions, directed graphs did not distinguish in-
+        and outgoing edges.
+        Graphs without attributes previously performed an extra iteration of
+        WL at initialisation, which was not visible in the output of this
+        function. This hash value is now included in the returned dictionary,
+        shifting the other calculated hashes one position to the right. To
+        obtain the same last subgraph hash, increase the number of iterations
+        by one.
+        For more details, see `issue #7806
+        <https://github.com/networkx/networkx/issues/7806>`_.
+
+    Dictionary keys are nodes in `G`, and values are a list of hashes.
+    Each hash corresponds to a subgraph rooted at a given node u in `G`.
+    Lists of subgraph hashes are sorted in increasing order of depth from
+    their root node, with the hash at index i corresponding to a subgraph
+    of nodes at most i-hops (i edges) distance from u. Thus, each list will contain
+    `iterations` elements - a hash for a subgraph at each depth. If
+    `include_initial_labels` is set to `True`, each list will additionally
+    have contain a hash of the initial node label (or equivalently a
+    subgraph of depth 0) prepended, totalling ``iterations + 1`` elements.
+
+    The function iteratively aggregates and hashes neighborhoods of each node.
+    This is achieved for each step by replacing for each node its label from
+    the previous iteration with its hashed 1-hop neighborhood aggregate.
+    The new node label is then appended to a list of node labels for each
+    node.
+
+    To aggregate neighborhoods for a node $u$ at each step, all labels of
+    nodes adjacent to $u$ are concatenated. If the `edge_attr` parameter is set,
+    labels for each neighboring node are prefixed with the value of this attribute
+    along the connecting edge from this neighbor to node $u$. The resulting string
+    is then hashed to compress this information into a fixed digest size.
+
+    Thus, at the i-th iteration, nodes within i hops influence any given
+    hashed node label. We can therefore say that at depth $i$ for node $u$
+    we have a hash for a subgraph induced by the i-hop neighborhood of $u$.
+
+    The output can be used to create general Weisfeiler-Lehman graph kernels,
+    or generate features for graphs or nodes - for example to generate 'words' in
+    a graph as seen in the 'graph2vec' algorithm.
+    See [1]_ & [2]_ respectively for details.
+
+    Hashes are identical for isomorphic subgraphs and there exist strong
+    guarantees that non-isomorphic graphs will get different hashes.
+    See [1]_ for details.
+
+    If no node or edge attributes are provided, the degree of each node
+    is used as its initial label.
+    Otherwise, node and/or edge labels are used to compute the hash.
+
+    Parameters
+    ----------
+    G : graph
+        The graph to be hashed.
+        Can have node and/or edge attributes. Can also have no attributes.
+    edge_attr : string, optional (default=None)
+        The key in edge attribute dictionary to be used for hashing.
+        If None, edge labels are ignored.
+    node_attr : string, optional (default=None)
+        The key in node attribute dictionary to be used for hashing.
+        If None, and no edge_attr given, use the degrees of the nodes as labels.
+        If None, and edge_attr is given, each node starts with an identical label.
+    iterations : int, optional (default=3)
+        Number of neighbor aggregations to perform.
+        Should be larger for larger graphs.
+    digest_size : int, optional (default=16)
+        Size (in bytes) of blake2b hash digest to use for hashing node labels.
+        The default size is 16 bytes.
+    include_initial_labels : bool, optional (default=False)
+        If True, include the hashed initial node label as the first subgraph
+        hash for each node.
+
+    Returns
+    -------
+    node_subgraph_hashes : dict
+        A dictionary with each key given by a node in G, and each value given
+        by the subgraph hashes in order of depth from the key node.
+        Hashes are hexadecimal strings (hence ``2 * digest_size`` long).
+
+
+    Raises
+    ------
+    ValueError
+        If `iterations` is not a positve number.
+
+    Examples
+    --------
+    Finding similar nodes in different graphs:
+
+    >>> G1 = nx.Graph()
+    >>> G1.add_edges_from([(1, 2), (2, 3), (2, 4), (3, 5), (4, 6), (5, 7), (6, 7)])
+    >>> G2 = nx.Graph()
+    >>> G2.add_edges_from([(1, 3), (2, 3), (1, 6), (1, 5), (4, 6)])
+    >>> g1_hashes = nx.weisfeiler_lehman_subgraph_hashes(
+    ...     G1, iterations=4, digest_size=8
+    ... )
+    >>> g2_hashes = nx.weisfeiler_lehman_subgraph_hashes(
+    ...     G2, iterations=4, digest_size=8
+    ... )
+
+    Even though G1 and G2 are not isomorphic (they have different numbers of edges),
+    the hash sequence of depth 3 for node 1 in G1 and node 5 in G2 are similar:
+
+    >>> g1_hashes[1]
+    ['f6fc42039fba3776', 'a93b64973cfc8897', 'db1b43ae35a1878f', '57872a7d2059c1c0']
+    >>> g2_hashes[5]
+    ['f6fc42039fba3776', 'a93b64973cfc8897', 'db1b43ae35a1878f', '1716d2a4012fa4bc']
+
+    The first 3 WL subgraph hashes match. From this we can conclude that it's very
+    likely the neighborhood of 3 hops around these nodes are isomorphic.
+
+    However the 4-hop neighborhoods of ``G1`` and ``G2`` are not isomorphic since the
+    4th hashes in the lists above are not equal.
+
+    These nodes may be candidates to be classified together since their local topology
+    is similar.
+
+    Notes
+    -----
+    To hash the full graph when subgraph hashes are not needed, use
+    `weisfeiler_lehman_graph_hash` for efficiency.
+
+    Similarity between hashes does not imply similarity between graphs.
+
+    References
+    ----------
+    .. [1] Shervashidze, Nino, Pascal Schweitzer, Erik Jan Van Leeuwen,
+       Kurt Mehlhorn, and Karsten M. Borgwardt. Weisfeiler Lehman
+       Graph Kernels. Journal of Machine Learning Research. 2011.
+       http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf
+    .. [2] Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan,
+       Lihui Chen, Yang Liu and Shantanu Jaiswa. graph2vec: Learning
+       Distributed Representations of Graphs. arXiv. 2017
+       https://arxiv.org/pdf/1707.05005.pdf
+
+    See also
+    --------
+    weisfeiler_lehman_graph_hash
+    """
+
+    if G.is_directed():
+        _neighborhood_aggregate = _neighborhood_aggregate_directed
+        warnings.warn(
+            "The hashes produced for directed graphs changed in v3.5"
+            " due to a bugfix (see documentation).",
+            UserWarning,
+            stacklevel=2,
+        )
+    else:
+        _neighborhood_aggregate = _neighborhood_aggregate_undirected
+
+    def weisfeiler_lehman_step(G, labels, node_subgraph_hashes, edge_attr=None):
+        """
+        Apply neighborhood aggregation to each node
+        in the graph.
+        Computes a dictionary with labels for each node.
+        Appends the new hashed label to the dictionary of subgraph hashes
+        originating from and indexed by each node in G
+        """
+        new_labels = {}
+        for node in G.nodes():
+            label = _neighborhood_aggregate(G, node, labels, edge_attr=edge_attr)
+            hashed_label = _hash_label(label, digest_size)
+            new_labels[node] = hashed_label
+            node_subgraph_hashes[node].append(hashed_label)
+        return new_labels
+
+    if iterations <= 0:
+        raise ValueError("The WL algorithm requires that `iterations` be positive")
+
+    node_labels = _init_node_labels(G, edge_attr, node_attr)
+
+    if include_initial_labels:
+        node_subgraph_hashes = {
+            k: [_hash_label(v, digest_size)] for k, v in node_labels.items()
+        }
+    else:
+        node_subgraph_hashes = defaultdict(list)
+
+    # If the graph has no attributes, initial labels are the nodes' degrees.
+    # This is equivalent to doing the first iterations of WL.
+    if not edge_attr and not node_attr:
+        iterations -= 1
+        for node in G.nodes():
+            hashed_label = _hash_label(node_labels[node], digest_size)
+            node_subgraph_hashes[node].append(hashed_label)
+
+    for _ in range(iterations):
+        node_labels = weisfeiler_lehman_step(
+            G, node_labels, node_subgraph_hashes, edge_attr
+        )
+
+    return dict(node_subgraph_hashes)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/graphical.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/graphical.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5d82dedda6f9810e3f51bc4c82a9a2b252fa998
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/graphical.py
@@ -0,0 +1,483 @@
+"""Test sequences for graphiness."""
+
+import heapq
+
+import networkx as nx
+
+__all__ = [
+    "is_graphical",
+    "is_multigraphical",
+    "is_pseudographical",
+    "is_digraphical",
+    "is_valid_degree_sequence_erdos_gallai",
+    "is_valid_degree_sequence_havel_hakimi",
+]
+
+
+@nx._dispatchable(graphs=None)
+def is_graphical(sequence, method="eg"):
+    """Returns True if sequence is a valid degree sequence.
+
+    A degree sequence is valid if some graph can realize it.
+
+    Parameters
+    ----------
+    sequence : list or iterable container
+        A sequence of integer node degrees
+
+    method : "eg" | "hh"  (default: 'eg')
+        The method used to validate the degree sequence.
+        "eg" corresponds to the Erdős-Gallai algorithm
+        [EG1960]_, [choudum1986]_, and
+        "hh" to the Havel-Hakimi algorithm
+        [havel1955]_, [hakimi1962]_, [CL1996]_.
+
+    Returns
+    -------
+    valid : bool
+        True if the sequence is a valid degree sequence and False if not.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> sequence = (d for n, d in G.degree())
+    >>> nx.is_graphical(sequence)
+    True
+
+    To test a non-graphical sequence:
+    >>> sequence_list = [d for n, d in G.degree()]
+    >>> sequence_list[-1] += 1
+    >>> nx.is_graphical(sequence_list)
+    False
+
+    References
+    ----------
+    .. [EG1960] Erdős and Gallai, Mat. Lapok 11 264, 1960.
+    .. [choudum1986] S.A. Choudum. "A simple proof of the Erdős-Gallai theorem on
+       graph sequences." Bulletin of the Australian Mathematical Society, 33,
+       pp 67-70, 1986. https://doi.org/10.1017/S0004972700002872
+    .. [havel1955] Havel, V. "A Remark on the Existence of Finite Graphs"
+       Casopis Pest. Mat. 80, 477-480, 1955.
+    .. [hakimi1962] Hakimi, S. "On the Realizability of a Set of Integers as
+       Degrees of the Vertices of a Graph." SIAM J. Appl. Math. 10, 496-506, 1962.
+    .. [CL1996] G. Chartrand and L. Lesniak, "Graphs and Digraphs",
+       Chapman and Hall/CRC, 1996.
+    """
+    if method == "eg":
+        valid = is_valid_degree_sequence_erdos_gallai(list(sequence))
+    elif method == "hh":
+        valid = is_valid_degree_sequence_havel_hakimi(list(sequence))
+    else:
+        msg = "`method` must be 'eg' or 'hh'"
+        raise nx.NetworkXException(msg)
+    return valid
+
+
+def _basic_graphical_tests(deg_sequence):
+    # Sort and perform some simple tests on the sequence
+    deg_sequence = nx.utils.make_list_of_ints(deg_sequence)
+    p = len(deg_sequence)
+    num_degs = [0] * p
+    dmax, dmin, dsum, n = 0, p, 0, 0
+    for d in deg_sequence:
+        # Reject if degree is negative or larger than the sequence length
+        if d < 0 or d >= p:
+            raise nx.NetworkXUnfeasible
+        # Process only the non-zero integers
+        elif d > 0:
+            dmax, dmin, dsum, n = max(dmax, d), min(dmin, d), dsum + d, n + 1
+            num_degs[d] += 1
+    # Reject sequence if it has odd sum or is oversaturated
+    if dsum % 2 or dsum > n * (n - 1):
+        raise nx.NetworkXUnfeasible
+    return dmax, dmin, dsum, n, num_degs
+
+
+@nx._dispatchable(graphs=None)
+def is_valid_degree_sequence_havel_hakimi(deg_sequence):
+    r"""Returns True if deg_sequence can be realized by a simple graph.
+
+    The validation proceeds using the Havel-Hakimi theorem
+    [havel1955]_, [hakimi1962]_, [CL1996]_.
+    Worst-case run time is $O(s)$ where $s$ is the sum of the sequence.
+
+    Parameters
+    ----------
+    deg_sequence : list
+        A list of integers where each element specifies the degree of a node
+        in a graph.
+
+    Returns
+    -------
+    valid : bool
+        True if deg_sequence is graphical and False if not.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (3, 4), (4, 2), (5, 1), (5, 4)])
+    >>> sequence = (d for _, d in G.degree())
+    >>> nx.is_valid_degree_sequence_havel_hakimi(sequence)
+    True
+
+    To test a non-valid sequence:
+    >>> sequence_list = [d for _, d in G.degree()]
+    >>> sequence_list[-1] += 1
+    >>> nx.is_valid_degree_sequence_havel_hakimi(sequence_list)
+    False
+
+    Notes
+    -----
+    The ZZ condition says that for the sequence d if
+
+    .. math::
+        |d| >= \frac{(\max(d) + \min(d) + 1)^2}{4*\min(d)}
+
+    then d is graphical.  This was shown in Theorem 6 in [1]_.
+
+    References
+    ----------
+    .. [1] I.E. Zverovich and V.E. Zverovich. "Contributions to the theory
+       of graphic sequences", Discrete Mathematics, 105, pp. 292-303 (1992).
+    .. [havel1955] Havel, V. "A Remark on the Existence of Finite Graphs"
+       Casopis Pest. Mat. 80, 477-480, 1955.
+    .. [hakimi1962] Hakimi, S. "On the Realizability of a Set of Integers as
+       Degrees of the Vertices of a Graph." SIAM J. Appl. Math. 10, 496-506, 1962.
+    .. [CL1996] G. Chartrand and L. Lesniak, "Graphs and Digraphs",
+       Chapman and Hall/CRC, 1996.
+    """
+    try:
+        dmax, dmin, dsum, n, num_degs = _basic_graphical_tests(deg_sequence)
+    except nx.NetworkXUnfeasible:
+        return False
+    # Accept if sequence has no non-zero degrees or passes the ZZ condition
+    if n == 0 or 4 * dmin * n >= (dmax + dmin + 1) * (dmax + dmin + 1):
+        return True
+
+    modstubs = [0] * (dmax + 1)
+    # Successively reduce degree sequence by removing the maximum degree
+    while n > 0:
+        # Retrieve the maximum degree in the sequence
+        while num_degs[dmax] == 0:
+            dmax -= 1
+        # If there are not enough stubs to connect to, then the sequence is
+        # not graphical
+        if dmax > n - 1:
+            return False
+
+        # Remove largest stub in list
+        num_degs[dmax], n = num_degs[dmax] - 1, n - 1
+        # Reduce the next dmax largest stubs
+        mslen = 0
+        k = dmax
+        for i in range(dmax):
+            while num_degs[k] == 0:
+                k -= 1
+            num_degs[k], n = num_degs[k] - 1, n - 1
+            if k > 1:
+                modstubs[mslen] = k - 1
+                mslen += 1
+        # Add back to the list any non-zero stubs that were removed
+        for i in range(mslen):
+            stub = modstubs[i]
+            num_degs[stub], n = num_degs[stub] + 1, n + 1
+    return True
+
+
+@nx._dispatchable(graphs=None)
+def is_valid_degree_sequence_erdos_gallai(deg_sequence):
+    r"""Returns True if deg_sequence can be realized by a simple graph.
+
+    The validation is done using the Erdős-Gallai theorem [EG1960]_.
+
+    Parameters
+    ----------
+    deg_sequence : list
+        A list of integers
+
+    Returns
+    -------
+    valid : bool
+        True if deg_sequence is graphical and False if not.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (3, 4), (4, 2), (5, 1), (5, 4)])
+    >>> sequence = (d for _, d in G.degree())
+    >>> nx.is_valid_degree_sequence_erdos_gallai(sequence)
+    True
+
+    To test a non-valid sequence:
+    >>> sequence_list = [d for _, d in G.degree()]
+    >>> sequence_list[-1] += 1
+    >>> nx.is_valid_degree_sequence_erdos_gallai(sequence_list)
+    False
+
+    Notes
+    -----
+
+    This implementation uses an equivalent form of the Erdős-Gallai criterion.
+    Worst-case run time is $O(n)$ where $n$ is the length of the sequence.
+
+    Specifically, a sequence d is graphical if and only if the
+    sum of the sequence is even and for all strong indices k in the sequence,
+
+     .. math::
+
+       \sum_{i=1}^{k} d_i \leq k(k-1) + \sum_{j=k+1}^{n} \min(d_i,k)
+             = k(n-1) - ( k \sum_{j=0}^{k-1} n_j - \sum_{j=0}^{k-1} j n_j )
+
+    A strong index k is any index where d_k >= k and the value n_j is the
+    number of occurrences of j in d.  The maximal strong index is called the
+    Durfee index.
+
+    This particular rearrangement comes from the proof of Theorem 3 in [2]_.
+
+    The ZZ condition says that for the sequence d if
+
+    .. math::
+        |d| >= \frac{(\max(d) + \min(d) + 1)^2}{4*\min(d)}
+
+    then d is graphical.  This was shown in Theorem 6 in [2]_.
+
+    References
+    ----------
+    .. [1] A. Tripathi and S. Vijay. "A note on a theorem of Erdős & Gallai",
+       Discrete Mathematics, 265, pp. 417-420 (2003).
+    .. [2] I.E. Zverovich and V.E. Zverovich. "Contributions to the theory
+       of graphic sequences", Discrete Mathematics, 105, pp. 292-303 (1992).
+    .. [EG1960] Erdős and Gallai, Mat. Lapok 11 264, 1960.
+    """
+    try:
+        dmax, dmin, dsum, n, num_degs = _basic_graphical_tests(deg_sequence)
+    except nx.NetworkXUnfeasible:
+        return False
+    # Accept if sequence has no non-zero degrees or passes the ZZ condition
+    if n == 0 or 4 * dmin * n >= (dmax + dmin + 1) * (dmax + dmin + 1):
+        return True
+
+    # Perform the EG checks using the reformulation of Zverovich and Zverovich
+    k, sum_deg, sum_nj, sum_jnj = 0, 0, 0, 0
+    for dk in range(dmax, dmin - 1, -1):
+        if dk < k + 1:  # Check if already past Durfee index
+            return True
+        if num_degs[dk] > 0:
+            run_size = num_degs[dk]  # Process a run of identical-valued degrees
+            if dk < k + run_size:  # Check if end of run is past Durfee index
+                run_size = dk - k  # Adjust back to Durfee index
+            sum_deg += run_size * dk
+            for v in range(run_size):
+                sum_nj += num_degs[k + v]
+                sum_jnj += (k + v) * num_degs[k + v]
+            k += run_size
+            if sum_deg > k * (n - 1) - k * sum_nj + sum_jnj:
+                return False
+    return True
+
+
+@nx._dispatchable(graphs=None)
+def is_multigraphical(sequence):
+    """Returns True if some multigraph can realize the sequence.
+
+    Parameters
+    ----------
+    sequence : list
+        A list of integers
+
+    Returns
+    -------
+    valid : bool
+        True if deg_sequence is a multigraphic degree sequence and False if not.
+
+    Examples
+    --------
+    >>> G = nx.MultiGraph([(1, 2), (1, 3), (2, 3), (3, 4), (4, 2), (5, 1), (5, 4)])
+    >>> sequence = (d for _, d in G.degree())
+    >>> nx.is_multigraphical(sequence)
+    True
+
+    To test a non-multigraphical sequence:
+    >>> sequence_list = [d for _, d in G.degree()]
+    >>> sequence_list[-1] += 1
+    >>> nx.is_multigraphical(sequence_list)
+    False
+
+    Notes
+    -----
+    The worst-case run time is $O(n)$ where $n$ is the length of the sequence.
+
+    References
+    ----------
+    .. [1] S. L. Hakimi. "On the realizability of a set of integers as
+       degrees of the vertices of a linear graph", J. SIAM, 10, pp. 496-506
+       (1962).
+    """
+    try:
+        deg_sequence = nx.utils.make_list_of_ints(sequence)
+    except nx.NetworkXError:
+        return False
+    dsum, dmax = 0, 0
+    for d in deg_sequence:
+        if d < 0:
+            return False
+        dsum, dmax = dsum + d, max(dmax, d)
+    if dsum % 2 or dsum < 2 * dmax:
+        return False
+    return True
+
+
+@nx._dispatchable(graphs=None)
+def is_pseudographical(sequence):
+    """Returns True if some pseudograph can realize the sequence.
+
+    Every nonnegative integer sequence with an even sum is pseudographical
+    (see [1]_).
+
+    Parameters
+    ----------
+    sequence : list or iterable container
+        A sequence of integer node degrees
+
+    Returns
+    -------
+    valid : bool
+      True if the sequence is a pseudographic degree sequence and False if not.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (3, 4), (4, 2), (5, 1), (5, 4)])
+    >>> sequence = (d for _, d in G.degree())
+    >>> nx.is_pseudographical(sequence)
+    True
+
+    To test a non-pseudographical sequence:
+    >>> sequence_list = [d for _, d in G.degree()]
+    >>> sequence_list[-1] += 1
+    >>> nx.is_pseudographical(sequence_list)
+    False
+
+    Notes
+    -----
+    The worst-case run time is $O(n)$ where n is the length of the sequence.
+
+    References
+    ----------
+    .. [1] F. Boesch and F. Harary. "Line removal algorithms for graphs
+       and their degree lists", IEEE Trans. Circuits and Systems, CAS-23(12),
+       pp. 778-782 (1976).
+    """
+    try:
+        deg_sequence = nx.utils.make_list_of_ints(sequence)
+    except nx.NetworkXError:
+        return False
+    return sum(deg_sequence) % 2 == 0 and min(deg_sequence) >= 0
+
+
+@nx._dispatchable(graphs=None)
+def is_digraphical(in_sequence, out_sequence):
+    r"""Returns True if some directed graph can realize the in- and out-degree
+    sequences.
+
+    Parameters
+    ----------
+    in_sequence : list or iterable container
+        A sequence of integer node in-degrees
+
+    out_sequence : list or iterable container
+        A sequence of integer node out-degrees
+
+    Returns
+    -------
+    valid : bool
+      True if in and out-sequences are digraphic False if not.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 2), (1, 3), (2, 3), (3, 4), (4, 2), (5, 1), (5, 4)])
+    >>> in_seq = (d for n, d in G.in_degree())
+    >>> out_seq = (d for n, d in G.out_degree())
+    >>> nx.is_digraphical(in_seq, out_seq)
+    True
+
+    To test a non-digraphical scenario:
+    >>> in_seq_list = [d for n, d in G.in_degree()]
+    >>> in_seq_list[-1] += 1
+    >>> nx.is_digraphical(in_seq_list, out_seq)
+    False
+
+    Notes
+    -----
+    This algorithm is from Kleitman and Wang [1]_.
+    The worst case runtime is $O(s \times \log n)$ where $s$ and $n$ are the
+    sum and length of the sequences respectively.
+
+    References
+    ----------
+    .. [1] D.J. Kleitman and D.L. Wang
+       Algorithms for Constructing Graphs and Digraphs with Given Valences
+       and Factors, Discrete Mathematics, 6(1), pp. 79-88 (1973)
+    """
+    try:
+        in_deg_sequence = nx.utils.make_list_of_ints(in_sequence)
+        out_deg_sequence = nx.utils.make_list_of_ints(out_sequence)
+    except nx.NetworkXError:
+        return False
+    # Process the sequences and form two heaps to store degree pairs with
+    # either zero or non-zero out degrees
+    sumin, sumout, nin, nout = 0, 0, len(in_deg_sequence), len(out_deg_sequence)
+    maxn = max(nin, nout)
+    maxin = 0
+    if maxn == 0:
+        return True
+    stubheap, zeroheap = [], []
+    for n in range(maxn):
+        in_deg, out_deg = 0, 0
+        if n < nout:
+            out_deg = out_deg_sequence[n]
+        if n < nin:
+            in_deg = in_deg_sequence[n]
+        if in_deg < 0 or out_deg < 0:
+            return False
+        sumin, sumout, maxin = sumin + in_deg, sumout + out_deg, max(maxin, in_deg)
+        if in_deg > 0:
+            stubheap.append((-1 * out_deg, -1 * in_deg))
+        elif out_deg > 0:
+            zeroheap.append(-1 * out_deg)
+    if sumin != sumout:
+        return False
+    heapq.heapify(stubheap)
+    heapq.heapify(zeroheap)
+
+    modstubs = [(0, 0)] * (maxin + 1)
+    # Successively reduce degree sequence by removing the maximum out degree
+    while stubheap:
+        # Take the first value in the sequence with non-zero in degree
+        (freeout, freein) = heapq.heappop(stubheap)
+        freein *= -1
+        if freein > len(stubheap) + len(zeroheap):
+            return False
+
+        # Attach out stubs to the nodes with the most in stubs
+        mslen = 0
+        for i in range(freein):
+            if zeroheap and (not stubheap or stubheap[0][0] > zeroheap[0]):
+                stubout = heapq.heappop(zeroheap)
+                stubin = 0
+            else:
+                (stubout, stubin) = heapq.heappop(stubheap)
+            if stubout == 0:
+                return False
+            # Check if target is now totally connected
+            if stubout + 1 < 0 or stubin < 0:
+                modstubs[mslen] = (stubout + 1, stubin)
+                mslen += 1
+
+        # Add back the nodes to the heap that still have available stubs
+        for i in range(mslen):
+            stub = modstubs[i]
+            if stub[1] < 0:
+                heapq.heappush(stubheap, stub)
+            else:
+                heapq.heappush(zeroheap, stub[0])
+        if freeout < 0:
+            heapq.heappush(zeroheap, freeout)
+    return True
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/hierarchy.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/hierarchy.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5a05525e7ddf1e98b1e07f120df0b0b5b52414b
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/hierarchy.py
@@ -0,0 +1,57 @@
+"""
+Flow Hierarchy.
+"""
+
+import networkx as nx
+
+__all__ = ["flow_hierarchy"]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def flow_hierarchy(G, weight=None):
+    """Returns the flow hierarchy of a directed network.
+
+    Flow hierarchy is defined as the fraction of edges not participating
+    in cycles in a directed graph [1]_.
+
+    Parameters
+    ----------
+    G : DiGraph or MultiDiGraph
+       A directed graph
+
+    weight : string, optional (default=None)
+       Attribute to use for edge weights. If None the weight defaults to 1.
+
+    Returns
+    -------
+    h : float
+       Flow hierarchy value
+
+    Raises
+    ------
+    NetworkXError
+       If `G` is not a directed graph or if `G` has no edges.
+
+    Notes
+    -----
+    The algorithm described in [1]_ computes the flow hierarchy through
+    exponentiation of the adjacency matrix.  This function implements an
+    alternative approach that finds strongly connected components.
+    An edge is in a cycle if and only if it is in a strongly connected
+    component, which can be found in $O(m)$ time using Tarjan's algorithm.
+
+    References
+    ----------
+    .. [1] Luo, J.; Magee, C.L. (2011),
+       Detecting evolving patterns of self-organizing networks by flow
+       hierarchy measurement, Complexity, Volume 16 Issue 6 53-61.
+       DOI: 10.1002/cplx.20368
+       http://web.mit.edu/~cmagee/www/documents/28-DetectingEvolvingPatterns_FlowHierarchy.pdf
+    """
+    # corner case: G has no edges
+    if nx.is_empty(G):
+        raise nx.NetworkXError("flow_hierarchy not applicable to empty graphs")
+    if not G.is_directed():
+        raise nx.NetworkXError("G must be a digraph in flow_hierarchy")
+    scc = nx.strongly_connected_components(G)
+    return 1 - sum(G.subgraph(c).size(weight) for c in scc) / G.size(weight)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/hybrid.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/hybrid.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d3dd3078cd25fb520a20f5866043ad977ef02f5
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/hybrid.py
@@ -0,0 +1,196 @@
+"""
+Provides functions for finding and testing for locally `(k, l)`-connected
+graphs.
+
+"""
+
+import copy
+
+import networkx as nx
+
+__all__ = ["kl_connected_subgraph", "is_kl_connected"]
+
+
+@nx._dispatchable(returns_graph=True)
+def kl_connected_subgraph(G, k, l, low_memory=False, same_as_graph=False):
+    """Returns the maximum locally `(k, l)`-connected subgraph of `G`.
+
+    A graph is locally `(k, l)`-connected if for each edge `(u, v)` in the
+    graph there are at least `l` edge-disjoint paths of length at most `k`
+    joining `u` to `v`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The graph in which to find a maximum locally `(k, l)`-connected
+        subgraph.
+
+    k : integer
+        The maximum length of paths to consider. A higher number means a looser
+        connectivity requirement.
+
+    l : integer
+        The number of edge-disjoint paths. A higher number means a stricter
+        connectivity requirement.
+
+    low_memory : bool
+        If this is True, this function uses an algorithm that uses slightly
+        more time but less memory.
+
+    same_as_graph : bool
+        If True then return a tuple of the form `(H, is_same)`,
+        where `H` is the maximum locally `(k, l)`-connected subgraph and
+        `is_same` is a Boolean representing whether `G` is locally `(k,
+        l)`-connected (and hence, whether `H` is simply a copy of the input
+        graph `G`).
+
+    Returns
+    -------
+    NetworkX graph or two-tuple
+        If `same_as_graph` is True, then this function returns a
+        two-tuple as described above. Otherwise, it returns only the maximum
+        locally `(k, l)`-connected subgraph.
+
+    See also
+    --------
+    is_kl_connected
+
+    References
+    ----------
+    .. [1] Chung, Fan and Linyuan Lu. "The Small World Phenomenon in Hybrid
+           Power Law Graphs." *Complex Networks*. Springer Berlin Heidelberg,
+           2004. 89--104.
+
+    """
+    H = copy.deepcopy(G)  # subgraph we construct by removing from G
+
+    graphOK = True
+    deleted_some = True  # hack to start off the while loop
+    while deleted_some:
+        deleted_some = False
+        # We use `for edge in list(H.edges()):` instead of
+        # `for edge in H.edges():` because we edit the graph `H` in
+        # the loop. Hence using an iterator will result in
+        # `RuntimeError: dictionary changed size during iteration`
+        for edge in list(H.edges()):
+            (u, v) = edge
+            # Get copy of graph needed for this search
+            if low_memory:
+                verts = {u, v}
+                for i in range(k):
+                    for w in verts.copy():
+                        verts.update(G[w])
+                G2 = G.subgraph(verts).copy()
+            else:
+                G2 = copy.deepcopy(G)
+            ###
+            path = [u, v]
+            cnt = 0
+            accept = 0
+            while path:
+                cnt += 1  # Found a path
+                if cnt >= l:
+                    accept = 1
+                    break
+                # record edges along this graph
+                prev = u
+                for w in path:
+                    if prev != w:
+                        G2.remove_edge(prev, w)
+                        prev = w
+                #                path = shortest_path(G2, u, v, k) # ??? should "Cutoff" be k+1?
+                try:
+                    path = nx.shortest_path(G2, u, v)  # ??? should "Cutoff" be k+1?
+                except nx.NetworkXNoPath:
+                    path = False
+            # No Other Paths
+            if accept == 0:
+                H.remove_edge(u, v)
+                deleted_some = True
+                if graphOK:
+                    graphOK = False
+    # We looked through all edges and removed none of them.
+    # So, H is the maximal (k,l)-connected subgraph of G
+    if same_as_graph:
+        return (H, graphOK)
+    return H
+
+
+@nx._dispatchable
+def is_kl_connected(G, k, l, low_memory=False):
+    """Returns True if and only if `G` is locally `(k, l)`-connected.
+
+    A graph is locally `(k, l)`-connected if for each edge `(u, v)` in the
+    graph there are at least `l` edge-disjoint paths of length at most `k`
+    joining `u` to `v`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The graph to test for local `(k, l)`-connectedness.
+
+    k : integer
+        The maximum length of paths to consider. A higher number means a looser
+        connectivity requirement.
+
+    l : integer
+        The number of edge-disjoint paths. A higher number means a stricter
+        connectivity requirement.
+
+    low_memory : bool
+        If this is True, this function uses an algorithm that uses slightly
+        more time but less memory.
+
+    Returns
+    -------
+    bool
+        Whether the graph is locally `(k, l)`-connected subgraph.
+
+    See also
+    --------
+    kl_connected_subgraph
+
+    References
+    ----------
+    .. [1] Chung, Fan and Linyuan Lu. "The Small World Phenomenon in Hybrid
+           Power Law Graphs." *Complex Networks*. Springer Berlin Heidelberg,
+           2004. 89--104.
+
+    """
+    graphOK = True
+    for edge in G.edges():
+        (u, v) = edge
+        # Get copy of graph needed for this search
+        if low_memory:
+            verts = {u, v}
+            for i in range(k):
+                [verts.update(G.neighbors(w)) for w in verts.copy()]
+            G2 = G.subgraph(verts)
+        else:
+            G2 = copy.deepcopy(G)
+        ###
+        path = [u, v]
+        cnt = 0
+        accept = 0
+        while path:
+            cnt += 1  # Found a path
+            if cnt >= l:
+                accept = 1
+                break
+            # record edges along this graph
+            prev = u
+            for w in path:
+                if w != prev:
+                    G2.remove_edge(prev, w)
+                    prev = w
+            #            path = shortest_path(G2, u, v, k) # ??? should "Cutoff" be k+1?
+            try:
+                path = nx.shortest_path(G2, u, v)  # ??? should "Cutoff" be k+1?
+            except nx.NetworkXNoPath:
+                path = False
+        # No Other Paths
+        if accept == 0:
+            graphOK = False
+            break
+    # return status
+    return graphOK
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/isolate.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/isolate.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ea8abe9c8329c9f281059765aa8bfeb9487721f
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/isolate.py
@@ -0,0 +1,107 @@
+"""
+Functions for identifying isolate (degree zero) nodes.
+"""
+
+import networkx as nx
+
+__all__ = ["is_isolate", "isolates", "number_of_isolates"]
+
+
+@nx._dispatchable
+def is_isolate(G, n):
+    """Determines whether a node is an isolate.
+
+    An *isolate* is a node with no neighbors (that is, with degree
+    zero). For directed graphs, this means no in-neighbors and no
+    out-neighbors.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    n : node
+        A node in `G`.
+
+    Returns
+    -------
+    is_isolate : bool
+       True if and only if `n` has no neighbors.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_edge(1, 2)
+    >>> G.add_node(3)
+    >>> nx.is_isolate(G, 2)
+    False
+    >>> nx.is_isolate(G, 3)
+    True
+    """
+    return G.degree(n) == 0
+
+
+@nx._dispatchable
+def isolates(G):
+    """Iterator over isolates in the graph.
+
+    An *isolate* is a node with no neighbors (that is, with degree
+    zero). For directed graphs, this means no in-neighbors and no
+    out-neighbors.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    iterator
+        An iterator over the isolates of `G`.
+
+    Examples
+    --------
+    To get a list of all isolates of a graph, use the :class:`list`
+    constructor::
+
+        >>> G = nx.Graph()
+        >>> G.add_edge(1, 2)
+        >>> G.add_node(3)
+        >>> list(nx.isolates(G))
+        [3]
+
+    To remove all isolates in the graph, first create a list of the
+    isolates, then use :meth:`Graph.remove_nodes_from`::
+
+        >>> G.remove_nodes_from(list(nx.isolates(G)))
+        >>> list(G)
+        [1, 2]
+
+    For digraphs, isolates have zero in-degree and zero out_degre::
+
+        >>> G = nx.DiGraph([(0, 1), (1, 2)])
+        >>> G.add_node(3)
+        >>> list(nx.isolates(G))
+        [3]
+
+    """
+    return (n for n, d in G.degree() if d == 0)
+
+
+@nx._dispatchable
+def number_of_isolates(G):
+    """Returns the number of isolates in the graph.
+
+    An *isolate* is a node with no neighbors (that is, with degree
+    zero). For directed graphs, this means no in-neighbors and no
+    out-neighbors.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    int
+        The number of degree zero nodes in the graph `G`.
+
+    """
+    return sum(1 for v in isolates(G))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/link_prediction.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/link_prediction.py
new file mode 100644
index 0000000000000000000000000000000000000000..3615f26deb6d3c2f3c01e55f3fcf8ca3361968b3
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/link_prediction.py
@@ -0,0 +1,687 @@
+"""
+Link prediction algorithms.
+"""
+
+from math import log
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = [
+    "resource_allocation_index",
+    "jaccard_coefficient",
+    "adamic_adar_index",
+    "preferential_attachment",
+    "cn_soundarajan_hopcroft",
+    "ra_index_soundarajan_hopcroft",
+    "within_inter_cluster",
+    "common_neighbor_centrality",
+]
+
+
+def _apply_prediction(G, func, ebunch=None):
+    """Applies the given function to each edge in the specified iterable
+    of edges.
+
+    `G` is an instance of :class:`networkx.Graph`.
+
+    `func` is a function on two inputs, each of which is a node in the
+    graph. The function can return anything, but it should return a
+    value representing a prediction of the likelihood of a "link"
+    joining the two nodes.
+
+    `ebunch` is an iterable of pairs of nodes. If not specified, all
+    non-edges in the graph `G` will be used.
+
+    """
+    if ebunch is None:
+        ebunch = nx.non_edges(G)
+    else:
+        for u, v in ebunch:
+            if u not in G:
+                raise nx.NodeNotFound(f"Node {u} not in G.")
+            if v not in G:
+                raise nx.NodeNotFound(f"Node {v} not in G.")
+    return ((u, v, func(u, v)) for u, v in ebunch)
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def resource_allocation_index(G, ebunch=None):
+    r"""Compute the resource allocation index of all node pairs in ebunch.
+
+    Resource allocation index of `u` and `v` is defined as
+
+    .. math::
+
+        \sum_{w \in \Gamma(u) \cap \Gamma(v)} \frac{1}{|\Gamma(w)|}
+
+    where $\Gamma(u)$ denotes the set of neighbors of $u$.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX undirected graph.
+
+    ebunch : iterable of node pairs, optional (default = None)
+        Resource allocation index will be computed for each pair of
+        nodes given in the iterable. The pairs must be given as
+        2-tuples (u, v) where u and v are nodes in the graph. If ebunch
+        is None then all nonexistent edges in the graph will be used.
+        Default value: None.
+
+    Returns
+    -------
+    piter : iterator
+        An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
+        pair of nodes and p is their resource allocation index.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is a `DiGraph`, a `Multigraph` or a `MultiDiGraph`.
+
+    NodeNotFound
+        If `ebunch` has a node that is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> preds = nx.resource_allocation_index(G, [(0, 1), (2, 3)])
+    >>> for u, v, p in preds:
+    ...     print(f"({u}, {v}) -> {p:.8f}")
+    (0, 1) -> 0.75000000
+    (2, 3) -> 0.75000000
+
+    References
+    ----------
+    .. [1] T. Zhou, L. Lu, Y.-C. Zhang.
+       Predicting missing links via local information.
+       Eur. Phys. J. B 71 (2009) 623.
+       https://arxiv.org/pdf/0901.0553.pdf
+    """
+
+    def predict(u, v):
+        return sum(1 / G.degree(w) for w in nx.common_neighbors(G, u, v))
+
+    return _apply_prediction(G, predict, ebunch)
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def jaccard_coefficient(G, ebunch=None):
+    r"""Compute the Jaccard coefficient of all node pairs in ebunch.
+
+    Jaccard coefficient of nodes `u` and `v` is defined as
+
+    .. math::
+
+        \frac{|\Gamma(u) \cap \Gamma(v)|}{|\Gamma(u) \cup \Gamma(v)|}
+
+    where $\Gamma(u)$ denotes the set of neighbors of $u$.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX undirected graph.
+
+    ebunch : iterable of node pairs, optional (default = None)
+        Jaccard coefficient will be computed for each pair of nodes
+        given in the iterable. The pairs must be given as 2-tuples
+        (u, v) where u and v are nodes in the graph. If ebunch is None
+        then all nonexistent edges in the graph will be used.
+        Default value: None.
+
+    Returns
+    -------
+    piter : iterator
+        An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
+        pair of nodes and p is their Jaccard coefficient.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is a `DiGraph`, a `Multigraph` or a `MultiDiGraph`.
+
+    NodeNotFound
+        If `ebunch` has a node that is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> preds = nx.jaccard_coefficient(G, [(0, 1), (2, 3)])
+    >>> for u, v, p in preds:
+    ...     print(f"({u}, {v}) -> {p:.8f}")
+    (0, 1) -> 0.60000000
+    (2, 3) -> 0.60000000
+
+    References
+    ----------
+    .. [1] D. Liben-Nowell, J. Kleinberg.
+           The Link Prediction Problem for Social Networks (2004).
+           http://www.cs.cornell.edu/home/kleinber/link-pred.pdf
+    """
+
+    def predict(u, v):
+        union_size = len(set(G[u]) | set(G[v]))
+        if union_size == 0:
+            return 0
+        return len(nx.common_neighbors(G, u, v)) / union_size
+
+    return _apply_prediction(G, predict, ebunch)
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def adamic_adar_index(G, ebunch=None):
+    r"""Compute the Adamic-Adar index of all node pairs in ebunch.
+
+    Adamic-Adar index of `u` and `v` is defined as
+
+    .. math::
+
+        \sum_{w \in \Gamma(u) \cap \Gamma(v)} \frac{1}{\log |\Gamma(w)|}
+
+    where $\Gamma(u)$ denotes the set of neighbors of $u$.
+    This index leads to zero-division for nodes only connected via self-loops.
+    It is intended to be used when no self-loops are present.
+
+    Parameters
+    ----------
+    G : graph
+        NetworkX undirected graph.
+
+    ebunch : iterable of node pairs, optional (default = None)
+        Adamic-Adar index will be computed for each pair of nodes given
+        in the iterable. The pairs must be given as 2-tuples (u, v)
+        where u and v are nodes in the graph. If ebunch is None then all
+        nonexistent edges in the graph will be used.
+        Default value: None.
+
+    Returns
+    -------
+    piter : iterator
+        An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
+        pair of nodes and p is their Adamic-Adar index.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is a `DiGraph`, a `Multigraph` or a `MultiDiGraph`.
+
+    NodeNotFound
+        If `ebunch` has a node that is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> preds = nx.adamic_adar_index(G, [(0, 1), (2, 3)])
+    >>> for u, v, p in preds:
+    ...     print(f"({u}, {v}) -> {p:.8f}")
+    (0, 1) -> 2.16404256
+    (2, 3) -> 2.16404256
+
+    References
+    ----------
+    .. [1] D. Liben-Nowell, J. Kleinberg.
+           The Link Prediction Problem for Social Networks (2004).
+           http://www.cs.cornell.edu/home/kleinber/link-pred.pdf
+    """
+
+    def predict(u, v):
+        return sum(1 / log(G.degree(w)) for w in nx.common_neighbors(G, u, v))
+
+    return _apply_prediction(G, predict, ebunch)
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def common_neighbor_centrality(G, ebunch=None, alpha=0.8):
+    r"""Return the CCPA score for each pair of nodes.
+
+    Compute the Common Neighbor and Centrality based Parameterized Algorithm(CCPA)
+    score of all node pairs in ebunch.
+
+    CCPA score of `u` and `v` is defined as
+
+    .. math::
+
+        \alpha \cdot (|\Gamma (u){\cap }^{}\Gamma (v)|)+(1-\alpha )\cdot \frac{N}{{d}_{uv}}
+
+    where $\Gamma(u)$ denotes the set of neighbors of $u$, $\Gamma(v)$ denotes the
+    set of neighbors of $v$, $\alpha$ is  parameter varies between [0,1], $N$ denotes
+    total number of nodes in the Graph and ${d}_{uv}$ denotes shortest distance
+    between $u$ and $v$.
+
+    This algorithm is based on two vital properties of nodes, namely the number
+    of common neighbors and their centrality. Common neighbor refers to the common
+    nodes between two nodes. Centrality refers to the prestige that a node enjoys
+    in a network.
+
+    .. seealso::
+
+        :func:`common_neighbors`
+
+    Parameters
+    ----------
+    G : graph
+        NetworkX undirected graph.
+
+    ebunch : iterable of node pairs, optional (default = None)
+        Preferential attachment score will be computed for each pair of
+        nodes given in the iterable. The pairs must be given as
+        2-tuples (u, v) where u and v are nodes in the graph. If ebunch
+        is None then all nonexistent edges in the graph will be used.
+        Default value: None.
+
+    alpha : Parameter defined for participation of Common Neighbor
+            and Centrality Algorithm share. Values for alpha should
+            normally be between 0 and 1. Default value set to 0.8
+            because author found better performance at 0.8 for all the
+            dataset.
+            Default value: 0.8
+
+
+    Returns
+    -------
+    piter : iterator
+        An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
+        pair of nodes and p is their Common Neighbor and Centrality based
+        Parameterized Algorithm(CCPA) score.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is a `DiGraph`, a `Multigraph` or a `MultiDiGraph`.
+
+    NetworkXAlgorithmError
+        If self loops exist in `ebunch` or in `G` (if `ebunch` is `None`).
+
+    NodeNotFound
+        If `ebunch` has a node that is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> preds = nx.common_neighbor_centrality(G, [(0, 1), (2, 3)])
+    >>> for u, v, p in preds:
+    ...     print(f"({u}, {v}) -> {p}")
+    (0, 1) -> 3.4000000000000004
+    (2, 3) -> 3.4000000000000004
+
+    References
+    ----------
+    .. [1] Ahmad, I., Akhtar, M.U., Noor, S. et al.
+           Missing Link Prediction using Common Neighbor and Centrality based Parameterized Algorithm.
+           Sci Rep 10, 364 (2020).
+           https://doi.org/10.1038/s41598-019-57304-y
+    """
+
+    # When alpha == 1, the CCPA score simplifies to the number of common neighbors.
+    if alpha == 1:
+
+        def predict(u, v):
+            if u == v:
+                raise nx.NetworkXAlgorithmError("Self loops are not supported")
+
+            return len(nx.common_neighbors(G, u, v))
+
+    else:
+        spl = dict(nx.shortest_path_length(G))
+        inf = float("inf")
+
+        def predict(u, v):
+            if u == v:
+                raise nx.NetworkXAlgorithmError("Self loops are not supported")
+            path_len = spl[u].get(v, inf)
+
+            n_nbrs = len(nx.common_neighbors(G, u, v))
+            return alpha * n_nbrs + (1 - alpha) * len(G) / path_len
+
+    return _apply_prediction(G, predict, ebunch)
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def preferential_attachment(G, ebunch=None):
+    r"""Compute the preferential attachment score of all node pairs in ebunch.
+
+    Preferential attachment score of `u` and `v` is defined as
+
+    .. math::
+
+        |\Gamma(u)| |\Gamma(v)|
+
+    where $\Gamma(u)$ denotes the set of neighbors of $u$.
+
+    Parameters
+    ----------
+    G : graph
+        NetworkX undirected graph.
+
+    ebunch : iterable of node pairs, optional (default = None)
+        Preferential attachment score will be computed for each pair of
+        nodes given in the iterable. The pairs must be given as
+        2-tuples (u, v) where u and v are nodes in the graph. If ebunch
+        is None then all nonexistent edges in the graph will be used.
+        Default value: None.
+
+    Returns
+    -------
+    piter : iterator
+        An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
+        pair of nodes and p is their preferential attachment score.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is a `DiGraph`, a `Multigraph` or a `MultiDiGraph`.
+
+    NodeNotFound
+        If `ebunch` has a node that is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> preds = nx.preferential_attachment(G, [(0, 1), (2, 3)])
+    >>> for u, v, p in preds:
+    ...     print(f"({u}, {v}) -> {p}")
+    (0, 1) -> 16
+    (2, 3) -> 16
+
+    References
+    ----------
+    .. [1] D. Liben-Nowell, J. Kleinberg.
+           The Link Prediction Problem for Social Networks (2004).
+           http://www.cs.cornell.edu/home/kleinber/link-pred.pdf
+    """
+
+    def predict(u, v):
+        return G.degree(u) * G.degree(v)
+
+    return _apply_prediction(G, predict, ebunch)
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(node_attrs="community")
+def cn_soundarajan_hopcroft(G, ebunch=None, community="community"):
+    r"""Count the number of common neighbors of all node pairs in ebunch
+        using community information.
+
+    For two nodes $u$ and $v$, this function computes the number of
+    common neighbors and bonus one for each common neighbor belonging to
+    the same community as $u$ and $v$. Mathematically,
+
+    .. math::
+
+        |\Gamma(u) \cap \Gamma(v)| + \sum_{w \in \Gamma(u) \cap \Gamma(v)} f(w)
+
+    where $f(w)$ equals 1 if $w$ belongs to the same community as $u$
+    and $v$ or 0 otherwise and $\Gamma(u)$ denotes the set of
+    neighbors of $u$.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX undirected graph.
+
+    ebunch : iterable of node pairs, optional (default = None)
+        The score will be computed for each pair of nodes given in the
+        iterable. The pairs must be given as 2-tuples (u, v) where u
+        and v are nodes in the graph. If ebunch is None then all
+        nonexistent edges in the graph will be used.
+        Default value: None.
+
+    community : string, optional (default = 'community')
+        Nodes attribute name containing the community information.
+        G[u][community] identifies which community u belongs to. Each
+        node belongs to at most one community. Default value: 'community'.
+
+    Returns
+    -------
+    piter : iterator
+        An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
+        pair of nodes and p is their score.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is a `DiGraph`, a `Multigraph` or a `MultiDiGraph`.
+
+    NetworkXAlgorithmError
+        If no community information is available for a node in `ebunch` or in `G` (if `ebunch` is `None`).
+
+    NodeNotFound
+        If `ebunch` has a node that is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> G.nodes[0]["community"] = 0
+    >>> G.nodes[1]["community"] = 0
+    >>> G.nodes[2]["community"] = 0
+    >>> preds = nx.cn_soundarajan_hopcroft(G, [(0, 2)])
+    >>> for u, v, p in preds:
+    ...     print(f"({u}, {v}) -> {p}")
+    (0, 2) -> 2
+
+    References
+    ----------
+    .. [1] Sucheta Soundarajan and John Hopcroft.
+       Using community information to improve the precision of link
+       prediction methods.
+       In Proceedings of the 21st international conference companion on
+       World Wide Web (WWW '12 Companion). ACM, New York, NY, USA, 607-608.
+       http://doi.acm.org/10.1145/2187980.2188150
+    """
+
+    def predict(u, v):
+        Cu = _community(G, u, community)
+        Cv = _community(G, v, community)
+        cnbors = nx.common_neighbors(G, u, v)
+        neighbors = (
+            sum(_community(G, w, community) == Cu for w in cnbors) if Cu == Cv else 0
+        )
+        return len(cnbors) + neighbors
+
+    return _apply_prediction(G, predict, ebunch)
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(node_attrs="community")
+def ra_index_soundarajan_hopcroft(G, ebunch=None, community="community"):
+    r"""Compute the resource allocation index of all node pairs in
+    ebunch using community information.
+
+    For two nodes $u$ and $v$, this function computes the resource
+    allocation index considering only common neighbors belonging to the
+    same community as $u$ and $v$. Mathematically,
+
+    .. math::
+
+        \sum_{w \in \Gamma(u) \cap \Gamma(v)} \frac{f(w)}{|\Gamma(w)|}
+
+    where $f(w)$ equals 1 if $w$ belongs to the same community as $u$
+    and $v$ or 0 otherwise and $\Gamma(u)$ denotes the set of
+    neighbors of $u$.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX undirected graph.
+
+    ebunch : iterable of node pairs, optional (default = None)
+        The score will be computed for each pair of nodes given in the
+        iterable. The pairs must be given as 2-tuples (u, v) where u
+        and v are nodes in the graph. If ebunch is None then all
+        nonexistent edges in the graph will be used.
+        Default value: None.
+
+    community : string, optional (default = 'community')
+        Nodes attribute name containing the community information.
+        G[u][community] identifies which community u belongs to. Each
+        node belongs to at most one community. Default value: 'community'.
+
+    Returns
+    -------
+    piter : iterator
+        An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
+        pair of nodes and p is their score.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is a `DiGraph`, a `Multigraph` or a `MultiDiGraph`.
+
+    NetworkXAlgorithmError
+        If no community information is available for a node in `ebunch` or in `G` (if `ebunch` is `None`).
+
+    NodeNotFound
+        If `ebunch` has a node that is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
+    >>> G.nodes[0]["community"] = 0
+    >>> G.nodes[1]["community"] = 0
+    >>> G.nodes[2]["community"] = 1
+    >>> G.nodes[3]["community"] = 0
+    >>> preds = nx.ra_index_soundarajan_hopcroft(G, [(0, 3)])
+    >>> for u, v, p in preds:
+    ...     print(f"({u}, {v}) -> {p:.8f}")
+    (0, 3) -> 0.50000000
+
+    References
+    ----------
+    .. [1] Sucheta Soundarajan and John Hopcroft.
+       Using community information to improve the precision of link
+       prediction methods.
+       In Proceedings of the 21st international conference companion on
+       World Wide Web (WWW '12 Companion). ACM, New York, NY, USA, 607-608.
+       http://doi.acm.org/10.1145/2187980.2188150
+    """
+
+    def predict(u, v):
+        Cu = _community(G, u, community)
+        Cv = _community(G, v, community)
+        if Cu != Cv:
+            return 0
+        cnbors = nx.common_neighbors(G, u, v)
+        return sum(1 / G.degree(w) for w in cnbors if _community(G, w, community) == Cu)
+
+    return _apply_prediction(G, predict, ebunch)
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(node_attrs="community")
+def within_inter_cluster(G, ebunch=None, delta=0.001, community="community"):
+    """Compute the ratio of within- and inter-cluster common neighbors
+    of all node pairs in ebunch.
+
+    For two nodes `u` and `v`, if a common neighbor `w` belongs to the
+    same community as them, `w` is considered as within-cluster common
+    neighbor of `u` and `v`. Otherwise, it is considered as
+    inter-cluster common neighbor of `u` and `v`. The ratio between the
+    size of the set of within- and inter-cluster common neighbors is
+    defined as the WIC measure. [1]_
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX undirected graph.
+
+    ebunch : iterable of node pairs, optional (default = None)
+        The WIC measure will be computed for each pair of nodes given in
+        the iterable. The pairs must be given as 2-tuples (u, v) where
+        u and v are nodes in the graph. If ebunch is None then all
+        nonexistent edges in the graph will be used.
+        Default value: None.
+
+    delta : float, optional (default = 0.001)
+        Value to prevent division by zero in case there is no
+        inter-cluster common neighbor between two nodes. See [1]_ for
+        details. Default value: 0.001.
+
+    community : string, optional (default = 'community')
+        Nodes attribute name containing the community information.
+        G[u][community] identifies which community u belongs to. Each
+        node belongs to at most one community. Default value: 'community'.
+
+    Returns
+    -------
+    piter : iterator
+        An iterator of 3-tuples in the form (u, v, p) where (u, v) is a
+        pair of nodes and p is their WIC measure.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is a `DiGraph`, a `Multigraph` or a `MultiDiGraph`.
+
+    NetworkXAlgorithmError
+        - If `delta` is less than or equal to zero.
+        - If no community information is available for a node in `ebunch` or in `G` (if `ebunch` is `None`).
+
+    NodeNotFound
+        If `ebunch` has a node that is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 4), (2, 4), (3, 4)])
+    >>> G.nodes[0]["community"] = 0
+    >>> G.nodes[1]["community"] = 1
+    >>> G.nodes[2]["community"] = 0
+    >>> G.nodes[3]["community"] = 0
+    >>> G.nodes[4]["community"] = 0
+    >>> preds = nx.within_inter_cluster(G, [(0, 4)])
+    >>> for u, v, p in preds:
+    ...     print(f"({u}, {v}) -> {p:.8f}")
+    (0, 4) -> 1.99800200
+    >>> preds = nx.within_inter_cluster(G, [(0, 4)], delta=0.5)
+    >>> for u, v, p in preds:
+    ...     print(f"({u}, {v}) -> {p:.8f}")
+    (0, 4) -> 1.33333333
+
+    References
+    ----------
+    .. [1] Jorge Carlos Valverde-Rebaza and Alneu de Andrade Lopes.
+       Link prediction in complex networks based on cluster information.
+       In Proceedings of the 21st Brazilian conference on Advances in
+       Artificial Intelligence (SBIA'12)
+       https://doi.org/10.1007/978-3-642-34459-6_10
+    """
+    if delta <= 0:
+        raise nx.NetworkXAlgorithmError("Delta must be greater than zero")
+
+    def predict(u, v):
+        Cu = _community(G, u, community)
+        Cv = _community(G, v, community)
+        if Cu != Cv:
+            return 0
+        cnbors = nx.common_neighbors(G, u, v)
+        within = {w for w in cnbors if _community(G, w, community) == Cu}
+        inter = cnbors - within
+        return len(within) / (len(inter) + delta)
+
+    return _apply_prediction(G, predict, ebunch)
+
+
+def _community(G, u, community):
+    """Get the community of the given node."""
+    node_u = G.nodes[u]
+    try:
+        return node_u[community]
+    except KeyError as err:
+        raise nx.NetworkXAlgorithmError(
+            f"No community information available for Node {u}"
+        ) from err
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/lowest_common_ancestors.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/lowest_common_ancestors.py
new file mode 100644
index 0000000000000000000000000000000000000000..963a7839800b11d621a35e3e44b87711cbe40d63
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/lowest_common_ancestors.py
@@ -0,0 +1,280 @@
+"""Algorithms for finding the lowest common ancestor of trees and DAGs."""
+
+from collections import defaultdict
+from collections.abc import Mapping, Set
+from itertools import combinations_with_replacement
+
+import networkx as nx
+from networkx.utils import UnionFind, arbitrary_element, not_implemented_for
+
+__all__ = [
+    "all_pairs_lowest_common_ancestor",
+    "tree_all_pairs_lowest_common_ancestor",
+    "lowest_common_ancestor",
+]
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def all_pairs_lowest_common_ancestor(G, pairs=None):
+    """Return the lowest common ancestor of all pairs or the provided pairs
+
+    Parameters
+    ----------
+    G : NetworkX directed graph
+
+    pairs : iterable of pairs of nodes, optional (default: all pairs)
+        The pairs of nodes of interest.
+        If None, will find the LCA of all pairs of nodes.
+
+    Yields
+    ------
+    ((node1, node2), lca) : 2-tuple
+        Where lca is least common ancestor of node1 and node2.
+        Note that for the default case, the order of the node pair is not considered,
+        e.g. you will not get both ``(a, b)`` and ``(b, a)``
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `G` is null.
+    NetworkXError
+        If `G` is not a DAG.
+
+    Examples
+    --------
+    >>> from pprint import pprint
+
+    The default behavior is to yield the lowest common ancestor for all
+    possible combinations of nodes in `G`, including self-pairings:
+
+    >>> G = nx.DiGraph([(0, 1), (0, 3), (1, 2)])
+    >>> pprint(dict(nx.all_pairs_lowest_common_ancestor(G)))
+    {(0, 0): 0,
+     (0, 1): 0,
+     (0, 2): 0,
+     (0, 3): 0,
+     (1, 1): 1,
+     (1, 2): 1,
+     (1, 3): 0,
+     (2, 2): 2,
+     (3, 2): 0,
+     (3, 3): 3}
+
+    The pairs argument can be used to limit the output to only the
+    specified node pairings:
+
+    >>> dict(nx.all_pairs_lowest_common_ancestor(G, pairs=[(1, 2), (2, 3)]))
+    {(1, 2): 1, (2, 3): 0}
+
+    Notes
+    -----
+    Only defined on non-null directed acyclic graphs.
+
+    See Also
+    --------
+    lowest_common_ancestor
+    """
+    if not nx.is_directed_acyclic_graph(G):
+        raise nx.NetworkXError("LCA only defined on directed acyclic graphs.")
+    if len(G) == 0:
+        raise nx.NetworkXPointlessConcept("LCA meaningless on null graphs.")
+
+    if pairs is None:
+        pairs = combinations_with_replacement(G, 2)
+    else:
+        # Convert iterator to iterable, if necessary. Trim duplicates.
+        pairs = dict.fromkeys(pairs)
+        # Verify that each of the nodes in the provided pairs is in G
+        nodeset = set(G)
+        for pair in pairs:
+            if set(pair) - nodeset:
+                raise nx.NodeNotFound(
+                    f"Node(s) {set(pair) - nodeset} from pair {pair} not in G."
+                )
+
+    # Once input validation is done, construct the generator
+    def generate_lca_from_pairs(G, pairs):
+        ancestor_cache = {}
+
+        for v, w in pairs:
+            if v not in ancestor_cache:
+                ancestor_cache[v] = nx.ancestors(G, v)
+                ancestor_cache[v].add(v)
+            if w not in ancestor_cache:
+                ancestor_cache[w] = nx.ancestors(G, w)
+                ancestor_cache[w].add(w)
+
+            common_ancestors = ancestor_cache[v] & ancestor_cache[w]
+
+            if common_ancestors:
+                common_ancestor = next(iter(common_ancestors))
+                while True:
+                    successor = None
+                    for lower_ancestor in G.successors(common_ancestor):
+                        if lower_ancestor in common_ancestors:
+                            successor = lower_ancestor
+                            break
+                    if successor is None:
+                        break
+                    common_ancestor = successor
+                yield ((v, w), common_ancestor)
+
+    return generate_lca_from_pairs(G, pairs)
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def lowest_common_ancestor(G, node1, node2, default=None):
+    """Compute the lowest common ancestor of the given pair of nodes.
+
+    Parameters
+    ----------
+    G : NetworkX directed graph
+
+    node1, node2 : nodes in the graph.
+
+    default : object
+        Returned if no common ancestor between `node1` and `node2`
+
+    Returns
+    -------
+    The lowest common ancestor of node1 and node2,
+    or default if they have no common ancestors.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph()
+    >>> nx.add_path(G, (0, 1, 2, 3))
+    >>> nx.add_path(G, (0, 4, 3))
+    >>> nx.lowest_common_ancestor(G, 2, 4)
+    0
+
+    See Also
+    --------
+    all_pairs_lowest_common_ancestor"""
+
+    ans = list(all_pairs_lowest_common_ancestor(G, pairs=[(node1, node2)]))
+    if ans:
+        assert len(ans) == 1
+        return ans[0][1]
+    return default
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def tree_all_pairs_lowest_common_ancestor(G, root=None, pairs=None):
+    r"""Yield the lowest common ancestor for sets of pairs in a tree.
+
+    Parameters
+    ----------
+    G : NetworkX directed graph (must be a tree)
+
+    root : node, optional (default: None)
+        The root of the subtree to operate on.
+        If None, assume the entire graph has exactly one source and use that.
+
+    pairs : iterable or iterator of pairs of nodes, optional (default: None)
+        The pairs of interest. If None, Defaults to all pairs of nodes
+        under `root` that have a lowest common ancestor.
+
+    Returns
+    -------
+    lcas : generator of tuples `((u, v), lca)` where `u` and `v` are nodes
+        in `pairs` and `lca` is their lowest common ancestor.
+
+    Examples
+    --------
+    >>> import pprint
+    >>> G = nx.DiGraph([(1, 3), (2, 4), (1, 2)])
+    >>> pprint.pprint(dict(nx.tree_all_pairs_lowest_common_ancestor(G)))
+    {(1, 1): 1,
+     (2, 1): 1,
+     (2, 2): 2,
+     (3, 1): 1,
+     (3, 2): 1,
+     (3, 3): 3,
+     (3, 4): 1,
+     (4, 1): 1,
+     (4, 2): 2,
+     (4, 4): 4}
+
+    We can also use `pairs` argument to specify the pairs of nodes for which we
+    want to compute lowest common ancestors. Here is an example:
+
+    >>> dict(nx.tree_all_pairs_lowest_common_ancestor(G, pairs=[(1, 4), (2, 3)]))
+    {(2, 3): 1, (1, 4): 1}
+
+    Notes
+    -----
+    Only defined on non-null trees represented with directed edges from
+    parents to children. Uses Tarjan's off-line lowest-common-ancestors
+    algorithm. Runs in time $O(4 \times (V + E + P))$ time, where 4 is the largest
+    value of the inverse Ackermann function likely to ever come up in actual
+    use, and $P$ is the number of pairs requested (or $V^2$ if all are needed).
+
+    Tarjan, R. E. (1979), "Applications of path compression on balanced trees",
+    Journal of the ACM 26 (4): 690-715, doi:10.1145/322154.322161.
+
+    See Also
+    --------
+    all_pairs_lowest_common_ancestor: similar routine for general DAGs
+    lowest_common_ancestor: just a single pair for general DAGs
+    """
+    if len(G) == 0:
+        raise nx.NetworkXPointlessConcept("LCA meaningless on null graphs.")
+
+    # Index pairs of interest for efficient lookup from either side.
+    if pairs is not None:
+        pair_dict = defaultdict(set)
+        # See note on all_pairs_lowest_common_ancestor.
+        if not isinstance(pairs, Mapping | Set):
+            pairs = set(pairs)
+        for u, v in pairs:
+            for n in (u, v):
+                if n not in G:
+                    msg = f"The node {str(n)} is not in the digraph."
+                    raise nx.NodeNotFound(msg)
+            pair_dict[u].add(v)
+            pair_dict[v].add(u)
+
+    # If root is not specified, find the exactly one node with in degree 0 and
+    # use it. Raise an error if none are found, or more than one is. Also check
+    # for any nodes with in degree larger than 1, which would imply G is not a
+    # tree.
+    if root is None:
+        for n, deg in G.in_degree:
+            if deg == 0:
+                if root is not None:
+                    msg = "No root specified and tree has multiple sources."
+                    raise nx.NetworkXError(msg)
+                root = n
+            # checking deg>1 is not sufficient for MultiDiGraphs
+            elif deg > 1 and len(G.pred[n]) > 1:
+                msg = "Tree LCA only defined on trees; use DAG routine."
+                raise nx.NetworkXError(msg)
+    if root is None:
+        raise nx.NetworkXError("Graph contains a cycle.")
+
+    # Iterative implementation of Tarjan's offline lca algorithm
+    # as described in CLRS on page 521 (2nd edition)/page 584 (3rd edition)
+    uf = UnionFind()
+    ancestors = {}
+    for node in G:
+        ancestors[node] = uf[node]
+
+    colors = defaultdict(bool)
+    for node in nx.dfs_postorder_nodes(G, root):
+        colors[node] = True
+        for v in pair_dict[node] if pairs is not None else G:
+            if colors[v]:
+                # If the user requested both directions of a pair, give it.
+                # Otherwise, just give one.
+                if pairs is not None and (node, v) in pairs:
+                    yield (node, v), ancestors[uf[v]]
+                if pairs is None or (v, node) in pairs:
+                    yield (v, node), ancestors[uf[v]]
+        if node != root:
+            parent = arbitrary_element(G.pred[node])
+            uf.union(parent, node)
+            ancestors[uf[parent]] = parent
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/matching.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/matching.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d9fc58ef5fa84447fe29576ca93f6671833d942
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/matching.py
@@ -0,0 +1,1151 @@
+"""Functions for computing and verifying matchings in a graph."""
+
+from itertools import combinations, repeat
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = [
+    "is_matching",
+    "is_maximal_matching",
+    "is_perfect_matching",
+    "max_weight_matching",
+    "min_weight_matching",
+    "maximal_matching",
+]
+
+
+@not_implemented_for("multigraph")
+@not_implemented_for("directed")
+@nx._dispatchable
+def maximal_matching(G):
+    r"""Find a maximal matching in the graph.
+
+    A matching is a subset of edges in which no node occurs more than once.
+    A maximal matching cannot add more edges and still be a matching.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    Returns
+    -------
+    matching : set
+        A maximal matching of the graph.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (2, 4), (3, 5), (4, 5)])
+    >>> sorted(nx.maximal_matching(G))
+    [(1, 2), (3, 5)]
+
+    Notes
+    -----
+    The algorithm greedily selects a maximal matching M of the graph G
+    (i.e. no superset of M exists). It runs in $O(|E|)$ time.
+    """
+    matching = set()
+    nodes = set()
+    for edge in G.edges():
+        # If the edge isn't covered, add it to the matching
+        # then remove neighborhood of u and v from consideration.
+        u, v = edge
+        if u not in nodes and v not in nodes and u != v:
+            matching.add(edge)
+            nodes.update(edge)
+    return matching
+
+
+def matching_dict_to_set(matching):
+    """Converts matching dict format to matching set format
+
+    Converts a dictionary representing a matching (as returned by
+    :func:`max_weight_matching`) to a set representing a matching (as
+    returned by :func:`maximal_matching`).
+
+    In the definition of maximal matching adopted by NetworkX,
+    self-loops are not allowed, so the provided dictionary is expected
+    to never have any mapping from a key to itself. However, the
+    dictionary is expected to have mirrored key/value pairs, for
+    example, key ``u`` with value ``v`` and key ``v`` with value ``u``.
+
+    """
+    edges = set()
+    for edge in matching.items():
+        u, v = edge
+        if (v, u) in edges or edge in edges:
+            continue
+        if u == v:
+            raise nx.NetworkXError(f"Selfloops cannot appear in matchings {edge}")
+        edges.add(edge)
+    return edges
+
+
+@nx._dispatchable
+def is_matching(G, matching):
+    """Return True if ``matching`` is a valid matching of ``G``
+
+    A *matching* in a graph is a set of edges in which no two distinct
+    edges share a common endpoint. Each node is incident to at most one
+    edge in the matching. The edges are said to be independent.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    matching : dict or set
+        A dictionary or set representing a matching. If a dictionary, it
+        must have ``matching[u] == v`` and ``matching[v] == u`` for each
+        edge ``(u, v)`` in the matching. If a set, it must have elements
+        of the form ``(u, v)``, where ``(u, v)`` is an edge in the
+        matching.
+
+    Returns
+    -------
+    bool
+        Whether the given set or dictionary represents a valid matching
+        in the graph.
+
+    Raises
+    ------
+    NetworkXError
+        If the proposed matching has an edge to a node not in G.
+        Or if the matching is not a collection of 2-tuple edges.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (2, 4), (3, 5), (4, 5)])
+    >>> nx.is_maximal_matching(G, {1: 3, 2: 4})  # using dict to represent matching
+    True
+
+    >>> nx.is_matching(G, {(1, 3), (2, 4)})  # using set to represent matching
+    True
+
+    """
+    if isinstance(matching, dict):
+        matching = matching_dict_to_set(matching)
+
+    nodes = set()
+    for edge in matching:
+        if len(edge) != 2:
+            raise nx.NetworkXError(f"matching has non-2-tuple edge {edge}")
+        u, v = edge
+        if u not in G or v not in G:
+            raise nx.NetworkXError(f"matching contains edge {edge} with node not in G")
+        if u == v:
+            return False
+        if not G.has_edge(u, v):
+            return False
+        if u in nodes or v in nodes:
+            return False
+        nodes.update(edge)
+    return True
+
+
+@nx._dispatchable
+def is_maximal_matching(G, matching):
+    """Return True if ``matching`` is a maximal matching of ``G``
+
+    A *maximal matching* in a graph is a matching in which adding any
+    edge would cause the set to no longer be a valid matching.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    matching : dict or set
+        A dictionary or set representing a matching. If a dictionary, it
+        must have ``matching[u] == v`` and ``matching[v] == u`` for each
+        edge ``(u, v)`` in the matching. If a set, it must have elements
+        of the form ``(u, v)``, where ``(u, v)`` is an edge in the
+        matching.
+
+    Returns
+    -------
+    bool
+        Whether the given set or dictionary represents a valid maximal
+        matching in the graph.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (3, 4), (3, 5)])
+    >>> nx.is_maximal_matching(G, {(1, 2), (3, 4)})
+    True
+
+    """
+    if isinstance(matching, dict):
+        matching = matching_dict_to_set(matching)
+    # If the given set is not a matching, then it is not a maximal matching.
+    edges = set()
+    nodes = set()
+    for edge in matching:
+        if len(edge) != 2:
+            raise nx.NetworkXError(f"matching has non-2-tuple edge {edge}")
+        u, v = edge
+        if u not in G or v not in G:
+            raise nx.NetworkXError(f"matching contains edge {edge} with node not in G")
+        if u == v:
+            return False
+        if not G.has_edge(u, v):
+            return False
+        if u in nodes or v in nodes:
+            return False
+        nodes.update(edge)
+        edges.add(edge)
+        edges.add((v, u))
+    # A matching is maximal if adding any new edge from G to it
+    # causes the resulting set to match some node twice.
+    # Be careful to check for adding selfloops
+    for u, v in G.edges:
+        if (u, v) not in edges:
+            # could add edge (u, v) to edges and have a bigger matching
+            if u not in nodes and v not in nodes and u != v:
+                return False
+    return True
+
+
+@nx._dispatchable
+def is_perfect_matching(G, matching):
+    """Return True if ``matching`` is a perfect matching for ``G``
+
+    A *perfect matching* in a graph is a matching in which exactly one edge
+    is incident upon each vertex.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    matching : dict or set
+        A dictionary or set representing a matching. If a dictionary, it
+        must have ``matching[u] == v`` and ``matching[v] == u`` for each
+        edge ``(u, v)`` in the matching. If a set, it must have elements
+        of the form ``(u, v)``, where ``(u, v)`` is an edge in the
+        matching.
+
+    Returns
+    -------
+    bool
+        Whether the given set or dictionary represents a valid perfect
+        matching in the graph.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (1, 3), (2, 3), (2, 4), (3, 5), (4, 5), (4, 6)])
+    >>> my_match = {1: 2, 3: 5, 4: 6}
+    >>> nx.is_perfect_matching(G, my_match)
+    True
+
+    """
+    if isinstance(matching, dict):
+        matching = matching_dict_to_set(matching)
+
+    nodes = set()
+    for edge in matching:
+        if len(edge) != 2:
+            raise nx.NetworkXError(f"matching has non-2-tuple edge {edge}")
+        u, v = edge
+        if u not in G or v not in G:
+            raise nx.NetworkXError(f"matching contains edge {edge} with node not in G")
+        if u == v:
+            return False
+        if not G.has_edge(u, v):
+            return False
+        if u in nodes or v in nodes:
+            return False
+        nodes.update(edge)
+    return len(nodes) == len(G)
+
+
+@not_implemented_for("multigraph")
+@not_implemented_for("directed")
+@nx._dispatchable(edge_attrs="weight")
+def min_weight_matching(G, weight="weight"):
+    """Computing a minimum-weight maximal matching of G.
+
+    Use the maximum-weight algorithm with edge weights subtracted
+    from the maximum weight of all edges.
+
+    A matching is a subset of edges in which no node occurs more than once.
+    The weight of a matching is the sum of the weights of its edges.
+    A maximal matching cannot add more edges and still be a matching.
+    The cardinality of a matching is the number of matched edges.
+
+    This method replaces the edge weights with 1 plus the maximum edge weight
+    minus the original edge weight.
+
+    new_weight = (max_weight + 1) - edge_weight
+
+    then runs :func:`max_weight_matching` with the new weights.
+    The max weight matching with these new weights corresponds
+    to the min weight matching using the original weights.
+    Adding 1 to the max edge weight keeps all edge weights positive
+    and as integers if they started as integers.
+
+    You might worry that adding 1 to each weight would make the algorithm
+    favor matchings with more edges. But we use the parameter
+    `maxcardinality=True` in `max_weight_matching` to ensure that the
+    number of edges in the competing matchings are the same and thus
+    the optimum does not change due to changes in the number of edges.
+
+    Read the documentation of `max_weight_matching` for more information.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+      Undirected graph
+
+    weight: string, optional (default='weight')
+       Edge data key corresponding to the edge weight.
+       If key not found, uses 1 as weight.
+
+    Returns
+    -------
+    matching : set
+        A minimal weight matching of the graph.
+
+    See Also
+    --------
+    max_weight_matching
+    """
+    if len(G.edges) == 0:
+        return max_weight_matching(G, maxcardinality=True, weight=weight)
+    G_edges = G.edges(data=weight, default=1)
+    max_weight = 1 + max(w for _, _, w in G_edges)
+    InvG = nx.Graph()
+    edges = ((u, v, max_weight - w) for u, v, w in G_edges)
+    InvG.add_weighted_edges_from(edges, weight=weight)
+    return max_weight_matching(InvG, maxcardinality=True, weight=weight)
+
+
+@not_implemented_for("multigraph")
+@not_implemented_for("directed")
+@nx._dispatchable(edge_attrs="weight")
+def max_weight_matching(G, maxcardinality=False, weight="weight"):
+    """Compute a maximum-weighted matching of G.
+
+    A matching is a subset of edges in which no node occurs more than once.
+    The weight of a matching is the sum of the weights of its edges.
+    A maximal matching cannot add more edges and still be a matching.
+    The cardinality of a matching is the number of matched edges.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+      Undirected graph
+
+    maxcardinality: bool, optional (default=False)
+       If maxcardinality is True, compute the maximum-cardinality matching
+       with maximum weight among all maximum-cardinality matchings.
+
+    weight: string, optional (default='weight')
+       Edge data key corresponding to the edge weight.
+       If key not found, uses 1 as weight.
+
+
+    Returns
+    -------
+    matching : set
+        A maximal matching of the graph.
+
+     Examples
+    --------
+    >>> G = nx.Graph()
+    >>> edges = [(1, 2, 6), (1, 3, 2), (2, 3, 1), (2, 4, 7), (3, 5, 9), (4, 5, 3)]
+    >>> G.add_weighted_edges_from(edges)
+    >>> sorted(nx.max_weight_matching(G))
+    [(2, 4), (5, 3)]
+
+    Notes
+    -----
+    If G has edges with weight attributes the edge data are used as
+    weight values else the weights are assumed to be 1.
+
+    This function takes time O(number_of_nodes ** 3).
+
+    If all edge weights are integers, the algorithm uses only integer
+    computations.  If floating point weights are used, the algorithm
+    could return a slightly suboptimal matching due to numeric
+    precision errors.
+
+    This method is based on the "blossom" method for finding augmenting
+    paths and the "primal-dual" method for finding a matching of maximum
+    weight, both methods invented by Jack Edmonds [1]_.
+
+    Bipartite graphs can also be matched using the functions present in
+    :mod:`networkx.algorithms.bipartite.matching`.
+
+    References
+    ----------
+    .. [1] "Efficient Algorithms for Finding Maximum Matching in Graphs",
+       Zvi Galil, ACM Computing Surveys, 1986.
+    """
+    #
+    # The algorithm is taken from "Efficient Algorithms for Finding Maximum
+    # Matching in Graphs" by Zvi Galil, ACM Computing Surveys, 1986.
+    # It is based on the "blossom" method for finding augmenting paths and
+    # the "primal-dual" method for finding a matching of maximum weight, both
+    # methods invented by Jack Edmonds.
+    #
+    # A C program for maximum weight matching by Ed Rothberg was used
+    # extensively to validate this new code.
+    #
+    # Many terms used in the code comments are explained in the paper
+    # by Galil. You will probably need the paper to make sense of this code.
+    #
+
+    class NoNode:
+        """Dummy value which is different from any node."""
+
+    class Blossom:
+        """Representation of a non-trivial blossom or sub-blossom."""
+
+        __slots__ = ["childs", "edges", "mybestedges"]
+
+        # b.childs is an ordered list of b's sub-blossoms, starting with
+        # the base and going round the blossom.
+
+        # b.edges is the list of b's connecting edges, such that
+        # b.edges[i] = (v, w) where v is a vertex in b.childs[i]
+        # and w is a vertex in b.childs[wrap(i+1)].
+
+        # If b is a top-level S-blossom,
+        # b.mybestedges is a list of least-slack edges to neighboring
+        # S-blossoms, or None if no such list has been computed yet.
+        # This is used for efficient computation of delta3.
+
+        # Generate the blossom's leaf vertices.
+        def leaves(self):
+            stack = [*self.childs]
+            while stack:
+                t = stack.pop()
+                if isinstance(t, Blossom):
+                    stack.extend(t.childs)
+                else:
+                    yield t
+
+    # Get a list of vertices.
+    gnodes = list(G)
+    if not gnodes:
+        return set()  # don't bother with empty graphs
+
+    # Find the maximum edge weight.
+    maxweight = 0
+    allinteger = True
+    for i, j, d in G.edges(data=True):
+        wt = d.get(weight, 1)
+        if i != j and wt > maxweight:
+            maxweight = wt
+        allinteger = allinteger and (str(type(wt)).split("'")[1] in ("int", "long"))
+
+    # If v is a matched vertex, mate[v] is its partner vertex.
+    # If v is a single vertex, v does not occur as a key in mate.
+    # Initially all vertices are single; updated during augmentation.
+    mate = {}
+
+    # If b is a top-level blossom,
+    # label.get(b) is None if b is unlabeled (free),
+    #                 1 if b is an S-blossom,
+    #                 2 if b is a T-blossom.
+    # The label of a vertex is found by looking at the label of its top-level
+    # containing blossom.
+    # If v is a vertex inside a T-blossom, label[v] is 2 iff v is reachable
+    # from an S-vertex outside the blossom.
+    # Labels are assigned during a stage and reset after each augmentation.
+    label = {}
+
+    # If b is a labeled top-level blossom,
+    # labeledge[b] = (v, w) is the edge through which b obtained its label
+    # such that w is a vertex in b, or None if b's base vertex is single.
+    # If w is a vertex inside a T-blossom and label[w] == 2,
+    # labeledge[w] = (v, w) is an edge through which w is reachable from
+    # outside the blossom.
+    labeledge = {}
+
+    # If v is a vertex, inblossom[v] is the top-level blossom to which v
+    # belongs.
+    # If v is a top-level vertex, inblossom[v] == v since v is itself
+    # a (trivial) top-level blossom.
+    # Initially all vertices are top-level trivial blossoms.
+    inblossom = dict(zip(gnodes, gnodes))
+
+    # If b is a sub-blossom,
+    # blossomparent[b] is its immediate parent (sub-)blossom.
+    # If b is a top-level blossom, blossomparent[b] is None.
+    blossomparent = dict(zip(gnodes, repeat(None)))
+
+    # If b is a (sub-)blossom,
+    # blossombase[b] is its base VERTEX (i.e. recursive sub-blossom).
+    blossombase = dict(zip(gnodes, gnodes))
+
+    # If w is a free vertex (or an unreached vertex inside a T-blossom),
+    # bestedge[w] = (v, w) is the least-slack edge from an S-vertex,
+    # or None if there is no such edge.
+    # If b is a (possibly trivial) top-level S-blossom,
+    # bestedge[b] = (v, w) is the least-slack edge to a different S-blossom
+    # (v inside b), or None if there is no such edge.
+    # This is used for efficient computation of delta2 and delta3.
+    bestedge = {}
+
+    # If v is a vertex,
+    # dualvar[v] = 2 * u(v) where u(v) is the v's variable in the dual
+    # optimization problem (if all edge weights are integers, multiplication
+    # by two ensures that all values remain integers throughout the algorithm).
+    # Initially, u(v) = maxweight / 2.
+    dualvar = dict(zip(gnodes, repeat(maxweight)))
+
+    # If b is a non-trivial blossom,
+    # blossomdual[b] = z(b) where z(b) is b's variable in the dual
+    # optimization problem.
+    blossomdual = {}
+
+    # If (v, w) in allowedge or (w, v) in allowedg, then the edge
+    # (v, w) is known to have zero slack in the optimization problem;
+    # otherwise the edge may or may not have zero slack.
+    allowedge = {}
+
+    # Queue of newly discovered S-vertices.
+    queue = []
+
+    # Return 2 * slack of edge (v, w) (does not work inside blossoms).
+    def slack(v, w):
+        return dualvar[v] + dualvar[w] - 2 * G[v][w].get(weight, 1)
+
+    # Assign label t to the top-level blossom containing vertex w,
+    # coming through an edge from vertex v.
+    def assignLabel(w, t, v):
+        b = inblossom[w]
+        assert label.get(w) is None and label.get(b) is None
+        label[w] = label[b] = t
+        if v is not None:
+            labeledge[w] = labeledge[b] = (v, w)
+        else:
+            labeledge[w] = labeledge[b] = None
+        bestedge[w] = bestedge[b] = None
+        if t == 1:
+            # b became an S-vertex/blossom; add it(s vertices) to the queue.
+            if isinstance(b, Blossom):
+                queue.extend(b.leaves())
+            else:
+                queue.append(b)
+        elif t == 2:
+            # b became a T-vertex/blossom; assign label S to its mate.
+            # (If b is a non-trivial blossom, its base is the only vertex
+            # with an external mate.)
+            base = blossombase[b]
+            assignLabel(mate[base], 1, base)
+
+    # Trace back from vertices v and w to discover either a new blossom
+    # or an augmenting path. Return the base vertex of the new blossom,
+    # or NoNode if an augmenting path was found.
+    def scanBlossom(v, w):
+        # Trace back from v and w, placing breadcrumbs as we go.
+        path = []
+        base = NoNode
+        while v is not NoNode:
+            # Look for a breadcrumb in v's blossom or put a new breadcrumb.
+            b = inblossom[v]
+            if label[b] & 4:
+                base = blossombase[b]
+                break
+            assert label[b] == 1
+            path.append(b)
+            label[b] = 5
+            # Trace one step back.
+            if labeledge[b] is None:
+                # The base of blossom b is single; stop tracing this path.
+                assert blossombase[b] not in mate
+                v = NoNode
+            else:
+                assert labeledge[b][0] == mate[blossombase[b]]
+                v = labeledge[b][0]
+                b = inblossom[v]
+                assert label[b] == 2
+                # b is a T-blossom; trace one more step back.
+                v = labeledge[b][0]
+            # Swap v and w so that we alternate between both paths.
+            if w is not NoNode:
+                v, w = w, v
+        # Remove breadcrumbs.
+        for b in path:
+            label[b] = 1
+        # Return base vertex, if we found one.
+        return base
+
+    # Construct a new blossom with given base, through S-vertices v and w.
+    # Label the new blossom as S; set its dual variable to zero;
+    # relabel its T-vertices to S and add them to the queue.
+    def addBlossom(base, v, w):
+        bb = inblossom[base]
+        bv = inblossom[v]
+        bw = inblossom[w]
+        # Create blossom.
+        b = Blossom()
+        blossombase[b] = base
+        blossomparent[b] = None
+        blossomparent[bb] = b
+        # Make list of sub-blossoms and their interconnecting edge endpoints.
+        b.childs = path = []
+        b.edges = edgs = [(v, w)]
+        # Trace back from v to base.
+        while bv != bb:
+            # Add bv to the new blossom.
+            blossomparent[bv] = b
+            path.append(bv)
+            edgs.append(labeledge[bv])
+            assert label[bv] == 2 or (
+                label[bv] == 1 and labeledge[bv][0] == mate[blossombase[bv]]
+            )
+            # Trace one step back.
+            v = labeledge[bv][0]
+            bv = inblossom[v]
+        # Add base sub-blossom; reverse lists.
+        path.append(bb)
+        path.reverse()
+        edgs.reverse()
+        # Trace back from w to base.
+        while bw != bb:
+            # Add bw to the new blossom.
+            blossomparent[bw] = b
+            path.append(bw)
+            edgs.append((labeledge[bw][1], labeledge[bw][0]))
+            assert label[bw] == 2 or (
+                label[bw] == 1 and labeledge[bw][0] == mate[blossombase[bw]]
+            )
+            # Trace one step back.
+            w = labeledge[bw][0]
+            bw = inblossom[w]
+        # Set label to S.
+        assert label[bb] == 1
+        label[b] = 1
+        labeledge[b] = labeledge[bb]
+        # Set dual variable to zero.
+        blossomdual[b] = 0
+        # Relabel vertices.
+        for v in b.leaves():
+            if label[inblossom[v]] == 2:
+                # This T-vertex now turns into an S-vertex because it becomes
+                # part of an S-blossom; add it to the queue.
+                queue.append(v)
+            inblossom[v] = b
+        # Compute b.mybestedges.
+        bestedgeto = {}
+        for bv in path:
+            if isinstance(bv, Blossom):
+                if bv.mybestedges is not None:
+                    # Walk this subblossom's least-slack edges.
+                    nblist = bv.mybestedges
+                    # The sub-blossom won't need this data again.
+                    bv.mybestedges = None
+                else:
+                    # This subblossom does not have a list of least-slack
+                    # edges; get the information from the vertices.
+                    nblist = [
+                        (v, w) for v in bv.leaves() for w in G.neighbors(v) if v != w
+                    ]
+            else:
+                nblist = [(bv, w) for w in G.neighbors(bv) if bv != w]
+            for k in nblist:
+                (i, j) = k
+                if inblossom[j] == b:
+                    i, j = j, i
+                bj = inblossom[j]
+                if (
+                    bj != b
+                    and label.get(bj) == 1
+                    and ((bj not in bestedgeto) or slack(i, j) < slack(*bestedgeto[bj]))
+                ):
+                    bestedgeto[bj] = k
+            # Forget about least-slack edge of the subblossom.
+            bestedge[bv] = None
+        b.mybestedges = list(bestedgeto.values())
+        # Select bestedge[b].
+        mybestedge = None
+        bestedge[b] = None
+        for k in b.mybestedges:
+            kslack = slack(*k)
+            if mybestedge is None or kslack < mybestslack:
+                mybestedge = k
+                mybestslack = kslack
+        bestedge[b] = mybestedge
+
+    # Expand the given top-level blossom.
+    def expandBlossom(b, endstage):
+        # This is an obnoxiously complicated recursive function for the sake of
+        # a stack-transformation.  So, we hack around the complexity by using
+        # a trampoline pattern.  By yielding the arguments to each recursive
+        # call, we keep the actual callstack flat.
+
+        def _recurse(b, endstage):
+            # Convert sub-blossoms into top-level blossoms.
+            for s in b.childs:
+                blossomparent[s] = None
+                if isinstance(s, Blossom):
+                    if endstage and blossomdual[s] == 0:
+                        # Recursively expand this sub-blossom.
+                        yield s
+                    else:
+                        for v in s.leaves():
+                            inblossom[v] = s
+                else:
+                    inblossom[s] = s
+            # If we expand a T-blossom during a stage, its sub-blossoms must be
+            # relabeled.
+            if (not endstage) and label.get(b) == 2:
+                # Start at the sub-blossom through which the expanding
+                # blossom obtained its label, and relabel sub-blossoms untili
+                # we reach the base.
+                # Figure out through which sub-blossom the expanding blossom
+                # obtained its label initially.
+                entrychild = inblossom[labeledge[b][1]]
+                # Decide in which direction we will go round the blossom.
+                j = b.childs.index(entrychild)
+                if j & 1:
+                    # Start index is odd; go forward and wrap.
+                    j -= len(b.childs)
+                    jstep = 1
+                else:
+                    # Start index is even; go backward.
+                    jstep = -1
+                # Move along the blossom until we get to the base.
+                v, w = labeledge[b]
+                while j != 0:
+                    # Relabel the T-sub-blossom.
+                    if jstep == 1:
+                        p, q = b.edges[j]
+                    else:
+                        q, p = b.edges[j - 1]
+                    label[w] = None
+                    label[q] = None
+                    assignLabel(w, 2, v)
+                    # Step to the next S-sub-blossom and note its forward edge.
+                    allowedge[(p, q)] = allowedge[(q, p)] = True
+                    j += jstep
+                    if jstep == 1:
+                        v, w = b.edges[j]
+                    else:
+                        w, v = b.edges[j - 1]
+                    # Step to the next T-sub-blossom.
+                    allowedge[(v, w)] = allowedge[(w, v)] = True
+                    j += jstep
+                # Relabel the base T-sub-blossom WITHOUT stepping through to
+                # its mate (so don't call assignLabel).
+                bw = b.childs[j]
+                label[w] = label[bw] = 2
+                labeledge[w] = labeledge[bw] = (v, w)
+                bestedge[bw] = None
+                # Continue along the blossom until we get back to entrychild.
+                j += jstep
+                while b.childs[j] != entrychild:
+                    # Examine the vertices of the sub-blossom to see whether
+                    # it is reachable from a neighboring S-vertex outside the
+                    # expanding blossom.
+                    bv = b.childs[j]
+                    if label.get(bv) == 1:
+                        # This sub-blossom just got label S through one of its
+                        # neighbors; leave it be.
+                        j += jstep
+                        continue
+                    if isinstance(bv, Blossom):
+                        for v in bv.leaves():
+                            if label.get(v):
+                                break
+                    else:
+                        v = bv
+                    # If the sub-blossom contains a reachable vertex, assign
+                    # label T to the sub-blossom.
+                    if label.get(v):
+                        assert label[v] == 2
+                        assert inblossom[v] == bv
+                        label[v] = None
+                        label[mate[blossombase[bv]]] = None
+                        assignLabel(v, 2, labeledge[v][0])
+                    j += jstep
+            # Remove the expanded blossom entirely.
+            label.pop(b, None)
+            labeledge.pop(b, None)
+            bestedge.pop(b, None)
+            del blossomparent[b]
+            del blossombase[b]
+            del blossomdual[b]
+
+        # Now, we apply the trampoline pattern.  We simulate a recursive
+        # callstack by maintaining a stack of generators, each yielding a
+        # sequence of function arguments.  We grow the stack by appending a call
+        # to _recurse on each argument tuple, and shrink the stack whenever a
+        # generator is exhausted.
+        stack = [_recurse(b, endstage)]
+        while stack:
+            top = stack[-1]
+            for s in top:
+                stack.append(_recurse(s, endstage))
+                break
+            else:
+                stack.pop()
+
+    # Swap matched/unmatched edges over an alternating path through blossom b
+    # between vertex v and the base vertex. Keep blossom bookkeeping
+    # consistent.
+    def augmentBlossom(b, v):
+        # This is an obnoxiously complicated recursive function for the sake of
+        # a stack-transformation.  So, we hack around the complexity by using
+        # a trampoline pattern.  By yielding the arguments to each recursive
+        # call, we keep the actual callstack flat.
+
+        def _recurse(b, v):
+            # Bubble up through the blossom tree from vertex v to an immediate
+            # sub-blossom of b.
+            t = v
+            while blossomparent[t] != b:
+                t = blossomparent[t]
+            # Recursively deal with the first sub-blossom.
+            if isinstance(t, Blossom):
+                yield (t, v)
+            # Decide in which direction we will go round the blossom.
+            i = j = b.childs.index(t)
+            if i & 1:
+                # Start index is odd; go forward and wrap.
+                j -= len(b.childs)
+                jstep = 1
+            else:
+                # Start index is even; go backward.
+                jstep = -1
+            # Move along the blossom until we get to the base.
+            while j != 0:
+                # Step to the next sub-blossom and augment it recursively.
+                j += jstep
+                t = b.childs[j]
+                if jstep == 1:
+                    w, x = b.edges[j]
+                else:
+                    x, w = b.edges[j - 1]
+                if isinstance(t, Blossom):
+                    yield (t, w)
+                # Step to the next sub-blossom and augment it recursively.
+                j += jstep
+                t = b.childs[j]
+                if isinstance(t, Blossom):
+                    yield (t, x)
+                # Match the edge connecting those sub-blossoms.
+                mate[w] = x
+                mate[x] = w
+            # Rotate the list of sub-blossoms to put the new base at the front.
+            b.childs = b.childs[i:] + b.childs[:i]
+            b.edges = b.edges[i:] + b.edges[:i]
+            blossombase[b] = blossombase[b.childs[0]]
+            assert blossombase[b] == v
+
+        # Now, we apply the trampoline pattern.  We simulate a recursive
+        # callstack by maintaining a stack of generators, each yielding a
+        # sequence of function arguments.  We grow the stack by appending a call
+        # to _recurse on each argument tuple, and shrink the stack whenever a
+        # generator is exhausted.
+        stack = [_recurse(b, v)]
+        while stack:
+            top = stack[-1]
+            for args in top:
+                stack.append(_recurse(*args))
+                break
+            else:
+                stack.pop()
+
+    # Swap matched/unmatched edges over an alternating path between two
+    # single vertices. The augmenting path runs through S-vertices v and w.
+    def augmentMatching(v, w):
+        for s, j in ((v, w), (w, v)):
+            # Match vertex s to vertex j. Then trace back from s
+            # until we find a single vertex, swapping matched and unmatched
+            # edges as we go.
+            while 1:
+                bs = inblossom[s]
+                assert label[bs] == 1
+                assert (labeledge[bs] is None and blossombase[bs] not in mate) or (
+                    labeledge[bs][0] == mate[blossombase[bs]]
+                )
+                # Augment through the S-blossom from s to base.
+                if isinstance(bs, Blossom):
+                    augmentBlossom(bs, s)
+                # Update mate[s]
+                mate[s] = j
+                # Trace one step back.
+                if labeledge[bs] is None:
+                    # Reached single vertex; stop.
+                    break
+                t = labeledge[bs][0]
+                bt = inblossom[t]
+                assert label[bt] == 2
+                # Trace one more step back.
+                s, j = labeledge[bt]
+                # Augment through the T-blossom from j to base.
+                assert blossombase[bt] == t
+                if isinstance(bt, Blossom):
+                    augmentBlossom(bt, j)
+                # Update mate[j]
+                mate[j] = s
+
+    # Verify that the optimum solution has been reached.
+    def verifyOptimum():
+        if maxcardinality:
+            # Vertices may have negative dual;
+            # find a constant non-negative number to add to all vertex duals.
+            vdualoffset = max(0, -min(dualvar.values()))
+        else:
+            vdualoffset = 0
+        # 0. all dual variables are non-negative
+        assert min(dualvar.values()) + vdualoffset >= 0
+        assert len(blossomdual) == 0 or min(blossomdual.values()) >= 0
+        # 0. all edges have non-negative slack and
+        # 1. all matched edges have zero slack;
+        for i, j, d in G.edges(data=True):
+            wt = d.get(weight, 1)
+            if i == j:
+                continue  # ignore self-loops
+            s = dualvar[i] + dualvar[j] - 2 * wt
+            iblossoms = [i]
+            jblossoms = [j]
+            while blossomparent[iblossoms[-1]] is not None:
+                iblossoms.append(blossomparent[iblossoms[-1]])
+            while blossomparent[jblossoms[-1]] is not None:
+                jblossoms.append(blossomparent[jblossoms[-1]])
+            iblossoms.reverse()
+            jblossoms.reverse()
+            for bi, bj in zip(iblossoms, jblossoms):
+                if bi != bj:
+                    break
+                s += 2 * blossomdual[bi]
+            assert s >= 0
+            if mate.get(i) == j or mate.get(j) == i:
+                assert mate[i] == j and mate[j] == i
+                assert s == 0
+        # 2. all single vertices have zero dual value;
+        for v in gnodes:
+            assert (v in mate) or dualvar[v] + vdualoffset == 0
+        # 3. all blossoms with positive dual value are full.
+        for b in blossomdual:
+            if blossomdual[b] > 0:
+                assert len(b.edges) % 2 == 1
+                for i, j in b.edges[1::2]:
+                    assert mate[i] == j and mate[j] == i
+        # Ok.
+
+    # Main loop: continue until no further improvement is possible.
+    while 1:
+        # Each iteration of this loop is a "stage".
+        # A stage finds an augmenting path and uses that to improve
+        # the matching.
+
+        # Remove labels from top-level blossoms/vertices.
+        label.clear()
+        labeledge.clear()
+
+        # Forget all about least-slack edges.
+        bestedge.clear()
+        for b in blossomdual:
+            b.mybestedges = None
+
+        # Loss of labeling means that we can not be sure that currently
+        # allowable edges remain allowable throughout this stage.
+        allowedge.clear()
+
+        # Make queue empty.
+        queue[:] = []
+
+        # Label single blossoms/vertices with S and put them in the queue.
+        for v in gnodes:
+            if (v not in mate) and label.get(inblossom[v]) is None:
+                assignLabel(v, 1, None)
+
+        # Loop until we succeed in augmenting the matching.
+        augmented = 0
+        while 1:
+            # Each iteration of this loop is a "substage".
+            # A substage tries to find an augmenting path;
+            # if found, the path is used to improve the matching and
+            # the stage ends. If there is no augmenting path, the
+            # primal-dual method is used to pump some slack out of
+            # the dual variables.
+
+            # Continue labeling until all vertices which are reachable
+            # through an alternating path have got a label.
+            while queue and not augmented:
+                # Take an S vertex from the queue.
+                v = queue.pop()
+                assert label[inblossom[v]] == 1
+
+                # Scan its neighbors:
+                for w in G.neighbors(v):
+                    if w == v:
+                        continue  # ignore self-loops
+                    # w is a neighbor to v
+                    bv = inblossom[v]
+                    bw = inblossom[w]
+                    if bv == bw:
+                        # this edge is internal to a blossom; ignore it
+                        continue
+                    if (v, w) not in allowedge:
+                        kslack = slack(v, w)
+                        if kslack <= 0:
+                            # edge k has zero slack => it is allowable
+                            allowedge[(v, w)] = allowedge[(w, v)] = True
+                    if (v, w) in allowedge:
+                        if label.get(bw) is None:
+                            # (C1) w is a free vertex;
+                            # label w with T and label its mate with S (R12).
+                            assignLabel(w, 2, v)
+                        elif label.get(bw) == 1:
+                            # (C2) w is an S-vertex (not in the same blossom);
+                            # follow back-links to discover either an
+                            # augmenting path or a new blossom.
+                            base = scanBlossom(v, w)
+                            if base is not NoNode:
+                                # Found a new blossom; add it to the blossom
+                                # bookkeeping and turn it into an S-blossom.
+                                addBlossom(base, v, w)
+                            else:
+                                # Found an augmenting path; augment the
+                                # matching and end this stage.
+                                augmentMatching(v, w)
+                                augmented = 1
+                                break
+                        elif label.get(w) is None:
+                            # w is inside a T-blossom, but w itself has not
+                            # yet been reached from outside the blossom;
+                            # mark it as reached (we need this to relabel
+                            # during T-blossom expansion).
+                            assert label[bw] == 2
+                            label[w] = 2
+                            labeledge[w] = (v, w)
+                    elif label.get(bw) == 1:
+                        # keep track of the least-slack non-allowable edge to
+                        # a different S-blossom.
+                        if bestedge.get(bv) is None or kslack < slack(*bestedge[bv]):
+                            bestedge[bv] = (v, w)
+                    elif label.get(w) is None:
+                        # w is a free vertex (or an unreached vertex inside
+                        # a T-blossom) but we can not reach it yet;
+                        # keep track of the least-slack edge that reaches w.
+                        if bestedge.get(w) is None or kslack < slack(*bestedge[w]):
+                            bestedge[w] = (v, w)
+
+            if augmented:
+                break
+
+            # There is no augmenting path under these constraints;
+            # compute delta and reduce slack in the optimization problem.
+            # (Note that our vertex dual variables, edge slacks and delta's
+            # are pre-multiplied by two.)
+            deltatype = -1
+            delta = deltaedge = deltablossom = None
+
+            # Compute delta1: the minimum value of any vertex dual.
+            if not maxcardinality:
+                deltatype = 1
+                delta = min(dualvar.values())
+
+            # Compute delta2: the minimum slack on any edge between
+            # an S-vertex and a free vertex.
+            for v in G.nodes():
+                if label.get(inblossom[v]) is None and bestedge.get(v) is not None:
+                    d = slack(*bestedge[v])
+                    if deltatype == -1 or d < delta:
+                        delta = d
+                        deltatype = 2
+                        deltaedge = bestedge[v]
+
+            # Compute delta3: half the minimum slack on any edge between
+            # a pair of S-blossoms.
+            for b in blossomparent:
+                if (
+                    blossomparent[b] is None
+                    and label.get(b) == 1
+                    and bestedge.get(b) is not None
+                ):
+                    kslack = slack(*bestedge[b])
+                    if allinteger:
+                        assert (kslack % 2) == 0
+                        d = kslack // 2
+                    else:
+                        d = kslack / 2.0
+                    if deltatype == -1 or d < delta:
+                        delta = d
+                        deltatype = 3
+                        deltaedge = bestedge[b]
+
+            # Compute delta4: minimum z variable of any T-blossom.
+            for b in blossomdual:
+                if (
+                    blossomparent[b] is None
+                    and label.get(b) == 2
+                    and (deltatype == -1 or blossomdual[b] < delta)
+                ):
+                    delta = blossomdual[b]
+                    deltatype = 4
+                    deltablossom = b
+
+            if deltatype == -1:
+                # No further improvement possible; max-cardinality optimum
+                # reached. Do a final delta update to make the optimum
+                # verifiable.
+                assert maxcardinality
+                deltatype = 1
+                delta = max(0, min(dualvar.values()))
+
+            # Update dual variables according to delta.
+            for v in gnodes:
+                if label.get(inblossom[v]) == 1:
+                    # S-vertex: 2*u = 2*u - 2*delta
+                    dualvar[v] -= delta
+                elif label.get(inblossom[v]) == 2:
+                    # T-vertex: 2*u = 2*u + 2*delta
+                    dualvar[v] += delta
+            for b in blossomdual:
+                if blossomparent[b] is None:
+                    if label.get(b) == 1:
+                        # top-level S-blossom: z = z + 2*delta
+                        blossomdual[b] += delta
+                    elif label.get(b) == 2:
+                        # top-level T-blossom: z = z - 2*delta
+                        blossomdual[b] -= delta
+
+            # Take action at the point where minimum delta occurred.
+            if deltatype == 1:
+                # No further improvement possible; optimum reached.
+                break
+            elif deltatype == 2:
+                # Use the least-slack edge to continue the search.
+                (v, w) = deltaedge
+                assert label[inblossom[v]] == 1
+                allowedge[(v, w)] = allowedge[(w, v)] = True
+                queue.append(v)
+            elif deltatype == 3:
+                # Use the least-slack edge to continue the search.
+                (v, w) = deltaedge
+                allowedge[(v, w)] = allowedge[(w, v)] = True
+                assert label[inblossom[v]] == 1
+                queue.append(v)
+            elif deltatype == 4:
+                # Expand the least-z blossom.
+                expandBlossom(deltablossom, False)
+
+            # End of a this substage.
+
+        # Paranoia check that the matching is symmetric.
+        for v in mate:
+            assert mate[mate[v]] == v
+
+        # Stop when no more augmenting path can be found.
+        if not augmented:
+            break
+
+        # End of a stage; expand all S-blossoms which have zero dual.
+        for b in list(blossomdual.keys()):
+            if b not in blossomdual:
+                continue  # already expanded
+            if blossomparent[b] is None and label.get(b) == 1 and blossomdual[b] == 0:
+                expandBlossom(b, True)
+
+    # Verify that we reached the optimum solution (only for integer weights).
+    if allinteger:
+        verifyOptimum()
+
+    return matching_dict_to_set(mate)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/mis.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/mis.py
new file mode 100644
index 0000000000000000000000000000000000000000..0652ac4acec51c86edef8e8ed963d634c40f12ad
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/mis.py
@@ -0,0 +1,78 @@
+"""
+Algorithm to find a maximal (not maximum) independent set.
+
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for, py_random_state
+
+__all__ = ["maximal_independent_set"]
+
+
+@not_implemented_for("directed")
+@py_random_state(2)
+@nx._dispatchable
+def maximal_independent_set(G, nodes=None, seed=None):
+    """Returns a random maximal independent set guaranteed to contain
+    a given set of nodes.
+
+    An independent set is a set of nodes such that the subgraph
+    of G induced by these nodes contains no edges. A maximal
+    independent set is an independent set such that it is not possible
+    to add a new node and still get an independent set.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    nodes : list or iterable
+       Nodes that must be part of the independent set. This set of nodes
+       must be independent.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    indep_nodes : list
+       List of nodes that are part of a maximal independent set.
+
+    Raises
+    ------
+    NetworkXUnfeasible
+       If the nodes in the provided list are not part of the graph or
+       do not form an independent set, an exception is raised.
+
+    NetworkXNotImplemented
+        If `G` is directed.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.maximal_independent_set(G)  # doctest: +SKIP
+    [4, 0, 2]
+    >>> nx.maximal_independent_set(G, [1])  # doctest: +SKIP
+    [1, 3]
+
+    Notes
+    -----
+    This algorithm does not solve the maximum independent set problem.
+
+    """
+    if not nodes:
+        nodes = {seed.choice(list(G))}
+    else:
+        nodes = set(nodes)
+    if not nodes.issubset(G):
+        raise nx.NetworkXUnfeasible(f"{nodes} is not a subset of the nodes of G")
+    neighbors = set.union(*[set(G.adj[v]) for v in nodes])
+    if set.intersection(neighbors, nodes):
+        raise nx.NetworkXUnfeasible(f"{nodes} is not an independent set of G")
+    indep_nodes = list(nodes)
+    available_nodes = set(G.nodes()).difference(neighbors.union(nodes))
+    while available_nodes:
+        node = seed.choice(list(available_nodes))
+        indep_nodes.append(node)
+        available_nodes.difference_update(list(G.adj[node]) + [node])
+    return indep_nodes
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/moral.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/moral.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2acf80f6c3715da57dfc92e4c2d2daf986b3c29
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/moral.py
@@ -0,0 +1,59 @@
+r"""Function for computing the moral graph of a directed graph."""
+
+import itertools
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["moral_graph"]
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable(returns_graph=True)
+def moral_graph(G):
+    r"""Return the Moral Graph
+
+    Returns the moralized graph of a given directed graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Directed graph
+
+    Returns
+    -------
+    H : NetworkX graph
+        The undirected moralized graph of G
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If `G` is undirected.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 2), (2, 3), (2, 5), (3, 4), (4, 3)])
+    >>> G_moral = nx.moral_graph(G)
+    >>> G_moral.edges()
+    EdgeView([(1, 2), (2, 3), (2, 5), (2, 4), (3, 4)])
+
+    Notes
+    -----
+    A moral graph is an undirected graph H = (V, E) generated from a
+    directed Graph, where if a node has more than one parent node, edges
+    between these parent nodes are inserted and all directed edges become
+    undirected.
+
+    https://en.wikipedia.org/wiki/Moral_graph
+
+    References
+    ----------
+    .. [1] Wray L. Buntine. 1995. Chain graphs for learning.
+           In Proceedings of the Eleventh conference on Uncertainty
+           in artificial intelligence (UAI'95)
+    """
+    H = G.to_undirected()
+    for preds in G.pred.values():
+        predecessors_combinations = itertools.combinations(preds, r=2)
+        H.add_edges_from(predecessors_combinations)
+    return H
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/node_classification.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/node_classification.py
new file mode 100644
index 0000000000000000000000000000000000000000..b69a6c970dc80496be9aab9e9712bcd0f3ded5ca
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/node_classification.py
@@ -0,0 +1,219 @@
+"""This module provides the functions for node classification problem.
+
+The functions in this module are not imported
+into the top level `networkx` namespace.
+You can access these functions by importing
+the `networkx.algorithms.node_classification` modules,
+then accessing the functions as attributes of `node_classification`.
+For example:
+
+  >>> from networkx.algorithms import node_classification
+  >>> G = nx.path_graph(4)
+  >>> G.edges()
+  EdgeView([(0, 1), (1, 2), (2, 3)])
+  >>> G.nodes[0]["label"] = "A"
+  >>> G.nodes[3]["label"] = "B"
+  >>> node_classification.harmonic_function(G)
+  ['A', 'A', 'B', 'B']
+
+References
+----------
+Zhu, X., Ghahramani, Z., & Lafferty, J. (2003, August).
+Semi-supervised learning using gaussian fields and harmonic functions.
+In ICML (Vol. 3, pp. 912-919).
+"""
+
+import networkx as nx
+
+__all__ = ["harmonic_function", "local_and_global_consistency"]
+
+
+@nx.utils.not_implemented_for("directed")
+@nx._dispatchable(node_attrs="label_name")
+def harmonic_function(G, max_iter=30, label_name="label"):
+    """Node classification by Harmonic function
+
+    Function for computing Harmonic function algorithm by Zhu et al.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    max_iter : int
+        maximum number of iterations allowed
+    label_name : string
+        name of target labels to predict
+
+    Returns
+    -------
+    predicted : list
+        List of length ``len(G)`` with the predicted labels for each node.
+
+    Raises
+    ------
+    NetworkXError
+        If no nodes in `G` have attribute `label_name`.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import node_classification
+    >>> G = nx.path_graph(4)
+    >>> G.nodes[0]["label"] = "A"
+    >>> G.nodes[3]["label"] = "B"
+    >>> G.nodes(data=True)
+    NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}})
+    >>> G.edges()
+    EdgeView([(0, 1), (1, 2), (2, 3)])
+    >>> predicted = node_classification.harmonic_function(G)
+    >>> predicted
+    ['A', 'A', 'B', 'B']
+
+    References
+    ----------
+    Zhu, X., Ghahramani, Z., & Lafferty, J. (2003, August).
+    Semi-supervised learning using gaussian fields and harmonic functions.
+    In ICML (Vol. 3, pp. 912-919).
+    """
+    import numpy as np
+    import scipy as sp
+
+    X = nx.to_scipy_sparse_array(G)  # adjacency matrix
+    labels, label_dict = _get_label_info(G, label_name)
+
+    if labels.shape[0] == 0:
+        raise nx.NetworkXError(
+            f"No node on the input graph is labeled by '{label_name}'."
+        )
+
+    n_samples = X.shape[0]
+    n_classes = label_dict.shape[0]
+    F = np.zeros((n_samples, n_classes))
+
+    # Build propagation matrix
+    degrees = X.sum(axis=0)
+    degrees[degrees == 0] = 1  # Avoid division by 0
+    # TODO: csr_array
+    D = sp.sparse.csr_array(sp.sparse.diags((1.0 / degrees), offsets=0))
+    P = (D @ X).tolil()
+    P[labels[:, 0]] = 0  # labels[:, 0] indicates IDs of labeled nodes
+    # Build base matrix
+    B = np.zeros((n_samples, n_classes))
+    B[labels[:, 0], labels[:, 1]] = 1
+
+    for _ in range(max_iter):
+        F = (P @ F) + B
+
+    return label_dict[np.argmax(F, axis=1)].tolist()
+
+
+@nx.utils.not_implemented_for("directed")
+@nx._dispatchable(node_attrs="label_name")
+def local_and_global_consistency(G, alpha=0.99, max_iter=30, label_name="label"):
+    """Node classification by Local and Global Consistency
+
+    Function for computing Local and global consistency algorithm by Zhou et al.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    alpha : float
+        Clamping factor
+    max_iter : int
+        Maximum number of iterations allowed
+    label_name : string
+        Name of target labels to predict
+
+    Returns
+    -------
+    predicted : list
+        List of length ``len(G)`` with the predicted labels for each node.
+
+    Raises
+    ------
+    NetworkXError
+        If no nodes in `G` have attribute `label_name`.
+
+    Examples
+    --------
+    >>> from networkx.algorithms import node_classification
+    >>> G = nx.path_graph(4)
+    >>> G.nodes[0]["label"] = "A"
+    >>> G.nodes[3]["label"] = "B"
+    >>> G.nodes(data=True)
+    NodeDataView({0: {'label': 'A'}, 1: {}, 2: {}, 3: {'label': 'B'}})
+    >>> G.edges()
+    EdgeView([(0, 1), (1, 2), (2, 3)])
+    >>> predicted = node_classification.local_and_global_consistency(G)
+    >>> predicted
+    ['A', 'A', 'B', 'B']
+
+    References
+    ----------
+    Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2004).
+    Learning with local and global consistency.
+    Advances in neural information processing systems, 16(16), 321-328.
+    """
+    import numpy as np
+    import scipy as sp
+
+    X = nx.to_scipy_sparse_array(G)  # adjacency matrix
+    labels, label_dict = _get_label_info(G, label_name)
+
+    if labels.shape[0] == 0:
+        raise nx.NetworkXError(
+            f"No node on the input graph is labeled by '{label_name}'."
+        )
+
+    n_samples = X.shape[0]
+    n_classes = label_dict.shape[0]
+    F = np.zeros((n_samples, n_classes))
+
+    # Build propagation matrix
+    degrees = X.sum(axis=0)
+    degrees[degrees == 0] = 1  # Avoid division by 0
+    # TODO: csr_array
+    D2 = np.sqrt(sp.sparse.csr_array(sp.sparse.diags((1.0 / degrees), offsets=0)))
+    P = alpha * ((D2 @ X) @ D2)
+    # Build base matrix
+    B = np.zeros((n_samples, n_classes))
+    B[labels[:, 0], labels[:, 1]] = 1 - alpha
+
+    for _ in range(max_iter):
+        F = (P @ F) + B
+
+    return label_dict[np.argmax(F, axis=1)].tolist()
+
+
+def _get_label_info(G, label_name):
+    """Get and return information of labels from the input graph
+
+    Parameters
+    ----------
+    G : Network X graph
+    label_name : string
+        Name of the target label
+
+    Returns
+    -------
+    labels : numpy array, shape = [n_labeled_samples, 2]
+        Array of pairs of labeled node ID and label ID
+    label_dict : numpy array, shape = [n_classes]
+        Array of labels
+        i-th element contains the label corresponding label ID `i`
+    """
+    import numpy as np
+
+    labels = []
+    label_to_id = {}
+    lid = 0
+    for i, n in enumerate(G.nodes(data=True)):
+        if label_name in n[1]:
+            label = n[1][label_name]
+            if label not in label_to_id:
+                label_to_id[label] = lid
+                lid += 1
+            labels.append([i, label_to_id[label]])
+    labels = np.array(labels)
+    label_dict = np.array(
+        [label for label, _ in sorted(label_to_id.items(), key=lambda x: x[1])]
+    )
+    return (labels, label_dict)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/non_randomness.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/non_randomness.py
new file mode 100644
index 0000000000000000000000000000000000000000..137991157747e3720f026481ff1b0b3b5e8af1cf
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/non_randomness.py
@@ -0,0 +1,98 @@
+r"""Computation of graph non-randomness"""
+
+import math
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["non_randomness"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(edge_attrs="weight")
+def non_randomness(G, k=None, weight="weight"):
+    """Compute the non-randomness of graph G.
+
+    The first returned value nr is the sum of non-randomness values of all
+    edges within the graph (where the non-randomness of an edge tends to be
+    small when the two nodes linked by that edge are from two different
+    communities).
+
+    The second computed value nr_rd is a relative measure that indicates
+    to what extent graph G is different from random graphs in terms
+    of probability. When it is close to 0, the graph tends to be more
+    likely generated by an Erdos Renyi model.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Graph must be symmetric, connected, and without self-loops.
+
+    k : int
+        The number of communities in G.
+        If k is not set, the function will use a default community
+        detection algorithm to set it.
+
+    weight : string or None, optional (default=None)
+        The name of an edge attribute that holds the numerical value used
+        as a weight. If None, then each edge has weight 1, i.e., the graph is
+        binary.
+
+    Returns
+    -------
+    non-randomness : (float, float) tuple
+        Non-randomness, Relative non-randomness w.r.t.
+        Erdos Renyi random graphs.
+
+    Raises
+    ------
+    NetworkXException
+        if the input graph is not connected.
+    NetworkXError
+        if the input graph contains self-loops or if graph has no edges.
+
+    Examples
+    --------
+    >>> G = nx.karate_club_graph()
+    >>> nr, nr_rd = nx.non_randomness(G, 2)
+    >>> nr, nr_rd = nx.non_randomness(G, 2, "weight")
+
+    Notes
+    -----
+    This computes Eq. (4.4) and (4.5) in Ref. [1]_.
+
+    If a weight field is passed, this algorithm will use the eigenvalues
+    of the weighted adjacency matrix to compute Eq. (4.4) and (4.5).
+
+    References
+    ----------
+    .. [1] Xiaowei Ying and Xintao Wu,
+           On Randomness Measures for Social Networks,
+           SIAM International Conference on Data Mining. 2009
+    """
+    import numpy as np
+
+    # corner case: graph has no edges
+    if nx.is_empty(G):
+        raise nx.NetworkXError("non_randomness not applicable to empty graphs")
+    if not nx.is_connected(G):
+        raise nx.NetworkXException("Non connected graph.")
+    if len(list(nx.selfloop_edges(G))) > 0:
+        raise nx.NetworkXError("Graph must not contain self-loops")
+
+    if k is None:
+        k = len(tuple(nx.community.label_propagation_communities(G)))
+
+    # eq. 4.4
+    eigenvalues = np.linalg.eigvals(nx.to_numpy_array(G, weight=weight))
+    nr = float(np.real(np.sum(eigenvalues[:k])))
+
+    n = G.number_of_nodes()
+    m = G.number_of_edges()
+    p = (2 * k * m) / (n * (n - k))
+
+    # eq. 4.5
+    nr_rd = (nr - ((n - 2 * k) * p + k)) / math.sqrt(2 * k * p * (1 - p))
+
+    return nr, nr_rd
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/planar_drawing.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/planar_drawing.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea25809b6aeb198b23b44fe9878775d11b7e109c
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/planar_drawing.py
@@ -0,0 +1,464 @@
+from collections import defaultdict
+
+import networkx as nx
+
+__all__ = ["combinatorial_embedding_to_pos"]
+
+
+def combinatorial_embedding_to_pos(embedding, fully_triangulate=False):
+    """Assigns every node a (x, y) position based on the given embedding
+
+    The algorithm iteratively inserts nodes of the input graph in a certain
+    order and rearranges previously inserted nodes so that the planar drawing
+    stays valid. This is done efficiently by only maintaining relative
+    positions during the node placements and calculating the absolute positions
+    at the end. For more information see [1]_.
+
+    Parameters
+    ----------
+    embedding : nx.PlanarEmbedding
+        This defines the order of the edges
+
+    fully_triangulate : bool
+        If set to True the algorithm adds edges to a copy of the input
+        embedding and makes it chordal.
+
+    Returns
+    -------
+    pos : dict
+        Maps each node to a tuple that defines the (x, y) position
+
+    References
+    ----------
+    .. [1] M. Chrobak and T.H. Payne:
+        A Linear-time Algorithm for Drawing a Planar Graph on a Grid 1989
+        http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.6677
+
+    """
+    if len(embedding.nodes()) < 4:
+        # Position the node in any triangle
+        default_positions = [(0, 0), (2, 0), (1, 1)]
+        pos = {}
+        for i, v in enumerate(embedding.nodes()):
+            pos[v] = default_positions[i]
+        return pos
+
+    embedding, outer_face = triangulate_embedding(embedding, fully_triangulate)
+
+    # The following dicts map a node to another node
+    # If a node is not in the key set it means that the node is not yet in G_k
+    # If a node maps to None then the corresponding subtree does not exist
+    left_t_child = {}
+    right_t_child = {}
+
+    # The following dicts map a node to an integer
+    delta_x = {}
+    y_coordinate = {}
+
+    node_list = get_canonical_ordering(embedding, outer_face)
+
+    # 1. Phase: Compute relative positions
+
+    # Initialization
+    v1, v2, v3 = node_list[0][0], node_list[1][0], node_list[2][0]
+
+    delta_x[v1] = 0
+    y_coordinate[v1] = 0
+    right_t_child[v1] = v3
+    left_t_child[v1] = None
+
+    delta_x[v2] = 1
+    y_coordinate[v2] = 0
+    right_t_child[v2] = None
+    left_t_child[v2] = None
+
+    delta_x[v3] = 1
+    y_coordinate[v3] = 1
+    right_t_child[v3] = v2
+    left_t_child[v3] = None
+
+    for k in range(3, len(node_list)):
+        vk, contour_nbrs = node_list[k]
+        wp = contour_nbrs[0]
+        wp1 = contour_nbrs[1]
+        wq = contour_nbrs[-1]
+        wq1 = contour_nbrs[-2]
+        adds_mult_tri = len(contour_nbrs) > 2
+
+        # Stretch gaps:
+        delta_x[wp1] += 1
+        delta_x[wq] += 1
+
+        delta_x_wp_wq = sum(delta_x[x] for x in contour_nbrs[1:])
+
+        # Adjust offsets
+        delta_x[vk] = (-y_coordinate[wp] + delta_x_wp_wq + y_coordinate[wq]) // 2
+        y_coordinate[vk] = (y_coordinate[wp] + delta_x_wp_wq + y_coordinate[wq]) // 2
+        delta_x[wq] = delta_x_wp_wq - delta_x[vk]
+        if adds_mult_tri:
+            delta_x[wp1] -= delta_x[vk]
+
+        # Install v_k:
+        right_t_child[wp] = vk
+        right_t_child[vk] = wq
+        if adds_mult_tri:
+            left_t_child[vk] = wp1
+            right_t_child[wq1] = None
+        else:
+            left_t_child[vk] = None
+
+    # 2. Phase: Set absolute positions
+    pos = {}
+    pos[v1] = (0, y_coordinate[v1])
+    remaining_nodes = [v1]
+    while remaining_nodes:
+        parent_node = remaining_nodes.pop()
+
+        # Calculate position for left child
+        set_position(
+            parent_node, left_t_child, remaining_nodes, delta_x, y_coordinate, pos
+        )
+        # Calculate position for right child
+        set_position(
+            parent_node, right_t_child, remaining_nodes, delta_x, y_coordinate, pos
+        )
+    return pos
+
+
+def set_position(parent, tree, remaining_nodes, delta_x, y_coordinate, pos):
+    """Helper method to calculate the absolute position of nodes."""
+    child = tree[parent]
+    parent_node_x = pos[parent][0]
+    if child is not None:
+        # Calculate pos of child
+        child_x = parent_node_x + delta_x[child]
+        pos[child] = (child_x, y_coordinate[child])
+        # Remember to calculate pos of its children
+        remaining_nodes.append(child)
+
+
+def get_canonical_ordering(embedding, outer_face):
+    """Returns a canonical ordering of the nodes
+
+    The canonical ordering of nodes (v1, ..., vn) must fulfill the following
+    conditions:
+    (See Lemma 1 in [2]_)
+
+    - For the subgraph G_k of the input graph induced by v1, ..., vk it holds:
+        - 2-connected
+        - internally triangulated
+        - the edge (v1, v2) is part of the outer face
+    - For a node v(k+1) the following holds:
+        - The node v(k+1) is part of the outer face of G_k
+        - It has at least two neighbors in G_k
+        - All neighbors of v(k+1) in G_k lie consecutively on the outer face of
+          G_k (excluding the edge (v1, v2)).
+
+    The algorithm used here starts with G_n (containing all nodes). It first
+    selects the nodes v1 and v2. And then tries to find the order of the other
+    nodes by checking which node can be removed in order to fulfill the
+    conditions mentioned above. This is done by calculating the number of
+    chords of nodes on the outer face. For more information see [1]_.
+
+    Parameters
+    ----------
+    embedding : nx.PlanarEmbedding
+        The embedding must be triangulated
+    outer_face : list
+        The nodes on the outer face of the graph
+
+    Returns
+    -------
+    ordering : list
+        A list of tuples `(vk, wp_wq)`. Here `vk` is the node at this position
+        in the canonical ordering. The element `wp_wq` is a list of nodes that
+        make up the outer face of G_k.
+
+    References
+    ----------
+    .. [1] Steven Chaplick.
+        Canonical Orders of Planar Graphs and (some of) Their Applications 2015
+        https://wuecampus2.uni-wuerzburg.de/moodle/pluginfile.php/545727/mod_resource/content/0/vg-ss15-vl03-canonical-orders-druckversion.pdf
+    .. [2] M. Chrobak and T.H. Payne:
+        A Linear-time Algorithm for Drawing a Planar Graph on a Grid 1989
+        http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.6677
+
+    """
+    v1 = outer_face[0]
+    v2 = outer_face[1]
+    chords = defaultdict(int)  # Maps nodes to the number of their chords
+    marked_nodes = set()
+    ready_to_pick = set(outer_face)
+
+    # Initialize outer_face_ccw_nbr (do not include v1 -> v2)
+    outer_face_ccw_nbr = {}
+    prev_nbr = v2
+    for idx in range(2, len(outer_face)):
+        outer_face_ccw_nbr[prev_nbr] = outer_face[idx]
+        prev_nbr = outer_face[idx]
+    outer_face_ccw_nbr[prev_nbr] = v1
+
+    # Initialize outer_face_cw_nbr (do not include v2 -> v1)
+    outer_face_cw_nbr = {}
+    prev_nbr = v1
+    for idx in range(len(outer_face) - 1, 0, -1):
+        outer_face_cw_nbr[prev_nbr] = outer_face[idx]
+        prev_nbr = outer_face[idx]
+
+    def is_outer_face_nbr(x, y):
+        if x not in outer_face_ccw_nbr:
+            return outer_face_cw_nbr[x] == y
+        if x not in outer_face_cw_nbr:
+            return outer_face_ccw_nbr[x] == y
+        return outer_face_ccw_nbr[x] == y or outer_face_cw_nbr[x] == y
+
+    def is_on_outer_face(x):
+        return x not in marked_nodes and (x in outer_face_ccw_nbr or x == v1)
+
+    # Initialize number of chords
+    for v in outer_face:
+        for nbr in embedding.neighbors_cw_order(v):
+            if is_on_outer_face(nbr) and not is_outer_face_nbr(v, nbr):
+                chords[v] += 1
+                ready_to_pick.discard(v)
+
+    # Initialize canonical_ordering
+    canonical_ordering = [None] * len(embedding.nodes())
+    canonical_ordering[0] = (v1, [])
+    canonical_ordering[1] = (v2, [])
+    ready_to_pick.discard(v1)
+    ready_to_pick.discard(v2)
+
+    for k in range(len(embedding.nodes()) - 1, 1, -1):
+        # 1. Pick v from ready_to_pick
+        v = ready_to_pick.pop()
+        marked_nodes.add(v)
+
+        # v has exactly two neighbors on the outer face (wp and wq)
+        wp = None
+        wq = None
+        # Iterate over neighbors of v to find wp and wq
+        nbr_iterator = iter(embedding.neighbors_cw_order(v))
+        while True:
+            nbr = next(nbr_iterator)
+            if nbr in marked_nodes:
+                # Only consider nodes that are not yet removed
+                continue
+            if is_on_outer_face(nbr):
+                # nbr is either wp or wq
+                if nbr == v1:
+                    wp = v1
+                elif nbr == v2:
+                    wq = v2
+                else:
+                    if outer_face_cw_nbr[nbr] == v:
+                        # nbr is wp
+                        wp = nbr
+                    else:
+                        # nbr is wq
+                        wq = nbr
+            if wp is not None and wq is not None:
+                # We don't need to iterate any further
+                break
+
+        # Obtain new nodes on outer face (neighbors of v from wp to wq)
+        wp_wq = [wp]
+        nbr = wp
+        while nbr != wq:
+            # Get next neighbor (clockwise on the outer face)
+            next_nbr = embedding[v][nbr]["ccw"]
+            wp_wq.append(next_nbr)
+            # Update outer face
+            outer_face_cw_nbr[nbr] = next_nbr
+            outer_face_ccw_nbr[next_nbr] = nbr
+            # Move to next neighbor of v
+            nbr = next_nbr
+
+        if len(wp_wq) == 2:
+            # There was a chord between wp and wq, decrease number of chords
+            chords[wp] -= 1
+            if chords[wp] == 0:
+                ready_to_pick.add(wp)
+            chords[wq] -= 1
+            if chords[wq] == 0:
+                ready_to_pick.add(wq)
+        else:
+            # Update all chords involving w_(p+1) to w_(q-1)
+            new_face_nodes = set(wp_wq[1:-1])
+            for w in new_face_nodes:
+                # If we do not find a chord for w later we can pick it next
+                ready_to_pick.add(w)
+                for nbr in embedding.neighbors_cw_order(w):
+                    if is_on_outer_face(nbr) and not is_outer_face_nbr(w, nbr):
+                        # There is a chord involving w
+                        chords[w] += 1
+                        ready_to_pick.discard(w)
+                        if nbr not in new_face_nodes:
+                            # Also increase chord for the neighbor
+                            # We only iterator over new_face_nodes
+                            chords[nbr] += 1
+                            ready_to_pick.discard(nbr)
+        # Set the canonical ordering node and the list of contour neighbors
+        canonical_ordering[k] = (v, wp_wq)
+
+    return canonical_ordering
+
+
+def triangulate_face(embedding, v1, v2):
+    """Triangulates the face given by half edge (v, w)
+
+    Parameters
+    ----------
+    embedding : nx.PlanarEmbedding
+    v1 : node
+        The half-edge (v1, v2) belongs to the face that gets triangulated
+    v2 : node
+    """
+    _, v3 = embedding.next_face_half_edge(v1, v2)
+    _, v4 = embedding.next_face_half_edge(v2, v3)
+    if v1 in (v2, v3):
+        # The component has less than 3 nodes
+        return
+    while v1 != v4:
+        # Add edge if not already present on other side
+        if embedding.has_edge(v1, v3):
+            # Cannot triangulate at this position
+            v1, v2, v3 = v2, v3, v4
+        else:
+            # Add edge for triangulation
+            embedding.add_half_edge(v1, v3, ccw=v2)
+            embedding.add_half_edge(v3, v1, cw=v2)
+            v1, v2, v3 = v1, v3, v4
+        # Get next node
+        _, v4 = embedding.next_face_half_edge(v2, v3)
+
+
+def triangulate_embedding(embedding, fully_triangulate=True):
+    """Triangulates the embedding.
+
+    Traverses faces of the embedding and adds edges to a copy of the
+    embedding to triangulate it.
+    The method also ensures that the resulting graph is 2-connected by adding
+    edges if the same vertex is contained twice on a path around a face.
+
+    Parameters
+    ----------
+    embedding : nx.PlanarEmbedding
+        The input graph must contain at least 3 nodes.
+
+    fully_triangulate : bool
+        If set to False the face with the most nodes is chooses as outer face.
+        This outer face does not get triangulated.
+
+    Returns
+    -------
+    (embedding, outer_face) : (nx.PlanarEmbedding, list) tuple
+        The element `embedding` is a new embedding containing all edges from
+        the input embedding and the additional edges to triangulate the graph.
+        The element `outer_face` is a list of nodes that lie on the outer face.
+        If the graph is fully triangulated these are three arbitrary connected
+        nodes.
+
+    """
+    if len(embedding.nodes) <= 1:
+        return embedding, list(embedding.nodes)
+    embedding = nx.PlanarEmbedding(embedding)
+
+    # Get a list with a node for each connected component
+    component_nodes = [next(iter(x)) for x in nx.connected_components(embedding)]
+
+    # 1. Make graph a single component (add edge between components)
+    for i in range(len(component_nodes) - 1):
+        v1 = component_nodes[i]
+        v2 = component_nodes[i + 1]
+        embedding.connect_components(v1, v2)
+
+    # 2. Calculate faces, ensure 2-connectedness and determine outer face
+    outer_face = []  # A face with the most number of nodes
+    face_list = []
+    edges_visited = set()  # Used to keep track of already visited faces
+    for v in embedding.nodes():
+        for w in embedding.neighbors_cw_order(v):
+            new_face = make_bi_connected(embedding, v, w, edges_visited)
+            if new_face:
+                # Found a new face
+                face_list.append(new_face)
+                if len(new_face) > len(outer_face):
+                    # The face is a candidate to be the outer face
+                    outer_face = new_face
+
+    # 3. Triangulate (internal) faces
+    for face in face_list:
+        if face is not outer_face or fully_triangulate:
+            # Triangulate this face
+            triangulate_face(embedding, face[0], face[1])
+
+    if fully_triangulate:
+        v1 = outer_face[0]
+        v2 = outer_face[1]
+        v3 = embedding[v2][v1]["ccw"]
+        outer_face = [v1, v2, v3]
+
+    return embedding, outer_face
+
+
+def make_bi_connected(embedding, starting_node, outgoing_node, edges_counted):
+    """Triangulate a face and make it 2-connected
+
+    This method also adds all edges on the face to `edges_counted`.
+
+    Parameters
+    ----------
+    embedding: nx.PlanarEmbedding
+        The embedding that defines the faces
+    starting_node : node
+        A node on the face
+    outgoing_node : node
+        A node such that the half edge (starting_node, outgoing_node) belongs
+        to the face
+    edges_counted: set
+        Set of all half-edges that belong to a face that have been visited
+
+    Returns
+    -------
+    face_nodes: list
+        A list of all nodes at the border of this face
+    """
+
+    # Check if the face has already been calculated
+    if (starting_node, outgoing_node) in edges_counted:
+        # This face was already counted
+        return []
+    edges_counted.add((starting_node, outgoing_node))
+
+    # Add all edges to edges_counted which have this face to their left
+    v1 = starting_node
+    v2 = outgoing_node
+    face_list = [starting_node]  # List of nodes around the face
+    face_set = set(face_list)  # Set for faster queries
+    _, v3 = embedding.next_face_half_edge(v1, v2)
+
+    # Move the nodes v1, v2, v3 around the face:
+    while v2 != starting_node or v3 != outgoing_node:
+        if v1 == v2:
+            raise nx.NetworkXException("Invalid half-edge")
+        # cycle is not completed yet
+        if v2 in face_set:
+            # v2 encountered twice: Add edge to ensure 2-connectedness
+            embedding.add_half_edge(v1, v3, ccw=v2)
+            embedding.add_half_edge(v3, v1, cw=v2)
+            edges_counted.add((v2, v3))
+            edges_counted.add((v3, v1))
+            v2 = v1
+        else:
+            face_set.add(v2)
+            face_list.append(v2)
+
+        # set next edge
+        v1 = v2
+        v2, v3 = embedding.next_face_half_edge(v2, v3)
+
+        # remember that this edge has been counted
+        edges_counted.add((v1, v2))
+
+    return face_list
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/planarity.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/planarity.py
new file mode 100644
index 0000000000000000000000000000000000000000..41778f89522bdab7204cacde095bffc5b7be186c
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/planarity.py
@@ -0,0 +1,1463 @@
+from collections import defaultdict
+from copy import deepcopy
+
+import networkx as nx
+
+__all__ = ["check_planarity", "is_planar", "PlanarEmbedding"]
+
+
+@nx._dispatchable
+def is_planar(G):
+    """Returns True if and only if `G` is planar.
+
+    A graph is *planar* iff it can be drawn in a plane without
+    any edge intersections.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    bool
+       Whether the graph is planar.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2)])
+    >>> nx.is_planar(G)
+    True
+    >>> nx.is_planar(nx.complete_graph(5))
+    False
+
+    See Also
+    --------
+    check_planarity :
+        Check if graph is planar *and* return a `PlanarEmbedding` instance if True.
+    """
+
+    return check_planarity(G, counterexample=False)[0]
+
+
+@nx._dispatchable(returns_graph=True)
+def check_planarity(G, counterexample=False):
+    """Check if a graph is planar and return a counterexample or an embedding.
+
+    A graph is planar iff it can be drawn in a plane without
+    any edge intersections.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+    counterexample : bool
+        A Kuratowski subgraph (to proof non planarity) is only returned if set
+        to true.
+
+    Returns
+    -------
+    (is_planar, certificate) : (bool, NetworkX graph) tuple
+        is_planar is true if the graph is planar.
+        If the graph is planar `certificate` is a PlanarEmbedding
+        otherwise it is a Kuratowski subgraph.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2)])
+    >>> is_planar, P = nx.check_planarity(G)
+    >>> print(is_planar)
+    True
+
+    When `G` is planar, a `PlanarEmbedding` instance is returned:
+
+    >>> P.get_data()
+    {0: [1, 2], 1: [0], 2: [0]}
+
+    Notes
+    -----
+    A (combinatorial) embedding consists of cyclic orderings of the incident
+    edges at each vertex. Given such an embedding there are multiple approaches
+    discussed in literature to drawing the graph (subject to various
+    constraints, e.g. integer coordinates), see e.g. [2].
+
+    The planarity check algorithm and extraction of the combinatorial embedding
+    is based on the Left-Right Planarity Test [1].
+
+    A counterexample is only generated if the corresponding parameter is set,
+    because the complexity of the counterexample generation is higher.
+
+    See also
+    --------
+    is_planar :
+        Check for planarity without creating a `PlanarEmbedding` or counterexample.
+
+    References
+    ----------
+    .. [1] Ulrik Brandes:
+        The Left-Right Planarity Test
+        2009
+        http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.9208
+    .. [2] Takao Nishizeki, Md Saidur Rahman:
+        Planar graph drawing
+        Lecture Notes Series on Computing: Volume 12
+        2004
+    """
+
+    planarity_state = LRPlanarity(G)
+    embedding = planarity_state.lr_planarity()
+    if embedding is None:
+        # graph is not planar
+        if counterexample:
+            return False, get_counterexample(G)
+        else:
+            return False, None
+    else:
+        # graph is planar
+        return True, embedding
+
+
+@nx._dispatchable(returns_graph=True)
+def check_planarity_recursive(G, counterexample=False):
+    """Recursive version of :meth:`check_planarity`."""
+    planarity_state = LRPlanarity(G)
+    embedding = planarity_state.lr_planarity_recursive()
+    if embedding is None:
+        # graph is not planar
+        if counterexample:
+            return False, get_counterexample_recursive(G)
+        else:
+            return False, None
+    else:
+        # graph is planar
+        return True, embedding
+
+
+@nx._dispatchable(returns_graph=True)
+def get_counterexample(G):
+    """Obtains a Kuratowski subgraph.
+
+    Raises nx.NetworkXException if G is planar.
+
+    The function removes edges such that the graph is still not planar.
+    At some point the removal of any edge would make the graph planar.
+    This subgraph must be a Kuratowski subgraph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    subgraph : NetworkX graph
+        A Kuratowski subgraph that proves that G is not planar.
+
+    """
+    # copy graph
+    G = nx.Graph(G)
+
+    if check_planarity(G)[0]:
+        raise nx.NetworkXException("G is planar - no counter example.")
+
+    # find Kuratowski subgraph
+    subgraph = nx.Graph()
+    for u in G:
+        nbrs = list(G[u])
+        for v in nbrs:
+            G.remove_edge(u, v)
+            if check_planarity(G)[0]:
+                G.add_edge(u, v)
+                subgraph.add_edge(u, v)
+
+    return subgraph
+
+
+@nx._dispatchable(returns_graph=True)
+def get_counterexample_recursive(G):
+    """Recursive version of :meth:`get_counterexample`."""
+
+    # copy graph
+    G = nx.Graph(G)
+
+    if check_planarity_recursive(G)[0]:
+        raise nx.NetworkXException("G is planar - no counter example.")
+
+    # find Kuratowski subgraph
+    subgraph = nx.Graph()
+    for u in G:
+        nbrs = list(G[u])
+        for v in nbrs:
+            G.remove_edge(u, v)
+            if check_planarity_recursive(G)[0]:
+                G.add_edge(u, v)
+                subgraph.add_edge(u, v)
+
+    return subgraph
+
+
+class Interval:
+    """Represents a set of return edges.
+
+    All return edges in an interval induce a same constraint on the contained
+    edges, which means that all edges must either have a left orientation or
+    all edges must have a right orientation.
+    """
+
+    def __init__(self, low=None, high=None):
+        self.low = low
+        self.high = high
+
+    def empty(self):
+        """Check if the interval is empty"""
+        return self.low is None and self.high is None
+
+    def copy(self):
+        """Returns a copy of this interval"""
+        return Interval(self.low, self.high)
+
+    def conflicting(self, b, planarity_state):
+        """Returns True if interval I conflicts with edge b"""
+        return (
+            not self.empty()
+            and planarity_state.lowpt[self.high] > planarity_state.lowpt[b]
+        )
+
+
+class ConflictPair:
+    """Represents a different constraint between two intervals.
+
+    The edges in the left interval must have a different orientation than
+    the one in the right interval.
+    """
+
+    def __init__(self, left=Interval(), right=Interval()):
+        self.left = left
+        self.right = right
+
+    def swap(self):
+        """Swap left and right intervals"""
+        temp = self.left
+        self.left = self.right
+        self.right = temp
+
+    def lowest(self, planarity_state):
+        """Returns the lowest lowpoint of a conflict pair"""
+        if self.left.empty():
+            return planarity_state.lowpt[self.right.low]
+        if self.right.empty():
+            return planarity_state.lowpt[self.left.low]
+        return min(
+            planarity_state.lowpt[self.left.low], planarity_state.lowpt[self.right.low]
+        )
+
+
+def top_of_stack(l):
+    """Returns the element on top of the stack."""
+    if not l:
+        return None
+    return l[-1]
+
+
+class LRPlanarity:
+    """A class to maintain the state during planarity check."""
+
+    __slots__ = [
+        "G",
+        "roots",
+        "height",
+        "lowpt",
+        "lowpt2",
+        "nesting_depth",
+        "parent_edge",
+        "DG",
+        "adjs",
+        "ordered_adjs",
+        "ref",
+        "side",
+        "S",
+        "stack_bottom",
+        "lowpt_edge",
+        "left_ref",
+        "right_ref",
+        "embedding",
+    ]
+
+    def __init__(self, G):
+        # copy G without adding self-loops
+        self.G = nx.Graph()
+        self.G.add_nodes_from(G.nodes)
+        for e in G.edges:
+            if e[0] != e[1]:
+                self.G.add_edge(e[0], e[1])
+
+        self.roots = []
+
+        # distance from tree root
+        self.height = defaultdict(lambda: None)
+
+        self.lowpt = {}  # height of lowest return point of an edge
+        self.lowpt2 = {}  # height of second lowest return point
+        self.nesting_depth = {}  # for nesting order
+
+        # None -> missing edge
+        self.parent_edge = defaultdict(lambda: None)
+
+        # oriented DFS graph
+        self.DG = nx.DiGraph()
+        self.DG.add_nodes_from(G.nodes)
+
+        self.adjs = {}
+        self.ordered_adjs = {}
+
+        self.ref = defaultdict(lambda: None)
+        self.side = defaultdict(lambda: 1)
+
+        # stack of conflict pairs
+        self.S = []
+        self.stack_bottom = {}
+        self.lowpt_edge = {}
+
+        self.left_ref = {}
+        self.right_ref = {}
+
+        self.embedding = PlanarEmbedding()
+
+    def lr_planarity(self):
+        """Execute the LR planarity test.
+
+        Returns
+        -------
+        embedding : dict
+            If the graph is planar an embedding is returned. Otherwise None.
+        """
+        if self.G.order() > 2 and self.G.size() > 3 * self.G.order() - 6:
+            # graph is not planar
+            return None
+
+        # make adjacency lists for dfs
+        for v in self.G:
+            self.adjs[v] = list(self.G[v])
+
+        # orientation of the graph by depth first search traversal
+        for v in self.G:
+            if self.height[v] is None:
+                self.height[v] = 0
+                self.roots.append(v)
+                self.dfs_orientation(v)
+
+        # Free no longer used variables
+        self.G = None
+        self.lowpt2 = None
+        self.adjs = None
+
+        # testing
+        for v in self.DG:  # sort the adjacency lists by nesting depth
+            # note: this sorting leads to non linear time
+            self.ordered_adjs[v] = sorted(
+                self.DG[v], key=lambda x: self.nesting_depth[(v, x)]
+            )
+        for v in self.roots:
+            if not self.dfs_testing(v):
+                return None
+
+        # Free no longer used variables
+        self.height = None
+        self.lowpt = None
+        self.S = None
+        self.stack_bottom = None
+        self.lowpt_edge = None
+
+        for e in self.DG.edges:
+            self.nesting_depth[e] = self.sign(e) * self.nesting_depth[e]
+
+        self.embedding.add_nodes_from(self.DG.nodes)
+        for v in self.DG:
+            # sort the adjacency lists again
+            self.ordered_adjs[v] = sorted(
+                self.DG[v], key=lambda x: self.nesting_depth[(v, x)]
+            )
+            # initialize the embedding
+            previous_node = None
+            for w in self.ordered_adjs[v]:
+                self.embedding.add_half_edge(v, w, ccw=previous_node)
+                previous_node = w
+
+        # Free no longer used variables
+        self.DG = None
+        self.nesting_depth = None
+        self.ref = None
+
+        # compute the complete embedding
+        for v in self.roots:
+            self.dfs_embedding(v)
+
+        # Free no longer used variables
+        self.roots = None
+        self.parent_edge = None
+        self.ordered_adjs = None
+        self.left_ref = None
+        self.right_ref = None
+        self.side = None
+
+        return self.embedding
+
+    def lr_planarity_recursive(self):
+        """Recursive version of :meth:`lr_planarity`."""
+        if self.G.order() > 2 and self.G.size() > 3 * self.G.order() - 6:
+            # graph is not planar
+            return None
+
+        # orientation of the graph by depth first search traversal
+        for v in self.G:
+            if self.height[v] is None:
+                self.height[v] = 0
+                self.roots.append(v)
+                self.dfs_orientation_recursive(v)
+
+        # Free no longer used variable
+        self.G = None
+
+        # testing
+        for v in self.DG:  # sort the adjacency lists by nesting depth
+            # note: this sorting leads to non linear time
+            self.ordered_adjs[v] = sorted(
+                self.DG[v], key=lambda x: self.nesting_depth[(v, x)]
+            )
+        for v in self.roots:
+            if not self.dfs_testing_recursive(v):
+                return None
+
+        for e in self.DG.edges:
+            self.nesting_depth[e] = self.sign_recursive(e) * self.nesting_depth[e]
+
+        self.embedding.add_nodes_from(self.DG.nodes)
+        for v in self.DG:
+            # sort the adjacency lists again
+            self.ordered_adjs[v] = sorted(
+                self.DG[v], key=lambda x: self.nesting_depth[(v, x)]
+            )
+            # initialize the embedding
+            previous_node = None
+            for w in self.ordered_adjs[v]:
+                self.embedding.add_half_edge(v, w, ccw=previous_node)
+                previous_node = w
+
+        # compute the complete embedding
+        for v in self.roots:
+            self.dfs_embedding_recursive(v)
+
+        return self.embedding
+
+    def dfs_orientation(self, v):
+        """Orient the graph by DFS, compute lowpoints and nesting order."""
+        # the recursion stack
+        dfs_stack = [v]
+        # index of next edge to handle in adjacency list of each node
+        ind = defaultdict(lambda: 0)
+        # boolean to indicate whether to skip the initial work for an edge
+        skip_init = defaultdict(lambda: False)
+
+        while dfs_stack:
+            v = dfs_stack.pop()
+            e = self.parent_edge[v]
+
+            for w in self.adjs[v][ind[v] :]:
+                vw = (v, w)
+
+                if not skip_init[vw]:
+                    if (v, w) in self.DG.edges or (w, v) in self.DG.edges:
+                        ind[v] += 1
+                        continue  # the edge was already oriented
+
+                    self.DG.add_edge(v, w)  # orient the edge
+
+                    self.lowpt[vw] = self.height[v]
+                    self.lowpt2[vw] = self.height[v]
+                    if self.height[w] is None:  # (v, w) is a tree edge
+                        self.parent_edge[w] = vw
+                        self.height[w] = self.height[v] + 1
+
+                        dfs_stack.append(v)  # revisit v after finishing w
+                        dfs_stack.append(w)  # visit w next
+                        skip_init[vw] = True  # don't redo this block
+                        break  # handle next node in dfs_stack (i.e. w)
+                    else:  # (v, w) is a back edge
+                        self.lowpt[vw] = self.height[w]
+
+                # determine nesting graph
+                self.nesting_depth[vw] = 2 * self.lowpt[vw]
+                if self.lowpt2[vw] < self.height[v]:  # chordal
+                    self.nesting_depth[vw] += 1
+
+                # update lowpoints of parent edge e
+                if e is not None:
+                    if self.lowpt[vw] < self.lowpt[e]:
+                        self.lowpt2[e] = min(self.lowpt[e], self.lowpt2[vw])
+                        self.lowpt[e] = self.lowpt[vw]
+                    elif self.lowpt[vw] > self.lowpt[e]:
+                        self.lowpt2[e] = min(self.lowpt2[e], self.lowpt[vw])
+                    else:
+                        self.lowpt2[e] = min(self.lowpt2[e], self.lowpt2[vw])
+
+                ind[v] += 1
+
+    def dfs_orientation_recursive(self, v):
+        """Recursive version of :meth:`dfs_orientation`."""
+        e = self.parent_edge[v]
+        for w in self.G[v]:
+            if (v, w) in self.DG.edges or (w, v) in self.DG.edges:
+                continue  # the edge was already oriented
+            vw = (v, w)
+            self.DG.add_edge(v, w)  # orient the edge
+
+            self.lowpt[vw] = self.height[v]
+            self.lowpt2[vw] = self.height[v]
+            if self.height[w] is None:  # (v, w) is a tree edge
+                self.parent_edge[w] = vw
+                self.height[w] = self.height[v] + 1
+                self.dfs_orientation_recursive(w)
+            else:  # (v, w) is a back edge
+                self.lowpt[vw] = self.height[w]
+
+            # determine nesting graph
+            self.nesting_depth[vw] = 2 * self.lowpt[vw]
+            if self.lowpt2[vw] < self.height[v]:  # chordal
+                self.nesting_depth[vw] += 1
+
+            # update lowpoints of parent edge e
+            if e is not None:
+                if self.lowpt[vw] < self.lowpt[e]:
+                    self.lowpt2[e] = min(self.lowpt[e], self.lowpt2[vw])
+                    self.lowpt[e] = self.lowpt[vw]
+                elif self.lowpt[vw] > self.lowpt[e]:
+                    self.lowpt2[e] = min(self.lowpt2[e], self.lowpt[vw])
+                else:
+                    self.lowpt2[e] = min(self.lowpt2[e], self.lowpt2[vw])
+
+    def dfs_testing(self, v):
+        """Test for LR partition."""
+        # the recursion stack
+        dfs_stack = [v]
+        # index of next edge to handle in adjacency list of each node
+        ind = defaultdict(lambda: 0)
+        # boolean to indicate whether to skip the initial work for an edge
+        skip_init = defaultdict(lambda: False)
+
+        while dfs_stack:
+            v = dfs_stack.pop()
+            e = self.parent_edge[v]
+            # to indicate whether to skip the final block after the for loop
+            skip_final = False
+
+            for w in self.ordered_adjs[v][ind[v] :]:
+                ei = (v, w)
+
+                if not skip_init[ei]:
+                    self.stack_bottom[ei] = top_of_stack(self.S)
+
+                    if ei == self.parent_edge[w]:  # tree edge
+                        dfs_stack.append(v)  # revisit v after finishing w
+                        dfs_stack.append(w)  # visit w next
+                        skip_init[ei] = True  # don't redo this block
+                        skip_final = True  # skip final work after breaking
+                        break  # handle next node in dfs_stack (i.e. w)
+                    else:  # back edge
+                        self.lowpt_edge[ei] = ei
+                        self.S.append(ConflictPair(right=Interval(ei, ei)))
+
+                # integrate new return edges
+                if self.lowpt[ei] < self.height[v]:
+                    if w == self.ordered_adjs[v][0]:  # e_i has return edge
+                        self.lowpt_edge[e] = self.lowpt_edge[ei]
+                    else:  # add constraints of e_i
+                        if not self.add_constraints(ei, e):
+                            # graph is not planar
+                            return False
+
+                ind[v] += 1
+
+            if not skip_final:
+                # remove back edges returning to parent
+                if e is not None:  # v isn't root
+                    self.remove_back_edges(e)
+
+        return True
+
+    def dfs_testing_recursive(self, v):
+        """Recursive version of :meth:`dfs_testing`."""
+        e = self.parent_edge[v]
+        for w in self.ordered_adjs[v]:
+            ei = (v, w)
+            self.stack_bottom[ei] = top_of_stack(self.S)
+            if ei == self.parent_edge[w]:  # tree edge
+                if not self.dfs_testing_recursive(w):
+                    return False
+            else:  # back edge
+                self.lowpt_edge[ei] = ei
+                self.S.append(ConflictPair(right=Interval(ei, ei)))
+
+            # integrate new return edges
+            if self.lowpt[ei] < self.height[v]:
+                if w == self.ordered_adjs[v][0]:  # e_i has return edge
+                    self.lowpt_edge[e] = self.lowpt_edge[ei]
+                else:  # add constraints of e_i
+                    if not self.add_constraints(ei, e):
+                        # graph is not planar
+                        return False
+
+        # remove back edges returning to parent
+        if e is not None:  # v isn't root
+            self.remove_back_edges(e)
+        return True
+
+    def add_constraints(self, ei, e):
+        P = ConflictPair()
+        # merge return edges of e_i into P.right
+        while True:
+            Q = self.S.pop()
+            if not Q.left.empty():
+                Q.swap()
+            if not Q.left.empty():  # not planar
+                return False
+            if self.lowpt[Q.right.low] > self.lowpt[e]:
+                # merge intervals
+                if P.right.empty():  # topmost interval
+                    P.right = Q.right.copy()
+                else:
+                    self.ref[P.right.low] = Q.right.high
+                P.right.low = Q.right.low
+            else:  # align
+                self.ref[Q.right.low] = self.lowpt_edge[e]
+            if top_of_stack(self.S) == self.stack_bottom[ei]:
+                break
+        # merge conflicting return edges of e_1,...,e_i-1 into P.L
+        while top_of_stack(self.S).left.conflicting(ei, self) or top_of_stack(
+            self.S
+        ).right.conflicting(ei, self):
+            Q = self.S.pop()
+            if Q.right.conflicting(ei, self):
+                Q.swap()
+            if Q.right.conflicting(ei, self):  # not planar
+                return False
+            # merge interval below lowpt(e_i) into P.R
+            self.ref[P.right.low] = Q.right.high
+            if Q.right.low is not None:
+                P.right.low = Q.right.low
+
+            if P.left.empty():  # topmost interval
+                P.left = Q.left.copy()
+            else:
+                self.ref[P.left.low] = Q.left.high
+            P.left.low = Q.left.low
+
+        if not (P.left.empty() and P.right.empty()):
+            self.S.append(P)
+        return True
+
+    def remove_back_edges(self, e):
+        u = e[0]
+        # trim back edges ending at parent u
+        # drop entire conflict pairs
+        while self.S and top_of_stack(self.S).lowest(self) == self.height[u]:
+            P = self.S.pop()
+            if P.left.low is not None:
+                self.side[P.left.low] = -1
+
+        if self.S:  # one more conflict pair to consider
+            P = self.S.pop()
+            # trim left interval
+            while P.left.high is not None and P.left.high[1] == u:
+                P.left.high = self.ref[P.left.high]
+            if P.left.high is None and P.left.low is not None:
+                # just emptied
+                self.ref[P.left.low] = P.right.low
+                self.side[P.left.low] = -1
+                P.left.low = None
+            # trim right interval
+            while P.right.high is not None and P.right.high[1] == u:
+                P.right.high = self.ref[P.right.high]
+            if P.right.high is None and P.right.low is not None:
+                # just emptied
+                self.ref[P.right.low] = P.left.low
+                self.side[P.right.low] = -1
+                P.right.low = None
+            self.S.append(P)
+
+        # side of e is side of a highest return edge
+        if self.lowpt[e] < self.height[u]:  # e has return edge
+            hl = top_of_stack(self.S).left.high
+            hr = top_of_stack(self.S).right.high
+
+            if hl is not None and (hr is None or self.lowpt[hl] > self.lowpt[hr]):
+                self.ref[e] = hl
+            else:
+                self.ref[e] = hr
+
+    def dfs_embedding(self, v):
+        """Completes the embedding."""
+        # the recursion stack
+        dfs_stack = [v]
+        # index of next edge to handle in adjacency list of each node
+        ind = defaultdict(lambda: 0)
+
+        while dfs_stack:
+            v = dfs_stack.pop()
+
+            for w in self.ordered_adjs[v][ind[v] :]:
+                ind[v] += 1
+                ei = (v, w)
+
+                if ei == self.parent_edge[w]:  # tree edge
+                    self.embedding.add_half_edge_first(w, v)
+                    self.left_ref[v] = w
+                    self.right_ref[v] = w
+
+                    dfs_stack.append(v)  # revisit v after finishing w
+                    dfs_stack.append(w)  # visit w next
+                    break  # handle next node in dfs_stack (i.e. w)
+                else:  # back edge
+                    if self.side[ei] == 1:
+                        self.embedding.add_half_edge(w, v, ccw=self.right_ref[w])
+                    else:
+                        self.embedding.add_half_edge(w, v, cw=self.left_ref[w])
+                        self.left_ref[w] = v
+
+    def dfs_embedding_recursive(self, v):
+        """Recursive version of :meth:`dfs_embedding`."""
+        for w in self.ordered_adjs[v]:
+            ei = (v, w)
+            if ei == self.parent_edge[w]:  # tree edge
+                self.embedding.add_half_edge_first(w, v)
+                self.left_ref[v] = w
+                self.right_ref[v] = w
+                self.dfs_embedding_recursive(w)
+            else:  # back edge
+                if self.side[ei] == 1:
+                    # place v directly after right_ref[w] in embed. list of w
+                    self.embedding.add_half_edge(w, v, ccw=self.right_ref[w])
+                else:
+                    # place v directly before left_ref[w] in embed. list of w
+                    self.embedding.add_half_edge(w, v, cw=self.left_ref[w])
+                    self.left_ref[w] = v
+
+    def sign(self, e):
+        """Resolve the relative side of an edge to the absolute side."""
+        # the recursion stack
+        dfs_stack = [e]
+        # dict to remember reference edges
+        old_ref = defaultdict(lambda: None)
+
+        while dfs_stack:
+            e = dfs_stack.pop()
+
+            if self.ref[e] is not None:
+                dfs_stack.append(e)  # revisit e after finishing self.ref[e]
+                dfs_stack.append(self.ref[e])  # visit self.ref[e] next
+                old_ref[e] = self.ref[e]  # remember value of self.ref[e]
+                self.ref[e] = None
+            else:
+                self.side[e] *= self.side[old_ref[e]]
+
+        return self.side[e]
+
+    def sign_recursive(self, e):
+        """Recursive version of :meth:`sign`."""
+        if self.ref[e] is not None:
+            self.side[e] = self.side[e] * self.sign_recursive(self.ref[e])
+            self.ref[e] = None
+        return self.side[e]
+
+
+class PlanarEmbedding(nx.DiGraph):
+    """Represents a planar graph with its planar embedding.
+
+    The planar embedding is given by a `combinatorial embedding
+    <https://en.wikipedia.org/wiki/Graph_embedding#Combinatorial_embedding>`_.
+
+    .. note:: `check_planarity` is the preferred way to check if a graph is planar.
+
+    **Neighbor ordering:**
+
+    In comparison to a usual graph structure, the embedding also stores the
+    order of all neighbors for every vertex.
+    The order of the neighbors can be given in clockwise (cw) direction or
+    counterclockwise (ccw) direction. This order is stored as edge attributes
+    in the underlying directed graph. For the edge (u, v) the edge attribute
+    'cw' is set to the neighbor of u that follows immediately after v in
+    clockwise direction.
+
+    In order for a PlanarEmbedding to be valid it must fulfill multiple
+    conditions. It is possible to check if these conditions are fulfilled with
+    the method :meth:`check_structure`.
+    The conditions are:
+
+    * Edges must go in both directions (because the edge attributes differ)
+    * Every edge must have a 'cw' and 'ccw' attribute which corresponds to a
+      correct planar embedding.
+
+    As long as a PlanarEmbedding is invalid only the following methods should
+    be called:
+
+    * :meth:`add_half_edge`
+    * :meth:`connect_components`
+
+    Even though the graph is a subclass of nx.DiGraph, it can still be used
+    for algorithms that require undirected graphs, because the method
+    :meth:`is_directed` is overridden. This is possible, because a valid
+    PlanarGraph must have edges in both directions.
+
+    **Half edges:**
+
+    In methods like `add_half_edge` the term "half-edge" is used, which is
+    a term that is used in `doubly connected edge lists
+    <https://en.wikipedia.org/wiki/Doubly_connected_edge_list>`_. It is used
+    to emphasize that the edge is only in one direction and there exists
+    another half-edge in the opposite direction.
+    While conventional edges always have two faces (including outer face) next
+    to them, it is possible to assign each half-edge *exactly one* face.
+    For a half-edge (u, v) that is oriented such that u is below v then the
+    face that belongs to (u, v) is to the right of this half-edge.
+
+    See Also
+    --------
+    is_planar :
+        Preferred way to check if an existing graph is planar.
+
+    check_planarity :
+        A convenient way to create a `PlanarEmbedding`. If not planar,
+        it returns a subgraph that shows this.
+
+    Examples
+    --------
+
+    Create an embedding of a star graph (compare `nx.star_graph(3)`):
+
+    >>> G = nx.PlanarEmbedding()
+    >>> G.add_half_edge(0, 1)
+    >>> G.add_half_edge(0, 2, ccw=1)
+    >>> G.add_half_edge(0, 3, ccw=2)
+    >>> G.add_half_edge(1, 0)
+    >>> G.add_half_edge(2, 0)
+    >>> G.add_half_edge(3, 0)
+
+    Alternatively the same embedding can also be defined in counterclockwise
+    orientation. The following results in exactly the same PlanarEmbedding:
+
+    >>> G = nx.PlanarEmbedding()
+    >>> G.add_half_edge(0, 1)
+    >>> G.add_half_edge(0, 3, cw=1)
+    >>> G.add_half_edge(0, 2, cw=3)
+    >>> G.add_half_edge(1, 0)
+    >>> G.add_half_edge(2, 0)
+    >>> G.add_half_edge(3, 0)
+
+    After creating a graph, it is possible to validate that the PlanarEmbedding
+    object is correct:
+
+    >>> G.check_structure()
+
+    """
+
+    def __init__(self, incoming_graph_data=None, **attr):
+        super().__init__(incoming_graph_data=incoming_graph_data, **attr)
+        self.add_edge = self.__forbidden
+        self.add_edges_from = self.__forbidden
+        self.add_weighted_edges_from = self.__forbidden
+
+    def __forbidden(self, *args, **kwargs):
+        """Forbidden operation
+
+        Any edge additions to a PlanarEmbedding should be done using
+        method `add_half_edge`.
+        """
+        raise NotImplementedError(
+            "Use `add_half_edge` method to add edges to a PlanarEmbedding."
+        )
+
+    def get_data(self):
+        """Converts the adjacency structure into a better readable structure.
+
+        Returns
+        -------
+        embedding : dict
+            A dict mapping all nodes to a list of neighbors sorted in
+            clockwise order.
+
+        See Also
+        --------
+        set_data
+
+        """
+        embedding = {}
+        for v in self:
+            embedding[v] = list(self.neighbors_cw_order(v))
+        return embedding
+
+    def set_data(self, data):
+        """Inserts edges according to given sorted neighbor list.
+
+        The input format is the same as the output format of get_data().
+
+        Parameters
+        ----------
+        data : dict
+            A dict mapping all nodes to a list of neighbors sorted in
+            clockwise order.
+
+        See Also
+        --------
+        get_data
+
+        """
+        for v in data:
+            ref = None
+            for w in reversed(data[v]):
+                self.add_half_edge(v, w, cw=ref)
+                ref = w
+
+    def remove_node(self, n):
+        """Remove node n.
+
+        Removes the node n and all adjacent edges, updating the
+        PlanarEmbedding to account for any resulting edge removal.
+        Attempting to remove a non-existent node will raise an exception.
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph
+
+        Raises
+        ------
+        NetworkXError
+           If n is not in the graph.
+
+        See Also
+        --------
+        remove_nodes_from
+
+        """
+        try:
+            for u in self._pred[n]:
+                succs_u = self._succ[u]
+                un_cw = succs_u[n]["cw"]
+                un_ccw = succs_u[n]["ccw"]
+                del succs_u[n]
+                del self._pred[u][n]
+                if n != un_cw:
+                    succs_u[un_cw]["ccw"] = un_ccw
+                    succs_u[un_ccw]["cw"] = un_cw
+            del self._node[n]
+            del self._succ[n]
+            del self._pred[n]
+        except KeyError as err:  # NetworkXError if n not in self
+            raise nx.NetworkXError(
+                f"The node {n} is not in the planar embedding."
+            ) from err
+        nx._clear_cache(self)
+
+    def remove_nodes_from(self, nodes):
+        """Remove multiple nodes.
+
+        Parameters
+        ----------
+        nodes : iterable container
+            A container of nodes (list, dict, set, etc.).  If a node
+            in the container is not in the graph it is silently ignored.
+
+        See Also
+        --------
+        remove_node
+
+        Notes
+        -----
+        When removing nodes from an iterator over the graph you are changing,
+        a `RuntimeError` will be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_nodes)`, and pass this
+        object to `G.remove_nodes_from`.
+
+        """
+        for n in nodes:
+            if n in self._node:
+                self.remove_node(n)
+            # silently skip non-existing nodes
+
+    def neighbors_cw_order(self, v):
+        """Generator for the neighbors of v in clockwise order.
+
+        Parameters
+        ----------
+        v : node
+
+        Yields
+        ------
+        node
+
+        """
+        succs = self._succ[v]
+        if not succs:
+            # v has no neighbors
+            return
+        start_node = next(reversed(succs))
+        yield start_node
+        current_node = succs[start_node]["cw"]
+        while start_node != current_node:
+            yield current_node
+            current_node = succs[current_node]["cw"]
+
+    def add_half_edge(self, start_node, end_node, *, cw=None, ccw=None):
+        """Adds a half-edge from `start_node` to `end_node`.
+
+        If the half-edge is not the first one out of `start_node`, a reference
+        node must be provided either in the clockwise (parameter `cw`) or in
+        the counterclockwise (parameter `ccw`) direction. Only one of `cw`/`ccw`
+        can be specified (or neither in the case of the first edge).
+        Note that specifying a reference in the clockwise (`cw`) direction means
+        inserting the new edge in the first counterclockwise position with
+        respect to the reference (and vice-versa).
+
+        Parameters
+        ----------
+        start_node : node
+            Start node of inserted edge.
+        end_node : node
+            End node of inserted edge.
+        cw, ccw: node
+            End node of reference edge.
+            Omit or pass `None` if adding the first out-half-edge of `start_node`.
+
+
+        Raises
+        ------
+        NetworkXException
+            If the `cw` or `ccw` node is not a successor of `start_node`.
+            If `start_node` has successors, but neither `cw` or `ccw` is provided.
+            If both `cw` and `ccw` are specified.
+
+        See Also
+        --------
+        connect_components
+        """
+
+        succs = self._succ.get(start_node)
+        if succs:
+            # there is already some edge out of start_node
+            leftmost_nbr = next(reversed(self._succ[start_node]))
+            if cw is not None:
+                if cw not in succs:
+                    raise nx.NetworkXError("Invalid clockwise reference node.")
+                if ccw is not None:
+                    raise nx.NetworkXError("Only one of cw/ccw can be specified.")
+                ref_ccw = succs[cw]["ccw"]
+                super().add_edge(start_node, end_node, cw=cw, ccw=ref_ccw)
+                succs[ref_ccw]["cw"] = end_node
+                succs[cw]["ccw"] = end_node
+                # when (cw == leftmost_nbr), the newly added neighbor is
+                # already at the end of dict self._succ[start_node] and
+                # takes the place of the former leftmost_nbr
+                move_leftmost_nbr_to_end = cw != leftmost_nbr
+            elif ccw is not None:
+                if ccw not in succs:
+                    raise nx.NetworkXError("Invalid counterclockwise reference node.")
+                ref_cw = succs[ccw]["cw"]
+                super().add_edge(start_node, end_node, cw=ref_cw, ccw=ccw)
+                succs[ref_cw]["ccw"] = end_node
+                succs[ccw]["cw"] = end_node
+                move_leftmost_nbr_to_end = True
+            else:
+                raise nx.NetworkXError(
+                    "Node already has out-half-edge(s), either cw or ccw reference node required."
+                )
+            if move_leftmost_nbr_to_end:
+                # LRPlanarity (via self.add_half_edge_first()) requires that
+                # we keep track of the leftmost neighbor, which we accomplish
+                # by keeping it as the last key in dict self._succ[start_node]
+                succs[leftmost_nbr] = succs.pop(leftmost_nbr)
+
+        else:
+            if cw is not None or ccw is not None:
+                raise nx.NetworkXError("Invalid reference node.")
+            # adding the first edge out of start_node
+            super().add_edge(start_node, end_node, ccw=end_node, cw=end_node)
+
+    def check_structure(self):
+        """Runs without exceptions if this object is valid.
+
+        Checks that the following properties are fulfilled:
+
+        * Edges go in both directions (because the edge attributes differ).
+        * Every edge has a 'cw' and 'ccw' attribute which corresponds to a
+          correct planar embedding.
+
+        Running this method verifies that the underlying Graph must be planar.
+
+        Raises
+        ------
+        NetworkXException
+            This exception is raised with a short explanation if the
+            PlanarEmbedding is invalid.
+        """
+        # Check fundamental structure
+        for v in self:
+            try:
+                sorted_nbrs = set(self.neighbors_cw_order(v))
+            except KeyError as err:
+                msg = f"Bad embedding. Missing orientation for a neighbor of {v}"
+                raise nx.NetworkXException(msg) from err
+
+            unsorted_nbrs = set(self[v])
+            if sorted_nbrs != unsorted_nbrs:
+                msg = "Bad embedding. Edge orientations not set correctly."
+                raise nx.NetworkXException(msg)
+            for w in self[v]:
+                # Check if opposite half-edge exists
+                if not self.has_edge(w, v):
+                    msg = "Bad embedding. Opposite half-edge is missing."
+                    raise nx.NetworkXException(msg)
+
+        # Check planarity
+        counted_half_edges = set()
+        for component in nx.connected_components(self):
+            if len(component) == 1:
+                # Don't need to check single node component
+                continue
+            num_nodes = len(component)
+            num_half_edges = 0
+            num_faces = 0
+            for v in component:
+                for w in self.neighbors_cw_order(v):
+                    num_half_edges += 1
+                    if (v, w) not in counted_half_edges:
+                        # We encountered a new face
+                        num_faces += 1
+                        # Mark all half-edges belonging to this face
+                        self.traverse_face(v, w, counted_half_edges)
+            num_edges = num_half_edges // 2  # num_half_edges is even
+            if num_nodes - num_edges + num_faces != 2:
+                # The result does not match Euler's formula
+                msg = "Bad embedding. The graph does not match Euler's formula"
+                raise nx.NetworkXException(msg)
+
+    def add_half_edge_ccw(self, start_node, end_node, reference_neighbor):
+        """Adds a half-edge from start_node to end_node.
+
+        The half-edge is added counter clockwise next to the existing half-edge
+        (start_node, reference_neighbor).
+
+        Parameters
+        ----------
+        start_node : node
+            Start node of inserted edge.
+        end_node : node
+            End node of inserted edge.
+        reference_neighbor: node
+            End node of reference edge.
+
+        Raises
+        ------
+        NetworkXException
+            If the reference_neighbor does not exist.
+
+        See Also
+        --------
+        add_half_edge
+        add_half_edge_cw
+        connect_components
+
+        """
+        self.add_half_edge(start_node, end_node, cw=reference_neighbor)
+
+    def add_half_edge_cw(self, start_node, end_node, reference_neighbor):
+        """Adds a half-edge from start_node to end_node.
+
+        The half-edge is added clockwise next to the existing half-edge
+        (start_node, reference_neighbor).
+
+        Parameters
+        ----------
+        start_node : node
+            Start node of inserted edge.
+        end_node : node
+            End node of inserted edge.
+        reference_neighbor: node
+            End node of reference edge.
+
+        Raises
+        ------
+        NetworkXException
+            If the reference_neighbor does not exist.
+
+        See Also
+        --------
+        add_half_edge
+        add_half_edge_ccw
+        connect_components
+        """
+        self.add_half_edge(start_node, end_node, ccw=reference_neighbor)
+
+    def remove_edge(self, u, v):
+        """Remove the edge between u and v.
+
+        Parameters
+        ----------
+        u, v : nodes
+        Remove the half-edges (u, v) and (v, u) and update the
+        edge ordering around the removed edge.
+
+        Raises
+        ------
+        NetworkXError
+        If there is not an edge between u and v.
+
+        See Also
+        --------
+        remove_edges_from : remove a collection of edges
+        """
+        try:
+            succs_u = self._succ[u]
+            succs_v = self._succ[v]
+            uv_cw = succs_u[v]["cw"]
+            uv_ccw = succs_u[v]["ccw"]
+            vu_cw = succs_v[u]["cw"]
+            vu_ccw = succs_v[u]["ccw"]
+            del succs_u[v]
+            del self._pred[v][u]
+            del succs_v[u]
+            del self._pred[u][v]
+            if v != uv_cw:
+                succs_u[uv_cw]["ccw"] = uv_ccw
+                succs_u[uv_ccw]["cw"] = uv_cw
+            if u != vu_cw:
+                succs_v[vu_cw]["ccw"] = vu_ccw
+                succs_v[vu_ccw]["cw"] = vu_cw
+        except KeyError as err:
+            raise nx.NetworkXError(
+                f"The edge {u}-{v} is not in the planar embedding."
+            ) from err
+        nx._clear_cache(self)
+
+    def remove_edges_from(self, ebunch):
+        """Remove all edges specified in ebunch.
+
+        Parameters
+        ----------
+        ebunch: list or container of edge tuples
+            Each pair of half-edges between the nodes given in the tuples
+            will be removed from the graph. The nodes can be passed as:
+
+                - 2-tuples (u, v) half-edges (u, v) and (v, u).
+                - 3-tuples (u, v, k) where k is ignored.
+
+        See Also
+        --------
+        remove_edge : remove a single edge
+
+        Notes
+        -----
+        Will fail silently if an edge in ebunch is not in the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> ebunch = [(1, 2), (2, 3)]
+        >>> G.remove_edges_from(ebunch)
+        """
+        for e in ebunch:
+            u, v = e[:2]  # ignore edge data
+            # assuming that the PlanarEmbedding is valid, if the half_edge
+            # (u, v) is in the graph, then so is half_edge (v, u)
+            if u in self._succ and v in self._succ[u]:
+                self.remove_edge(u, v)
+
+    def connect_components(self, v, w):
+        """Adds half-edges for (v, w) and (w, v) at some position.
+
+        This method should only be called if v and w are in different
+        components, or it might break the embedding.
+        This especially means that if `connect_components(v, w)`
+        is called it is not allowed to call `connect_components(w, v)`
+        afterwards. The neighbor orientations in both directions are
+        all set correctly after the first call.
+
+        Parameters
+        ----------
+        v : node
+        w : node
+
+        See Also
+        --------
+        add_half_edge
+        """
+        if v in self._succ and self._succ[v]:
+            ref = next(reversed(self._succ[v]))
+        else:
+            ref = None
+        self.add_half_edge(v, w, cw=ref)
+        if w in self._succ and self._succ[w]:
+            ref = next(reversed(self._succ[w]))
+        else:
+            ref = None
+        self.add_half_edge(w, v, cw=ref)
+
+    def add_half_edge_first(self, start_node, end_node):
+        """Add a half-edge and set end_node as start_node's leftmost neighbor.
+
+        The new edge is inserted counterclockwise with respect to the current
+        leftmost neighbor, if there is one.
+
+        Parameters
+        ----------
+        start_node : node
+        end_node : node
+
+        See Also
+        --------
+        add_half_edge
+        connect_components
+        """
+        succs = self._succ.get(start_node)
+        # the leftmost neighbor is the last entry in the
+        # self._succ[start_node] dict
+        leftmost_nbr = next(reversed(succs)) if succs else None
+        self.add_half_edge(start_node, end_node, cw=leftmost_nbr)
+
+    def next_face_half_edge(self, v, w):
+        """Returns the following half-edge left of a face.
+
+        Parameters
+        ----------
+        v : node
+        w : node
+
+        Returns
+        -------
+        half-edge : tuple
+        """
+        new_node = self[w][v]["ccw"]
+        return w, new_node
+
+    def traverse_face(self, v, w, mark_half_edges=None):
+        """Returns nodes on the face that belong to the half-edge (v, w).
+
+        The face that is traversed lies to the right of the half-edge (in an
+        orientation where v is below w).
+
+        Optionally it is possible to pass a set to which all encountered half
+        edges are added. Before calling this method, this set must not include
+        any half-edges that belong to the face.
+
+        Parameters
+        ----------
+        v : node
+            Start node of half-edge.
+        w : node
+            End node of half-edge.
+        mark_half_edges: set, optional
+            Set to which all encountered half-edges are added.
+
+        Returns
+        -------
+        face : list
+            A list of nodes that lie on this face.
+        """
+        if mark_half_edges is None:
+            mark_half_edges = set()
+
+        face_nodes = [v]
+        mark_half_edges.add((v, w))
+        prev_node = v
+        cur_node = w
+        # Last half-edge is (incoming_node, v)
+        incoming_node = self[v][w]["cw"]
+
+        while cur_node != v or prev_node != incoming_node:
+            face_nodes.append(cur_node)
+            prev_node, cur_node = self.next_face_half_edge(prev_node, cur_node)
+            if (prev_node, cur_node) in mark_half_edges:
+                raise nx.NetworkXException("Bad planar embedding. Impossible face.")
+            mark_half_edges.add((prev_node, cur_node))
+
+        return face_nodes
+
+    def is_directed(self):
+        """A valid PlanarEmbedding is undirected.
+
+        All reverse edges are contained, i.e. for every existing
+        half-edge (v, w) the half-edge in the opposite direction (w, v) is also
+        contained.
+        """
+        return False
+
+    def copy(self, as_view=False):
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self)
+        G = self.__class__()
+        G.graph.update(self.graph)
+        G.add_nodes_from((n, d.copy()) for n, d in self._node.items())
+        super(self.__class__, G).add_edges_from(
+            (u, v, datadict.copy())
+            for u, nbrs in self._adj.items()
+            for v, datadict in nbrs.items()
+        )
+        return G
+
+    def to_undirected(self, reciprocal=False, as_view=False):
+        """
+        Returns a non-embedding undirected representation of the graph.
+
+        This method strips the planar embedding information and provides
+        a simple undirected graph representation. While creating the undirected graph,
+        all edge attributes are retained except the ``"cw"`` and ``"ccw"`` attributes
+        which are removed from the edge data. Those attributes are specific to
+        the requirements of planar embeddings.
+
+        Parameters
+        ----------
+        reciprocal : bool (optional)
+            Not supported for PlanarEmbedding. This parameter raises an exception
+            if used. All valid embeddings include reciprocal half-edges by definition,
+            making this parameter unnecessary.
+        as_view : bool (optional, default=False)
+            Not supported for PlanarEmbedding. This parameter raises an exception
+            if used.
+
+        Returns
+        -------
+        G : Graph
+            An undirected graph with the same name and nodes as the PlanarEmbedding.
+            Edges are included with their data, except for the ``"cw"`` and ``"ccw"``
+            attributes, which are omitted.
+
+
+        Notes
+        -----
+        - If edges exist in both directions ``(u, v)`` and ``(v, u)`` in the PlanarEmbedding,
+          attributes for the resulting undirected edge will be combined, excluding ``"cw"``
+          and ``"ccw"``.
+        - A deep copy is made of the other edge attributes as well as the
+          node and graph attributes, ensuring independence of the resulting graph.
+        - Subclass-specific data structures used in the original graph may not transfer
+          to the undirected graph. The resulting graph will be of type ``nx.Graph``.
+        """
+
+        if reciprocal:
+            raise ValueError(
+                "'reciprocal=True' is not supported for PlanarEmbedding.\n"
+                "All valid embeddings include reciprocal half-edges by definition,\n"
+                "making this parameter unnecessary."
+            )
+
+        if as_view:
+            raise ValueError("'as_view=True' is not supported for PlanarEmbedding.")
+
+        graph_class = self.to_undirected_class()
+        G = graph_class()
+        G.graph.update(deepcopy(self.graph))
+        G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+        G.add_edges_from(
+            (u, v, {k: deepcopy(v) for k, v in d.items() if k not in {"cw", "ccw"}})
+            for u, nbrs in self._adj.items()
+            for v, d in nbrs.items()
+        )
+        return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/polynomials.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/polynomials.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ebc7554a7654c8961c9d8a8024d17210ccf44ca
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/polynomials.py
@@ -0,0 +1,306 @@
+"""Provides algorithms supporting the computation of graph polynomials.
+
+Graph polynomials are polynomial-valued graph invariants that encode a wide
+variety of structural information. Examples include the Tutte polynomial,
+chromatic polynomial, characteristic polynomial, and matching polynomial. An
+extensive treatment is provided in [1]_.
+
+For a simple example, the `~sympy.matrices.matrices.MatrixDeterminant.charpoly`
+method can be used to compute the characteristic polynomial from the adjacency
+matrix of a graph. Consider the complete graph ``K_4``:
+
+>>> import sympy
+>>> x = sympy.Symbol("x")
+>>> G = nx.complete_graph(4)
+>>> A = nx.to_numpy_array(G, dtype=int)
+>>> M = sympy.SparseMatrix(A)
+>>> M.charpoly(x).as_expr()
+x**4 - 6*x**2 - 8*x - 3
+
+
+.. [1] Y. Shi, M. Dehmer, X. Li, I. Gutman,
+   "Graph Polynomials"
+"""
+
+from collections import deque
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["tutte_polynomial", "chromatic_polynomial"]
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def tutte_polynomial(G):
+    r"""Returns the Tutte polynomial of `G`
+
+    This function computes the Tutte polynomial via an iterative version of
+    the deletion-contraction algorithm.
+
+    The Tutte polynomial `T_G(x, y)` is a fundamental graph polynomial invariant in
+    two variables. It encodes a wide array of information related to the
+    edge-connectivity of a graph; "Many problems about graphs can be reduced to
+    problems of finding and evaluating the Tutte polynomial at certain values" [1]_.
+    In fact, every deletion-contraction-expressible feature of a graph is a
+    specialization of the Tutte polynomial [2]_ (see Notes for examples).
+
+    There are several equivalent definitions; here are three:
+
+    Def 1 (rank-nullity expansion): For `G` an undirected graph, `n(G)` the
+    number of vertices of `G`, `E` the edge set of `G`, `V` the vertex set of
+    `G`, and `c(A)` the number of connected components of the graph with vertex
+    set `V` and edge set `A` [3]_:
+
+    .. math::
+
+        T_G(x, y) = \sum_{A \in E} (x-1)^{c(A) - c(E)} (y-1)^{c(A) + |A| - n(G)}
+
+    Def 2 (spanning tree expansion): Let `G` be an undirected graph, `T` a spanning
+    tree of `G`, and `E` the edge set of `G`. Let `E` have an arbitrary strict
+    linear order `L`. Let `B_e` be the unique minimal nonempty edge cut of
+    $E \setminus T \cup {e}$. An edge `e` is internally active with respect to
+    `T` and `L` if `e` is the least edge in `B_e` according to the linear order
+    `L`. The internal activity of `T` (denoted `i(T)`) is the number of edges
+    in $E \setminus T$ that are internally active with respect to `T` and `L`.
+    Let `P_e` be the unique path in $T \cup {e}$ whose source and target vertex
+    are the same. An edge `e` is externally active with respect to `T` and `L`
+    if `e` is the least edge in `P_e` according to the linear order `L`. The
+    external activity of `T` (denoted `e(T)`) is the number of edges in
+    $E \setminus T$ that are externally active with respect to `T` and `L`.
+    Then [4]_ [5]_:
+
+    .. math::
+
+        T_G(x, y) = \sum_{T \text{ a spanning tree of } G} x^{i(T)} y^{e(T)}
+
+    Def 3 (deletion-contraction recurrence): For `G` an undirected graph, `G-e`
+    the graph obtained from `G` by deleting edge `e`, `G/e` the graph obtained
+    from `G` by contracting edge `e`, `k(G)` the number of cut-edges of `G`,
+    and `l(G)` the number of self-loops of `G`:
+
+    .. math::
+        T_G(x, y) = \begin{cases}
+    	   x^{k(G)} y^{l(G)}, & \text{if all edges are cut-edges or self-loops} \\
+           T_{G-e}(x, y) + T_{G/e}(x, y), & \text{otherwise, for an arbitrary edge $e$ not a cut-edge or loop}
+        \end{cases}
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    instance of `sympy.core.add.Add`
+        A Sympy expression representing the Tutte polynomial for `G`.
+
+    Examples
+    --------
+    >>> C = nx.cycle_graph(5)
+    >>> nx.tutte_polynomial(C)
+    x**4 + x**3 + x**2 + x + y
+
+    >>> D = nx.diamond_graph()
+    >>> nx.tutte_polynomial(D)
+    x**3 + 2*x**2 + 2*x*y + x + y**2 + y
+
+    Notes
+    -----
+    Some specializations of the Tutte polynomial:
+
+    - `T_G(1, 1)` counts the number of spanning trees of `G`
+    - `T_G(1, 2)` counts the number of connected spanning subgraphs of `G`
+    - `T_G(2, 1)` counts the number of spanning forests in `G`
+    - `T_G(0, 2)` counts the number of strong orientations of `G`
+    - `T_G(2, 0)` counts the number of acyclic orientations of `G`
+
+    Edge contraction is defined and deletion-contraction is introduced in [6]_.
+    Combinatorial meaning of the coefficients is introduced in [7]_.
+    Universality, properties, and applications are discussed in [8]_.
+
+    Practically, up-front computation of the Tutte polynomial may be useful when
+    users wish to repeatedly calculate edge-connectivity-related information
+    about one or more graphs.
+
+    References
+    ----------
+    .. [1] M. Brandt,
+       "The Tutte Polynomial."
+       Talking About Combinatorial Objects Seminar, 2015
+       https://math.berkeley.edu/~brandtm/talks/tutte.pdf
+    .. [2] A. Björklund, T. Husfeldt, P. Kaski, M. Koivisto,
+       "Computing the Tutte polynomial in vertex-exponential time"
+       49th Annual IEEE Symposium on Foundations of Computer Science, 2008
+       https://ieeexplore.ieee.org/abstract/document/4691000
+    .. [3] Y. Shi, M. Dehmer, X. Li, I. Gutman,
+       "Graph Polynomials," p. 14
+    .. [4] Y. Shi, M. Dehmer, X. Li, I. Gutman,
+       "Graph Polynomials," p. 46
+    .. [5] A. Nešetril, J. Goodall,
+       "Graph invariants, homomorphisms, and the Tutte polynomial"
+       https://iuuk.mff.cuni.cz/~andrew/Tutte.pdf
+    .. [6] D. B. West,
+       "Introduction to Graph Theory," p. 84
+    .. [7] G. Coutinho,
+       "A brief introduction to the Tutte polynomial"
+       Structural Analysis of Complex Networks, 2011
+       https://homepages.dcc.ufmg.br/~gabriel/seminars/coutinho_tuttepolynomial_seminar.pdf
+    .. [8] J. A. Ellis-Monaghan, C. Merino,
+       "Graph polynomials and their applications I: The Tutte polynomial"
+       Structural Analysis of Complex Networks, 2011
+       https://arxiv.org/pdf/0803.3079.pdf
+    """
+    import sympy
+
+    x = sympy.Symbol("x")
+    y = sympy.Symbol("y")
+    stack = deque()
+    stack.append(nx.MultiGraph(G))
+
+    polynomial = 0
+    while stack:
+        G = stack.pop()
+        bridges = set(nx.bridges(G))
+
+        e = None
+        for i in G.edges:
+            if (i[0], i[1]) not in bridges and i[0] != i[1]:
+                e = i
+                break
+        if not e:
+            loops = list(nx.selfloop_edges(G, keys=True))
+            polynomial += x ** len(bridges) * y ** len(loops)
+        else:
+            # deletion-contraction
+            C = nx.contracted_edge(G, e, self_loops=True)
+            C.remove_edge(e[0], e[0])
+            G.remove_edge(*e)
+            stack.append(G)
+            stack.append(C)
+    return sympy.simplify(polynomial)
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def chromatic_polynomial(G):
+    r"""Returns the chromatic polynomial of `G`
+
+    This function computes the chromatic polynomial via an iterative version of
+    the deletion-contraction algorithm.
+
+    The chromatic polynomial `X_G(x)` is a fundamental graph polynomial
+    invariant in one variable. Evaluating `X_G(k)` for an natural number `k`
+    enumerates the proper k-colorings of `G`.
+
+    There are several equivalent definitions; here are three:
+
+    Def 1 (explicit formula):
+    For `G` an undirected graph, `c(G)` the number of connected components of
+    `G`, `E` the edge set of `G`, and `G(S)` the spanning subgraph of `G` with
+    edge set `S` [1]_:
+
+    .. math::
+
+        X_G(x) = \sum_{S \subseteq E} (-1)^{|S|} x^{c(G(S))}
+
+
+    Def 2 (interpolating polynomial):
+    For `G` an undirected graph, `n(G)` the number of vertices of `G`, `k_0 = 0`,
+    and `k_i` the number of distinct ways to color the vertices of `G` with `i`
+    unique colors (for `i` a natural number at most `n(G)`), `X_G(x)` is the
+    unique Lagrange interpolating polynomial of degree `n(G)` through the points
+    `(0, k_0), (1, k_1), \dots, (n(G), k_{n(G)})` [2]_.
+
+
+    Def 3 (chromatic recurrence):
+    For `G` an undirected graph, `G-e` the graph obtained from `G` by deleting
+    edge `e`, `G/e` the graph obtained from `G` by contracting edge `e`, `n(G)`
+    the number of vertices of `G`, and `e(G)` the number of edges of `G` [3]_:
+
+    .. math::
+        X_G(x) = \begin{cases}
+    	   x^{n(G)}, & \text{if $e(G)=0$} \\
+           X_{G-e}(x) - X_{G/e}(x), & \text{otherwise, for an arbitrary edge $e$}
+        \end{cases}
+
+    This formulation is also known as the Fundamental Reduction Theorem [4]_.
+
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    instance of `sympy.core.add.Add`
+        A Sympy expression representing the chromatic polynomial for `G`.
+
+    Examples
+    --------
+    >>> C = nx.cycle_graph(5)
+    >>> nx.chromatic_polynomial(C)
+    x**5 - 5*x**4 + 10*x**3 - 10*x**2 + 4*x
+
+    >>> G = nx.complete_graph(4)
+    >>> nx.chromatic_polynomial(G)
+    x**4 - 6*x**3 + 11*x**2 - 6*x
+
+    Notes
+    -----
+    Interpretation of the coefficients is discussed in [5]_. Several special
+    cases are listed in [2]_.
+
+    The chromatic polynomial is a specialization of the Tutte polynomial; in
+    particular, ``X_G(x) = T_G(x, 0)`` [6]_.
+
+    The chromatic polynomial may take negative arguments, though evaluations
+    may not have chromatic interpretations. For instance, ``X_G(-1)`` enumerates
+    the acyclic orientations of `G` [7]_.
+
+    References
+    ----------
+    .. [1] D. B. West,
+       "Introduction to Graph Theory," p. 222
+    .. [2] E. W. Weisstein
+       "Chromatic Polynomial"
+       MathWorld--A Wolfram Web Resource
+       https://mathworld.wolfram.com/ChromaticPolynomial.html
+    .. [3] D. B. West,
+       "Introduction to Graph Theory," p. 221
+    .. [4] J. Zhang, J. Goodall,
+       "An Introduction to Chromatic Polynomials"
+       https://math.mit.edu/~apost/courses/18.204_2018/Julie_Zhang_paper.pdf
+    .. [5] R. C. Read,
+       "An Introduction to Chromatic Polynomials"
+       Journal of Combinatorial Theory, 1968
+       https://math.berkeley.edu/~mrklug/ReadChromatic.pdf
+    .. [6] W. T. Tutte,
+       "Graph-polynomials"
+       Advances in Applied Mathematics, 2004
+       https://www.sciencedirect.com/science/article/pii/S0196885803000411
+    .. [7] R. P. Stanley,
+       "Acyclic orientations of graphs"
+       Discrete Mathematics, 2006
+       https://math.mit.edu/~rstan/pubs/pubfiles/18.pdf
+    """
+    import sympy
+
+    x = sympy.Symbol("x")
+    stack = deque()
+    stack.append(nx.MultiGraph(G, contraction_idx=0))
+
+    polynomial = 0
+    while stack:
+        G = stack.pop()
+        edges = list(G.edges)
+        if not edges:
+            polynomial += (-1) ** G.graph["contraction_idx"] * x ** len(G)
+        else:
+            e = edges[0]
+            C = nx.contracted_edge(G, e, self_loops=True)
+            C.graph["contraction_idx"] = G.graph["contraction_idx"] + 1
+            C.remove_edge(e[0], e[0])
+            G.remove_edge(*e)
+            stack.append(G)
+            stack.append(C)
+    return polynomial
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/reciprocity.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/reciprocity.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ea7ed2ce26ab973e07bcc6ec0d92aa4799d9a6a
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/reciprocity.py
@@ -0,0 +1,98 @@
+"""Algorithms to calculate reciprocity in a directed graph."""
+
+import networkx as nx
+from networkx import NetworkXError
+
+from ..utils import not_implemented_for
+
+__all__ = ["reciprocity", "overall_reciprocity"]
+
+
+@not_implemented_for("undirected", "multigraph")
+@nx._dispatchable
+def reciprocity(G, nodes=None):
+    r"""Compute the reciprocity in a directed graph.
+
+    The reciprocity of a directed graph is defined as the ratio
+    of the number of edges pointing in both directions to the total
+    number of edges in the graph.
+    Formally, $r = |{(u,v) \in G|(v,u) \in G}| / |{(u,v) \in G}|$.
+
+    The reciprocity of a single node u is defined similarly,
+    it is the ratio of the number of edges in both directions to
+    the total number of edges attached to node u.
+
+    Parameters
+    ----------
+    G : graph
+       A networkx directed graph
+    nodes : container of nodes, optional (default=whole graph)
+       Compute reciprocity for nodes in this container.
+
+    Returns
+    -------
+    out : dictionary
+       Reciprocity keyed by node label.
+
+    Notes
+    -----
+    The reciprocity is not defined for isolated nodes.
+    In such cases this function will return None.
+
+    """
+    # If `nodes` is not specified, calculate the reciprocity of the graph.
+    if nodes is None:
+        return overall_reciprocity(G)
+
+    # If `nodes` represents a single node in the graph, return only its
+    # reciprocity.
+    if nodes in G:
+        reciprocity = next(_reciprocity_iter(G, nodes))[1]
+        if reciprocity is None:
+            raise NetworkXError("Not defined for isolated nodes.")
+        else:
+            return reciprocity
+
+    # Otherwise, `nodes` represents an iterable of nodes, so return a
+    # dictionary mapping node to its reciprocity.
+    return dict(_reciprocity_iter(G, nodes))
+
+
+def _reciprocity_iter(G, nodes):
+    """Return an iterator of (node, reciprocity)."""
+    n = G.nbunch_iter(nodes)
+    for node in n:
+        pred = set(G.predecessors(node))
+        succ = set(G.successors(node))
+        overlap = pred & succ
+        n_total = len(pred) + len(succ)
+
+        # Reciprocity is not defined for isolated nodes.
+        # Return None.
+        if n_total == 0:
+            yield (node, None)
+        else:
+            reciprocity = 2 * len(overlap) / n_total
+            yield (node, reciprocity)
+
+
+@not_implemented_for("undirected", "multigraph")
+@nx._dispatchable
+def overall_reciprocity(G):
+    """Compute the reciprocity for the whole graph.
+
+    See the doc of reciprocity for the definition.
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+
+    """
+    n_all_edge = G.number_of_edges()
+    n_overlap_edge = (n_all_edge - G.to_undirected().number_of_edges()) * 2
+
+    if n_all_edge == 0:
+        raise NetworkXError("Not defined for empty graphs")
+
+    return n_overlap_edge / n_all_edge
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/regular.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/regular.py
new file mode 100644
index 0000000000000000000000000000000000000000..f483ef32c85e67a61a863b6390df3f693c757de1
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/regular.py
@@ -0,0 +1,215 @@
+"""Functions for computing and verifying regular graphs."""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["is_regular", "is_k_regular", "k_factor"]
+
+
+@nx._dispatchable
+def is_regular(G):
+    """Determines whether the graph ``G`` is a regular graph.
+
+    A regular graph is a graph where each vertex has the same degree. A
+    regular digraph is a graph where the indegree and outdegree of each
+    vertex are equal.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    bool
+        Whether the given graph or digraph is regular.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 4), (4, 1)])
+    >>> nx.is_regular(G)
+    True
+
+    """
+    if len(G) == 0:
+        raise nx.NetworkXPointlessConcept("Graph has no nodes.")
+    n1 = nx.utils.arbitrary_element(G)
+    if not G.is_directed():
+        d1 = G.degree(n1)
+        return all(d1 == d for _, d in G.degree)
+    else:
+        d_in = G.in_degree(n1)
+        in_regular = all(d_in == d for _, d in G.in_degree)
+        d_out = G.out_degree(n1)
+        out_regular = all(d_out == d for _, d in G.out_degree)
+        return in_regular and out_regular
+
+
+@not_implemented_for("directed")
+@nx._dispatchable
+def is_k_regular(G, k):
+    """Determines whether the graph ``G`` is a k-regular graph.
+
+    A k-regular graph is a graph where each vertex has degree k.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    bool
+        Whether the given graph is k-regular.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (2, 3), (3, 4), (4, 1)])
+    >>> nx.is_k_regular(G, k=3)
+    False
+
+    """
+    return all(d == k for n, d in G.degree)
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(preserve_edge_attrs=True, returns_graph=True)
+def k_factor(G, k, matching_weight="weight"):
+    """Compute a k-factor of G
+
+    A k-factor of a graph is a spanning k-regular subgraph.
+    A spanning k-regular subgraph of G is a subgraph that contains
+    each vertex of G and a subset of the edges of G such that each
+    vertex has degree k.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+      Undirected graph
+
+    matching_weight: string, optional (default='weight')
+       Edge data key corresponding to the edge weight.
+       Used for finding the max-weighted perfect matching.
+       If key not found, uses 1 as weight.
+
+    Returns
+    -------
+    G2 : NetworkX graph
+        A k-factor of G
+
+    Examples
+    --------
+    >>> G = nx.Graph([(1, 2), (2, 3), (3, 4), (4, 1)])
+    >>> G2 = nx.k_factor(G, k=1)
+    >>> G2.edges()
+    EdgeView([(1, 2), (3, 4)])
+
+    References
+    ----------
+    .. [1] "An algorithm for computing simple k-factors.",
+       Meijer, Henk, Yurai Núñez-Rodríguez, and David Rappaport,
+       Information processing letters, 2009.
+    """
+
+    from networkx.algorithms.matching import is_perfect_matching, max_weight_matching
+
+    class LargeKGadget:
+        def __init__(self, k, degree, node, g):
+            self.original = node
+            self.g = g
+            self.k = k
+            self.degree = degree
+
+            self.outer_vertices = [(node, x) for x in range(degree)]
+            self.core_vertices = [(node, x + degree) for x in range(degree - k)]
+
+        def replace_node(self):
+            adj_view = self.g[self.original]
+            neighbors = list(adj_view.keys())
+            edge_attrs = list(adj_view.values())
+            for outer, neighbor, edge_attrs in zip(
+                self.outer_vertices, neighbors, edge_attrs
+            ):
+                self.g.add_edge(outer, neighbor, **edge_attrs)
+            for core in self.core_vertices:
+                for outer in self.outer_vertices:
+                    self.g.add_edge(core, outer)
+            self.g.remove_node(self.original)
+
+        def restore_node(self):
+            self.g.add_node(self.original)
+            for outer in self.outer_vertices:
+                adj_view = self.g[outer]
+                for neighbor, edge_attrs in list(adj_view.items()):
+                    if neighbor not in self.core_vertices:
+                        self.g.add_edge(self.original, neighbor, **edge_attrs)
+                        break
+            g.remove_nodes_from(self.outer_vertices)
+            g.remove_nodes_from(self.core_vertices)
+
+    class SmallKGadget:
+        def __init__(self, k, degree, node, g):
+            self.original = node
+            self.k = k
+            self.degree = degree
+            self.g = g
+
+            self.outer_vertices = [(node, x) for x in range(degree)]
+            self.inner_vertices = [(node, x + degree) for x in range(degree)]
+            self.core_vertices = [(node, x + 2 * degree) for x in range(k)]
+
+        def replace_node(self):
+            adj_view = self.g[self.original]
+            for outer, inner, (neighbor, edge_attrs) in zip(
+                self.outer_vertices, self.inner_vertices, list(adj_view.items())
+            ):
+                self.g.add_edge(outer, inner)
+                self.g.add_edge(outer, neighbor, **edge_attrs)
+            for core in self.core_vertices:
+                for inner in self.inner_vertices:
+                    self.g.add_edge(core, inner)
+            self.g.remove_node(self.original)
+
+        def restore_node(self):
+            self.g.add_node(self.original)
+            for outer in self.outer_vertices:
+                adj_view = self.g[outer]
+                for neighbor, edge_attrs in adj_view.items():
+                    if neighbor not in self.core_vertices:
+                        self.g.add_edge(self.original, neighbor, **edge_attrs)
+                        break
+            self.g.remove_nodes_from(self.outer_vertices)
+            self.g.remove_nodes_from(self.inner_vertices)
+            self.g.remove_nodes_from(self.core_vertices)
+
+    # Step 1
+    if any(d < k for _, d in G.degree):
+        raise nx.NetworkXUnfeasible("Graph contains a vertex with degree less than k")
+    g = G.copy()
+
+    # Step 2
+    gadgets = []
+    for node, degree in list(g.degree):
+        if k < degree / 2.0:
+            gadget = SmallKGadget(k, degree, node, g)
+        else:
+            gadget = LargeKGadget(k, degree, node, g)
+        gadget.replace_node()
+        gadgets.append(gadget)
+
+    # Step 3
+    matching = max_weight_matching(g, maxcardinality=True, weight=matching_weight)
+
+    # Step 4
+    if not is_perfect_matching(g, matching):
+        raise nx.NetworkXUnfeasible(
+            "Cannot find k-factor because no perfect matching exists"
+        )
+
+    for edge in g.edges():
+        if edge not in matching and (edge[1], edge[0]) not in matching:
+            g.remove_edge(edge[0], edge[1])
+
+    for gadget in gadgets:
+        gadget.restore_node()
+
+    return g
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/richclub.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/richclub.py
new file mode 100644
index 0000000000000000000000000000000000000000..445b27d142547e5cad04e00abc9ca33d45edbee6
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/richclub.py
@@ -0,0 +1,138 @@
+"""Functions for computing rich-club coefficients."""
+
+from itertools import accumulate
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["rich_club_coefficient"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def rich_club_coefficient(G, normalized=True, Q=100, seed=None):
+    r"""Returns the rich-club coefficient of the graph `G`.
+
+    For each degree *k*, the *rich-club coefficient* is the ratio of the
+    number of actual to the number of potential edges for nodes with
+    degree greater than *k*:
+
+    .. math::
+
+        \phi(k) = \frac{2 E_k}{N_k (N_k - 1)}
+
+    where `N_k` is the number of nodes with degree larger than *k*, and
+    `E_k` is the number of edges among those nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph with neither parallel edges nor self-loops.
+    normalized : bool (optional)
+        Normalize using randomized network as in [1]_
+    Q : float (optional, default=100)
+        If `normalized` is True, perform `Q * m` double-edge
+        swaps, where `m` is the number of edges in `G`, to use as a
+        null-model for normalization.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    rc : dictionary
+       A dictionary, keyed by degree, with rich-club coefficient values.
+
+    Raises
+    ------
+    NetworkXError
+        If `G` has fewer than four nodes and ``normalized=True``.
+        A randomly sampled graph for normalization cannot be generated in this case.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2), (1, 2), (1, 3), (1, 4), (4, 5)])
+    >>> rc = nx.rich_club_coefficient(G, normalized=False, seed=42)
+    >>> rc[0]
+    0.4
+
+    Notes
+    -----
+    The rich club definition and algorithm are found in [1]_.  This
+    algorithm ignores any edge weights and is not defined for directed
+    graphs or graphs with parallel edges or self loops.
+
+    Normalization is done by computing the rich club coefficient for a randomly
+    sampled graph with the same degree distribution as `G` by
+    repeatedly swapping the endpoints of existing edges. For graphs with fewer than 4
+    nodes, it is not possible to generate a random graph with a prescribed
+    degree distribution, as the degree distribution fully determines the graph
+    (hence making the coefficients trivially normalized to 1).
+    This function raises an exception in this case.
+
+    Estimates for appropriate values of `Q` are found in [2]_.
+
+    References
+    ----------
+    .. [1] Julian J. McAuley, Luciano da Fontoura Costa,
+       and Tibério S. Caetano,
+       "The rich-club phenomenon across complex network hierarchies",
+       Applied Physics Letters Vol 91 Issue 8, August 2007.
+       https://arxiv.org/abs/physics/0701290
+    .. [2] R. Milo, N. Kashtan, S. Itzkovitz, M. E. J. Newman, U. Alon,
+       "Uniform generation of random graphs with arbitrary degree
+       sequences", 2006. https://arxiv.org/abs/cond-mat/0312028
+    """
+    if nx.number_of_selfloops(G) > 0:
+        raise Exception(
+            "rich_club_coefficient is not implemented for graphs with self loops."
+        )
+    rc = _compute_rc(G)
+    if normalized:
+        # make R a copy of G, randomize with Q*|E| double edge swaps
+        # and use rich_club coefficient of R to normalize
+        R = G.copy()
+        E = R.number_of_edges()
+        nx.double_edge_swap(R, Q * E, max_tries=Q * E * 10, seed=seed)
+        rcran = _compute_rc(R)
+        rc = {k: v / rcran[k] for k, v in rc.items()}
+    return rc
+
+
+def _compute_rc(G):
+    """Returns the rich-club coefficient for each degree in the graph
+    `G`.
+
+    `G` is an undirected graph without multiedges.
+
+    Returns a dictionary mapping degree to rich-club coefficient for
+    that degree.
+
+    """
+    deghist = nx.degree_histogram(G)
+    total = sum(deghist)
+    # Compute the number of nodes with degree greater than `k`, for each
+    # degree `k` (omitting the last entry, which is zero).
+    nks = (total - cs for cs in accumulate(deghist) if total - cs > 1)
+    # Create a sorted list of pairs of edge endpoint degrees.
+    #
+    # The list is sorted in reverse order so that we can pop from the
+    # right side of the list later, instead of popping from the left
+    # side of the list, which would have a linear time cost.
+    edge_degrees = sorted((sorted(map(G.degree, e)) for e in G.edges()), reverse=True)
+    ek = G.number_of_edges()
+    if ek == 0:
+        return {}
+
+    k1, k2 = edge_degrees.pop()
+    rc = {}
+    for d, nk in enumerate(nks):
+        while k1 <= d:
+            if len(edge_degrees) == 0:
+                ek = 0
+                break
+            k1, k2 = edge_degrees.pop()
+            ek -= 1
+        rc[d] = 2 * ek / (nk * (nk - 1))
+    return rc
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/similarity.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/similarity.py
new file mode 100644
index 0000000000000000000000000000000000000000..245bc8913c53b32cac46b8754fc9d7b4d5465ce0
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/similarity.py
@@ -0,0 +1,1783 @@
+"""Functions measuring similarity using graph edit distance.
+
+The graph edit distance is the number of edge/node changes needed
+to make two graphs isomorphic.
+
+The default algorithm/implementation is sub-optimal for some graphs.
+The problem of finding the exact Graph Edit Distance (GED) is NP-hard
+so it is often slow. If the simple interface `graph_edit_distance`
+takes too long for your graph, try `optimize_graph_edit_distance`
+and/or `optimize_edit_paths`.
+
+At the same time, I encourage capable people to investigate
+alternative GED algorithms, in order to improve the choices available.
+"""
+
+import math
+import time
+import warnings
+from dataclasses import dataclass
+from itertools import product
+
+import networkx as nx
+from networkx.utils import np_random_state
+
+__all__ = [
+    "graph_edit_distance",
+    "optimal_edit_paths",
+    "optimize_graph_edit_distance",
+    "optimize_edit_paths",
+    "simrank_similarity",
+    "panther_similarity",
+    "generate_random_paths",
+]
+
+
+@nx._dispatchable(
+    graphs={"G1": 0, "G2": 1}, preserve_edge_attrs=True, preserve_node_attrs=True
+)
+def graph_edit_distance(
+    G1,
+    G2,
+    node_match=None,
+    edge_match=None,
+    node_subst_cost=None,
+    node_del_cost=None,
+    node_ins_cost=None,
+    edge_subst_cost=None,
+    edge_del_cost=None,
+    edge_ins_cost=None,
+    roots=None,
+    upper_bound=None,
+    timeout=None,
+):
+    """Returns GED (graph edit distance) between graphs G1 and G2.
+
+    Graph edit distance is a graph similarity measure analogous to
+    Levenshtein distance for strings.  It is defined as minimum cost
+    of edit path (sequence of node and edge edit operations)
+    transforming graph G1 to graph isomorphic to G2.
+
+    Parameters
+    ----------
+    G1, G2: graphs
+        The two graphs G1 and G2 must be of the same type.
+
+    node_match : callable
+        A function that returns True if node n1 in G1 and n2 in G2
+        should be considered equal during matching.
+
+        The function will be called like
+
+           node_match(G1.nodes[n1], G2.nodes[n2]).
+
+        That is, the function will receive the node attribute
+        dictionaries for n1 and n2 as inputs.
+
+        Ignored if node_subst_cost is specified.  If neither
+        node_match nor node_subst_cost are specified then node
+        attributes are not considered.
+
+    edge_match : callable
+        A function that returns True if the edge attribute dictionaries
+        for the pair of nodes (u1, v1) in G1 and (u2, v2) in G2 should
+        be considered equal during matching.
+
+        The function will be called like
+
+           edge_match(G1[u1][v1], G2[u2][v2]).
+
+        That is, the function will receive the edge attribute
+        dictionaries of the edges under consideration.
+
+        Ignored if edge_subst_cost is specified.  If neither
+        edge_match nor edge_subst_cost are specified then edge
+        attributes are not considered.
+
+    node_subst_cost, node_del_cost, node_ins_cost : callable
+        Functions that return the costs of node substitution, node
+        deletion, and node insertion, respectively.
+
+        The functions will be called like
+
+           node_subst_cost(G1.nodes[n1], G2.nodes[n2]),
+           node_del_cost(G1.nodes[n1]),
+           node_ins_cost(G2.nodes[n2]).
+
+        That is, the functions will receive the node attribute
+        dictionaries as inputs.  The functions are expected to return
+        positive numeric values.
+
+        Function node_subst_cost overrides node_match if specified.
+        If neither node_match nor node_subst_cost are specified then
+        default node substitution cost of 0 is used (node attributes
+        are not considered during matching).
+
+        If node_del_cost is not specified then default node deletion
+        cost of 1 is used.  If node_ins_cost is not specified then
+        default node insertion cost of 1 is used.
+
+    edge_subst_cost, edge_del_cost, edge_ins_cost : callable
+        Functions that return the costs of edge substitution, edge
+        deletion, and edge insertion, respectively.
+
+        The functions will be called like
+
+           edge_subst_cost(G1[u1][v1], G2[u2][v2]),
+           edge_del_cost(G1[u1][v1]),
+           edge_ins_cost(G2[u2][v2]).
+
+        That is, the functions will receive the edge attribute
+        dictionaries as inputs.  The functions are expected to return
+        positive numeric values.
+
+        Function edge_subst_cost overrides edge_match if specified.
+        If neither edge_match nor edge_subst_cost are specified then
+        default edge substitution cost of 0 is used (edge attributes
+        are not considered during matching).
+
+        If edge_del_cost is not specified then default edge deletion
+        cost of 1 is used.  If edge_ins_cost is not specified then
+        default edge insertion cost of 1 is used.
+
+    roots : 2-tuple
+        Tuple where first element is a node in G1 and the second
+        is a node in G2.
+        These nodes are forced to be matched in the comparison to
+        allow comparison between rooted graphs.
+
+    upper_bound : numeric
+        Maximum edit distance to consider.  Return None if no edit
+        distance under or equal to upper_bound exists.
+
+    timeout : numeric
+        Maximum number of seconds to execute.
+        After timeout is met, the current best GED is returned.
+
+    Examples
+    --------
+    >>> G1 = nx.cycle_graph(6)
+    >>> G2 = nx.wheel_graph(7)
+    >>> nx.graph_edit_distance(G1, G2)
+    7.0
+
+    >>> G1 = nx.star_graph(5)
+    >>> G2 = nx.star_graph(5)
+    >>> nx.graph_edit_distance(G1, G2, roots=(0, 0))
+    0.0
+    >>> nx.graph_edit_distance(G1, G2, roots=(1, 0))
+    8.0
+
+    See Also
+    --------
+    optimal_edit_paths, optimize_graph_edit_distance,
+
+    is_isomorphic: test for graph edit distance of 0
+
+    References
+    ----------
+    .. [1] Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, Patrick
+       Martineau. An Exact Graph Edit Distance Algorithm for Solving
+       Pattern Recognition Problems. 4th International Conference on
+       Pattern Recognition Applications and Methods 2015, Jan 2015,
+       Lisbon, Portugal. 2015,
+       <10.5220/0005209202710278>. <hal-01168816>
+       https://hal.archives-ouvertes.fr/hal-01168816
+
+    """
+    bestcost = None
+    for _, _, cost in optimize_edit_paths(
+        G1,
+        G2,
+        node_match,
+        edge_match,
+        node_subst_cost,
+        node_del_cost,
+        node_ins_cost,
+        edge_subst_cost,
+        edge_del_cost,
+        edge_ins_cost,
+        upper_bound,
+        True,
+        roots,
+        timeout,
+    ):
+        # assert bestcost is None or cost < bestcost
+        bestcost = cost
+    return bestcost
+
+
+@nx._dispatchable(graphs={"G1": 0, "G2": 1})
+def optimal_edit_paths(
+    G1,
+    G2,
+    node_match=None,
+    edge_match=None,
+    node_subst_cost=None,
+    node_del_cost=None,
+    node_ins_cost=None,
+    edge_subst_cost=None,
+    edge_del_cost=None,
+    edge_ins_cost=None,
+    upper_bound=None,
+):
+    """Returns all minimum-cost edit paths transforming G1 to G2.
+
+    Graph edit path is a sequence of node and edge edit operations
+    transforming graph G1 to graph isomorphic to G2.  Edit operations
+    include substitutions, deletions, and insertions.
+
+    Parameters
+    ----------
+    G1, G2: graphs
+        The two graphs G1 and G2 must be of the same type.
+
+    node_match : callable
+        A function that returns True if node n1 in G1 and n2 in G2
+        should be considered equal during matching.
+
+        The function will be called like
+
+           node_match(G1.nodes[n1], G2.nodes[n2]).
+
+        That is, the function will receive the node attribute
+        dictionaries for n1 and n2 as inputs.
+
+        Ignored if node_subst_cost is specified.  If neither
+        node_match nor node_subst_cost are specified then node
+        attributes are not considered.
+
+    edge_match : callable
+        A function that returns True if the edge attribute dictionaries
+        for the pair of nodes (u1, v1) in G1 and (u2, v2) in G2 should
+        be considered equal during matching.
+
+        The function will be called like
+
+           edge_match(G1[u1][v1], G2[u2][v2]).
+
+        That is, the function will receive the edge attribute
+        dictionaries of the edges under consideration.
+
+        Ignored if edge_subst_cost is specified.  If neither
+        edge_match nor edge_subst_cost are specified then edge
+        attributes are not considered.
+
+    node_subst_cost, node_del_cost, node_ins_cost : callable
+        Functions that return the costs of node substitution, node
+        deletion, and node insertion, respectively.
+
+        The functions will be called like
+
+           node_subst_cost(G1.nodes[n1], G2.nodes[n2]),
+           node_del_cost(G1.nodes[n1]),
+           node_ins_cost(G2.nodes[n2]).
+
+        That is, the functions will receive the node attribute
+        dictionaries as inputs.  The functions are expected to return
+        positive numeric values.
+
+        Function node_subst_cost overrides node_match if specified.
+        If neither node_match nor node_subst_cost are specified then
+        default node substitution cost of 0 is used (node attributes
+        are not considered during matching).
+
+        If node_del_cost is not specified then default node deletion
+        cost of 1 is used.  If node_ins_cost is not specified then
+        default node insertion cost of 1 is used.
+
+    edge_subst_cost, edge_del_cost, edge_ins_cost : callable
+        Functions that return the costs of edge substitution, edge
+        deletion, and edge insertion, respectively.
+
+        The functions will be called like
+
+           edge_subst_cost(G1[u1][v1], G2[u2][v2]),
+           edge_del_cost(G1[u1][v1]),
+           edge_ins_cost(G2[u2][v2]).
+
+        That is, the functions will receive the edge attribute
+        dictionaries as inputs.  The functions are expected to return
+        positive numeric values.
+
+        Function edge_subst_cost overrides edge_match if specified.
+        If neither edge_match nor edge_subst_cost are specified then
+        default edge substitution cost of 0 is used (edge attributes
+        are not considered during matching).
+
+        If edge_del_cost is not specified then default edge deletion
+        cost of 1 is used.  If edge_ins_cost is not specified then
+        default edge insertion cost of 1 is used.
+
+    upper_bound : numeric
+        Maximum edit distance to consider.
+
+    Returns
+    -------
+    edit_paths : list of tuples (node_edit_path, edge_edit_path)
+       - node_edit_path : list of tuples ``(u, v)`` indicating node transformations
+         between `G1` and `G2`. ``u`` is `None` for insertion, ``v`` is `None`
+         for deletion.
+       - edge_edit_path : list of tuples ``((u1, v1), (u2, v2))`` indicating edge
+         transformations between `G1` and `G2`. ``(None, (u2,v2))`` for insertion
+         and ``((u1,v1), None)`` for deletion.
+
+    cost : numeric
+        Optimal edit path cost (graph edit distance). When the cost
+        is zero, it indicates that `G1` and `G2` are isomorphic.
+
+    Examples
+    --------
+    >>> G1 = nx.cycle_graph(4)
+    >>> G2 = nx.wheel_graph(5)
+    >>> paths, cost = nx.optimal_edit_paths(G1, G2)
+    >>> len(paths)
+    40
+    >>> cost
+    5.0
+
+    Notes
+    -----
+    To transform `G1` into a graph isomorphic to `G2`, apply the node
+    and edge edits in the returned ``edit_paths``.
+    In the case of isomorphic graphs, the cost is zero, and the paths
+    represent different isomorphic mappings (isomorphisms). That is, the
+    edits involve renaming nodes and edges to match the structure of `G2`.
+
+    See Also
+    --------
+    graph_edit_distance, optimize_edit_paths
+
+    References
+    ----------
+    .. [1] Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, Patrick
+       Martineau. An Exact Graph Edit Distance Algorithm for Solving
+       Pattern Recognition Problems. 4th International Conference on
+       Pattern Recognition Applications and Methods 2015, Jan 2015,
+       Lisbon, Portugal. 2015,
+       <10.5220/0005209202710278>. <hal-01168816>
+       https://hal.archives-ouvertes.fr/hal-01168816
+
+    """
+    paths = []
+    bestcost = None
+    for vertex_path, edge_path, cost in optimize_edit_paths(
+        G1,
+        G2,
+        node_match,
+        edge_match,
+        node_subst_cost,
+        node_del_cost,
+        node_ins_cost,
+        edge_subst_cost,
+        edge_del_cost,
+        edge_ins_cost,
+        upper_bound,
+        False,
+    ):
+        # assert bestcost is None or cost <= bestcost
+        if bestcost is not None and cost < bestcost:
+            paths = []
+        paths.append((vertex_path, edge_path))
+        bestcost = cost
+    return paths, bestcost
+
+
+@nx._dispatchable(graphs={"G1": 0, "G2": 1})
+def optimize_graph_edit_distance(
+    G1,
+    G2,
+    node_match=None,
+    edge_match=None,
+    node_subst_cost=None,
+    node_del_cost=None,
+    node_ins_cost=None,
+    edge_subst_cost=None,
+    edge_del_cost=None,
+    edge_ins_cost=None,
+    upper_bound=None,
+):
+    """Returns consecutive approximations of GED (graph edit distance)
+    between graphs G1 and G2.
+
+    Graph edit distance is a graph similarity measure analogous to
+    Levenshtein distance for strings.  It is defined as minimum cost
+    of edit path (sequence of node and edge edit operations)
+    transforming graph G1 to graph isomorphic to G2.
+
+    Parameters
+    ----------
+    G1, G2: graphs
+        The two graphs G1 and G2 must be of the same type.
+
+    node_match : callable
+        A function that returns True if node n1 in G1 and n2 in G2
+        should be considered equal during matching.
+
+        The function will be called like
+
+           node_match(G1.nodes[n1], G2.nodes[n2]).
+
+        That is, the function will receive the node attribute
+        dictionaries for n1 and n2 as inputs.
+
+        Ignored if node_subst_cost is specified.  If neither
+        node_match nor node_subst_cost are specified then node
+        attributes are not considered.
+
+    edge_match : callable
+        A function that returns True if the edge attribute dictionaries
+        for the pair of nodes (u1, v1) in G1 and (u2, v2) in G2 should
+        be considered equal during matching.
+
+        The function will be called like
+
+           edge_match(G1[u1][v1], G2[u2][v2]).
+
+        That is, the function will receive the edge attribute
+        dictionaries of the edges under consideration.
+
+        Ignored if edge_subst_cost is specified.  If neither
+        edge_match nor edge_subst_cost are specified then edge
+        attributes are not considered.
+
+    node_subst_cost, node_del_cost, node_ins_cost : callable
+        Functions that return the costs of node substitution, node
+        deletion, and node insertion, respectively.
+
+        The functions will be called like
+
+           node_subst_cost(G1.nodes[n1], G2.nodes[n2]),
+           node_del_cost(G1.nodes[n1]),
+           node_ins_cost(G2.nodes[n2]).
+
+        That is, the functions will receive the node attribute
+        dictionaries as inputs.  The functions are expected to return
+        positive numeric values.
+
+        Function node_subst_cost overrides node_match if specified.
+        If neither node_match nor node_subst_cost are specified then
+        default node substitution cost of 0 is used (node attributes
+        are not considered during matching).
+
+        If node_del_cost is not specified then default node deletion
+        cost of 1 is used.  If node_ins_cost is not specified then
+        default node insertion cost of 1 is used.
+
+    edge_subst_cost, edge_del_cost, edge_ins_cost : callable
+        Functions that return the costs of edge substitution, edge
+        deletion, and edge insertion, respectively.
+
+        The functions will be called like
+
+           edge_subst_cost(G1[u1][v1], G2[u2][v2]),
+           edge_del_cost(G1[u1][v1]),
+           edge_ins_cost(G2[u2][v2]).
+
+        That is, the functions will receive the edge attribute
+        dictionaries as inputs.  The functions are expected to return
+        positive numeric values.
+
+        Function edge_subst_cost overrides edge_match if specified.
+        If neither edge_match nor edge_subst_cost are specified then
+        default edge substitution cost of 0 is used (edge attributes
+        are not considered during matching).
+
+        If edge_del_cost is not specified then default edge deletion
+        cost of 1 is used.  If edge_ins_cost is not specified then
+        default edge insertion cost of 1 is used.
+
+    upper_bound : numeric
+        Maximum edit distance to consider.
+
+    Returns
+    -------
+    Generator of consecutive approximations of graph edit distance.
+
+    Examples
+    --------
+    >>> G1 = nx.cycle_graph(6)
+    >>> G2 = nx.wheel_graph(7)
+    >>> for v in nx.optimize_graph_edit_distance(G1, G2):
+    ...     minv = v
+    >>> minv
+    7.0
+
+    See Also
+    --------
+    graph_edit_distance, optimize_edit_paths
+
+    References
+    ----------
+    .. [1] Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, Patrick
+       Martineau. An Exact Graph Edit Distance Algorithm for Solving
+       Pattern Recognition Problems. 4th International Conference on
+       Pattern Recognition Applications and Methods 2015, Jan 2015,
+       Lisbon, Portugal. 2015,
+       <10.5220/0005209202710278>. <hal-01168816>
+       https://hal.archives-ouvertes.fr/hal-01168816
+    """
+    for _, _, cost in optimize_edit_paths(
+        G1,
+        G2,
+        node_match,
+        edge_match,
+        node_subst_cost,
+        node_del_cost,
+        node_ins_cost,
+        edge_subst_cost,
+        edge_del_cost,
+        edge_ins_cost,
+        upper_bound,
+        True,
+    ):
+        yield cost
+
+
+@nx._dispatchable(
+    graphs={"G1": 0, "G2": 1}, preserve_edge_attrs=True, preserve_node_attrs=True
+)
+def optimize_edit_paths(
+    G1,
+    G2,
+    node_match=None,
+    edge_match=None,
+    node_subst_cost=None,
+    node_del_cost=None,
+    node_ins_cost=None,
+    edge_subst_cost=None,
+    edge_del_cost=None,
+    edge_ins_cost=None,
+    upper_bound=None,
+    strictly_decreasing=True,
+    roots=None,
+    timeout=None,
+):
+    """GED (graph edit distance) calculation: advanced interface.
+
+    Graph edit path is a sequence of node and edge edit operations
+    transforming graph G1 to graph isomorphic to G2.  Edit operations
+    include substitutions, deletions, and insertions.
+
+    Graph edit distance is defined as minimum cost of edit path.
+
+    Parameters
+    ----------
+    G1, G2: graphs
+        The two graphs G1 and G2 must be of the same type.
+
+    node_match : callable
+        A function that returns True if node n1 in G1 and n2 in G2
+        should be considered equal during matching.
+
+        The function will be called like
+
+           node_match(G1.nodes[n1], G2.nodes[n2]).
+
+        That is, the function will receive the node attribute
+        dictionaries for n1 and n2 as inputs.
+
+        Ignored if node_subst_cost is specified.  If neither
+        node_match nor node_subst_cost are specified then node
+        attributes are not considered.
+
+    edge_match : callable
+        A function that returns True if the edge attribute dictionaries
+        for the pair of nodes (u1, v1) in G1 and (u2, v2) in G2 should
+        be considered equal during matching.
+
+        The function will be called like
+
+           edge_match(G1[u1][v1], G2[u2][v2]).
+
+        That is, the function will receive the edge attribute
+        dictionaries of the edges under consideration.
+
+        Ignored if edge_subst_cost is specified.  If neither
+        edge_match nor edge_subst_cost are specified then edge
+        attributes are not considered.
+
+    node_subst_cost, node_del_cost, node_ins_cost : callable
+        Functions that return the costs of node substitution, node
+        deletion, and node insertion, respectively.
+
+        The functions will be called like
+
+           node_subst_cost(G1.nodes[n1], G2.nodes[n2]),
+           node_del_cost(G1.nodes[n1]),
+           node_ins_cost(G2.nodes[n2]).
+
+        That is, the functions will receive the node attribute
+        dictionaries as inputs.  The functions are expected to return
+        positive numeric values.
+
+        Function node_subst_cost overrides node_match if specified.
+        If neither node_match nor node_subst_cost are specified then
+        default node substitution cost of 0 is used (node attributes
+        are not considered during matching).
+
+        If node_del_cost is not specified then default node deletion
+        cost of 1 is used.  If node_ins_cost is not specified then
+        default node insertion cost of 1 is used.
+
+    edge_subst_cost, edge_del_cost, edge_ins_cost : callable
+        Functions that return the costs of edge substitution, edge
+        deletion, and edge insertion, respectively.
+
+        The functions will be called like
+
+           edge_subst_cost(G1[u1][v1], G2[u2][v2]),
+           edge_del_cost(G1[u1][v1]),
+           edge_ins_cost(G2[u2][v2]).
+
+        That is, the functions will receive the edge attribute
+        dictionaries as inputs.  The functions are expected to return
+        positive numeric values.
+
+        Function edge_subst_cost overrides edge_match if specified.
+        If neither edge_match nor edge_subst_cost are specified then
+        default edge substitution cost of 0 is used (edge attributes
+        are not considered during matching).
+
+        If edge_del_cost is not specified then default edge deletion
+        cost of 1 is used.  If edge_ins_cost is not specified then
+        default edge insertion cost of 1 is used.
+
+    upper_bound : numeric
+        Maximum edit distance to consider.
+
+    strictly_decreasing : bool
+        If True, return consecutive approximations of strictly
+        decreasing cost.  Otherwise, return all edit paths of cost
+        less than or equal to the previous minimum cost.
+
+    roots : 2-tuple
+        Tuple where first element is a node in G1 and the second
+        is a node in G2.
+        These nodes are forced to be matched in the comparison to
+        allow comparison between rooted graphs.
+
+    timeout : numeric
+        Maximum number of seconds to execute.
+        After timeout is met, the current best GED is returned.
+
+    Returns
+    -------
+    Generator of tuples (node_edit_path, edge_edit_path, cost)
+        node_edit_path : list of tuples (u, v)
+        edge_edit_path : list of tuples ((u1, v1), (u2, v2))
+        cost : numeric
+
+    See Also
+    --------
+    graph_edit_distance, optimize_graph_edit_distance, optimal_edit_paths
+
+    References
+    ----------
+    .. [1] Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, Patrick
+       Martineau. An Exact Graph Edit Distance Algorithm for Solving
+       Pattern Recognition Problems. 4th International Conference on
+       Pattern Recognition Applications and Methods 2015, Jan 2015,
+       Lisbon, Portugal. 2015,
+       <10.5220/0005209202710278>. <hal-01168816>
+       https://hal.archives-ouvertes.fr/hal-01168816
+
+    """
+    # TODO: support DiGraph
+
+    import numpy as np
+    import scipy as sp
+
+    @dataclass
+    class CostMatrix:
+        C: ...
+        lsa_row_ind: ...
+        lsa_col_ind: ...
+        ls: ...
+
+    def make_CostMatrix(C, m, n):
+        # assert(C.shape == (m + n, m + n))
+        lsa_row_ind, lsa_col_ind = sp.optimize.linear_sum_assignment(C)
+
+        # Fixup dummy assignments:
+        # each substitution i<->j should have dummy assignment m+j<->n+i
+        # NOTE: fast reduce of Cv relies on it
+        # Create masks for substitution and dummy indices
+        is_subst = (lsa_row_ind < m) & (lsa_col_ind < n)
+        is_dummy = (lsa_row_ind >= m) & (lsa_col_ind >= n)
+
+        # Map dummy assignments to the correct indices
+        lsa_row_ind[is_dummy] = lsa_col_ind[is_subst] + m
+        lsa_col_ind[is_dummy] = lsa_row_ind[is_subst] + n
+
+        return CostMatrix(
+            C, lsa_row_ind, lsa_col_ind, C[lsa_row_ind, lsa_col_ind].sum()
+        )
+
+    def extract_C(C, i, j, m, n):
+        # assert(C.shape == (m + n, m + n))
+        row_ind = [k in i or k - m in j for k in range(m + n)]
+        col_ind = [k in j or k - n in i for k in range(m + n)]
+        return C[row_ind, :][:, col_ind]
+
+    def reduce_C(C, i, j, m, n):
+        # assert(C.shape == (m + n, m + n))
+        row_ind = [k not in i and k - m not in j for k in range(m + n)]
+        col_ind = [k not in j and k - n not in i for k in range(m + n)]
+        return C[row_ind, :][:, col_ind]
+
+    def reduce_ind(ind, i):
+        # assert set(ind) == set(range(len(ind)))
+        rind = ind[[k not in i for k in ind]]
+        for k in set(i):
+            rind[rind >= k] -= 1
+        return rind
+
+    def match_edges(u, v, pending_g, pending_h, Ce, matched_uv=None):
+        """
+        Parameters:
+            u, v: matched vertices, u=None or v=None for
+               deletion/insertion
+            pending_g, pending_h: lists of edges not yet mapped
+            Ce: CostMatrix of pending edge mappings
+            matched_uv: partial vertex edit path
+                list of tuples (u, v) of previously matched vertex
+                    mappings u<->v, u=None or v=None for
+                    deletion/insertion
+
+        Returns:
+            list of (i, j): indices of edge mappings g<->h
+            localCe: local CostMatrix of edge mappings
+                (basically submatrix of Ce at cross of rows i, cols j)
+        """
+        M = len(pending_g)
+        N = len(pending_h)
+        # assert Ce.C.shape == (M + N, M + N)
+
+        # only attempt to match edges after one node match has been made
+        # this will stop self-edges on the first node being automatically deleted
+        # even when a substitution is the better option
+        if matched_uv is None or len(matched_uv) == 0:
+            g_ind = []
+            h_ind = []
+        else:
+            g_ind = [
+                i
+                for i in range(M)
+                if pending_g[i][:2] == (u, u)
+                or any(
+                    pending_g[i][:2] in ((p, u), (u, p), (p, p)) for p, q in matched_uv
+                )
+            ]
+            h_ind = [
+                j
+                for j in range(N)
+                if pending_h[j][:2] == (v, v)
+                or any(
+                    pending_h[j][:2] in ((q, v), (v, q), (q, q)) for p, q in matched_uv
+                )
+            ]
+
+        m = len(g_ind)
+        n = len(h_ind)
+
+        if m or n:
+            C = extract_C(Ce.C, g_ind, h_ind, M, N)
+            # assert C.shape == (m + n, m + n)
+
+            # Forbid structurally invalid matches
+            # NOTE: inf remembered from Ce construction
+            for k, i in enumerate(g_ind):
+                g = pending_g[i][:2]
+                for l, j in enumerate(h_ind):
+                    h = pending_h[j][:2]
+                    if nx.is_directed(G1) or nx.is_directed(G2):
+                        if any(
+                            g == (p, u) and h == (q, v) or g == (u, p) and h == (v, q)
+                            for p, q in matched_uv
+                        ):
+                            continue
+                    else:
+                        if any(
+                            g in ((p, u), (u, p)) and h in ((q, v), (v, q))
+                            for p, q in matched_uv
+                        ):
+                            continue
+                    if g == (u, u) or any(g == (p, p) for p, q in matched_uv):
+                        continue
+                    if h == (v, v) or any(h == (q, q) for p, q in matched_uv):
+                        continue
+                    C[k, l] = inf
+
+            localCe = make_CostMatrix(C, m, n)
+            ij = [
+                (
+                    g_ind[k] if k < m else M + h_ind[l],
+                    h_ind[l] if l < n else N + g_ind[k],
+                )
+                for k, l in zip(localCe.lsa_row_ind, localCe.lsa_col_ind)
+                if k < m or l < n
+            ]
+
+        else:
+            ij = []
+            localCe = CostMatrix(np.empty((0, 0)), [], [], 0)
+
+        return ij, localCe
+
+    def reduce_Ce(Ce, ij, m, n):
+        if len(ij):
+            i, j = zip(*ij)
+            m_i = m - sum(1 for t in i if t < m)
+            n_j = n - sum(1 for t in j if t < n)
+            return make_CostMatrix(reduce_C(Ce.C, i, j, m, n), m_i, n_j)
+        return Ce
+
+    def get_edit_ops(
+        matched_uv, pending_u, pending_v, Cv, pending_g, pending_h, Ce, matched_cost
+    ):
+        """
+        Parameters:
+            matched_uv: partial vertex edit path
+                list of tuples (u, v) of vertex mappings u<->v,
+                u=None or v=None for deletion/insertion
+            pending_u, pending_v: lists of vertices not yet mapped
+            Cv: CostMatrix of pending vertex mappings
+            pending_g, pending_h: lists of edges not yet mapped
+            Ce: CostMatrix of pending edge mappings
+            matched_cost: cost of partial edit path
+
+        Returns:
+            sequence of
+                (i, j): indices of vertex mapping u<->v
+                Cv_ij: reduced CostMatrix of pending vertex mappings
+                    (basically Cv with row i, col j removed)
+                list of (x, y): indices of edge mappings g<->h
+                Ce_xy: reduced CostMatrix of pending edge mappings
+                    (basically Ce with rows x, cols y removed)
+                cost: total cost of edit operation
+            NOTE: most promising ops first
+        """
+        m = len(pending_u)
+        n = len(pending_v)
+        # assert Cv.C.shape == (m + n, m + n)
+
+        # 1) a vertex mapping from optimal linear sum assignment
+        i, j = min(
+            (k, l) for k, l in zip(Cv.lsa_row_ind, Cv.lsa_col_ind) if k < m or l < n
+        )
+        xy, localCe = match_edges(
+            pending_u[i] if i < m else None,
+            pending_v[j] if j < n else None,
+            pending_g,
+            pending_h,
+            Ce,
+            matched_uv,
+        )
+        Ce_xy = reduce_Ce(Ce, xy, len(pending_g), len(pending_h))
+        # assert Ce.ls <= localCe.ls + Ce_xy.ls
+        if prune(matched_cost + Cv.ls + localCe.ls + Ce_xy.ls):
+            pass
+        else:
+            # get reduced Cv efficiently
+            Cv_ij = CostMatrix(
+                reduce_C(Cv.C, (i,), (j,), m, n),
+                reduce_ind(Cv.lsa_row_ind, (i, m + j)),
+                reduce_ind(Cv.lsa_col_ind, (j, n + i)),
+                Cv.ls - Cv.C[i, j],
+            )
+            yield (i, j), Cv_ij, xy, Ce_xy, Cv.C[i, j] + localCe.ls
+
+        # 2) other candidates, sorted by lower-bound cost estimate
+        other = []
+        fixed_i, fixed_j = i, j
+        if m <= n:
+            candidates = (
+                (t, fixed_j)
+                for t in range(m + n)
+                if t != fixed_i and (t < m or t == m + fixed_j)
+            )
+        else:
+            candidates = (
+                (fixed_i, t)
+                for t in range(m + n)
+                if t != fixed_j and (t < n or t == n + fixed_i)
+            )
+        for i, j in candidates:
+            if prune(matched_cost + Cv.C[i, j] + Ce.ls):
+                continue
+            Cv_ij = make_CostMatrix(
+                reduce_C(Cv.C, (i,), (j,), m, n),
+                m - 1 if i < m else m,
+                n - 1 if j < n else n,
+            )
+            # assert Cv.ls <= Cv.C[i, j] + Cv_ij.ls
+            if prune(matched_cost + Cv.C[i, j] + Cv_ij.ls + Ce.ls):
+                continue
+            xy, localCe = match_edges(
+                pending_u[i] if i < m else None,
+                pending_v[j] if j < n else None,
+                pending_g,
+                pending_h,
+                Ce,
+                matched_uv,
+            )
+            if prune(matched_cost + Cv.C[i, j] + Cv_ij.ls + localCe.ls):
+                continue
+            Ce_xy = reduce_Ce(Ce, xy, len(pending_g), len(pending_h))
+            # assert Ce.ls <= localCe.ls + Ce_xy.ls
+            if prune(matched_cost + Cv.C[i, j] + Cv_ij.ls + localCe.ls + Ce_xy.ls):
+                continue
+            other.append(((i, j), Cv_ij, xy, Ce_xy, Cv.C[i, j] + localCe.ls))
+
+        yield from sorted(other, key=lambda t: t[4] + t[1].ls + t[3].ls)
+
+    def get_edit_paths(
+        matched_uv,
+        pending_u,
+        pending_v,
+        Cv,
+        matched_gh,
+        pending_g,
+        pending_h,
+        Ce,
+        matched_cost,
+    ):
+        """
+        Parameters:
+            matched_uv: partial vertex edit path
+                list of tuples (u, v) of vertex mappings u<->v,
+                u=None or v=None for deletion/insertion
+            pending_u, pending_v: lists of vertices not yet mapped
+            Cv: CostMatrix of pending vertex mappings
+            matched_gh: partial edge edit path
+                list of tuples (g, h) of edge mappings g<->h,
+                g=None or h=None for deletion/insertion
+            pending_g, pending_h: lists of edges not yet mapped
+            Ce: CostMatrix of pending edge mappings
+            matched_cost: cost of partial edit path
+
+        Returns:
+            sequence of (vertex_path, edge_path, cost)
+                vertex_path: complete vertex edit path
+                    list of tuples (u, v) of vertex mappings u<->v,
+                    u=None or v=None for deletion/insertion
+                edge_path: complete edge edit path
+                    list of tuples (g, h) of edge mappings g<->h,
+                    g=None or h=None for deletion/insertion
+                cost: total cost of edit path
+            NOTE: path costs are non-increasing
+        """
+        if prune(matched_cost + Cv.ls + Ce.ls):
+            return
+
+        if not max(len(pending_u), len(pending_v)):
+            # assert not len(pending_g)
+            # assert not len(pending_h)
+            # path completed!
+            # assert matched_cost <= maxcost_value
+            nonlocal maxcost_value
+            maxcost_value = min(maxcost_value, matched_cost)
+            yield matched_uv, matched_gh, matched_cost
+
+        else:
+            edit_ops = get_edit_ops(
+                matched_uv,
+                pending_u,
+                pending_v,
+                Cv,
+                pending_g,
+                pending_h,
+                Ce,
+                matched_cost,
+            )
+            for ij, Cv_ij, xy, Ce_xy, edit_cost in edit_ops:
+                i, j = ij
+                # assert Cv.C[i, j] + sum(Ce.C[t] for t in xy) == edit_cost
+                if prune(matched_cost + edit_cost + Cv_ij.ls + Ce_xy.ls):
+                    continue
+
+                # dive deeper
+                u = pending_u.pop(i) if i < len(pending_u) else None
+                v = pending_v.pop(j) if j < len(pending_v) else None
+                matched_uv.append((u, v))
+                for x, y in xy:
+                    len_g = len(pending_g)
+                    len_h = len(pending_h)
+                    matched_gh.append(
+                        (
+                            pending_g[x] if x < len_g else None,
+                            pending_h[y] if y < len_h else None,
+                        )
+                    )
+                sortedx = sorted(x for x, y in xy)
+                sortedy = sorted(y for x, y in xy)
+                G = [
+                    (pending_g.pop(x) if x < len(pending_g) else None)
+                    for x in reversed(sortedx)
+                ]
+                H = [
+                    (pending_h.pop(y) if y < len(pending_h) else None)
+                    for y in reversed(sortedy)
+                ]
+
+                yield from get_edit_paths(
+                    matched_uv,
+                    pending_u,
+                    pending_v,
+                    Cv_ij,
+                    matched_gh,
+                    pending_g,
+                    pending_h,
+                    Ce_xy,
+                    matched_cost + edit_cost,
+                )
+
+                # backtrack
+                if u is not None:
+                    pending_u.insert(i, u)
+                if v is not None:
+                    pending_v.insert(j, v)
+                matched_uv.pop()
+                for x, g in zip(sortedx, reversed(G)):
+                    if g is not None:
+                        pending_g.insert(x, g)
+                for y, h in zip(sortedy, reversed(H)):
+                    if h is not None:
+                        pending_h.insert(y, h)
+                for _ in xy:
+                    matched_gh.pop()
+
+    # Initialization
+
+    pending_u = list(G1.nodes)
+    pending_v = list(G2.nodes)
+
+    initial_cost = 0
+    if roots:
+        root_u, root_v = roots
+        if root_u not in pending_u or root_v not in pending_v:
+            raise nx.NodeNotFound("Root node not in graph.")
+
+        # remove roots from pending
+        pending_u.remove(root_u)
+        pending_v.remove(root_v)
+
+    # cost matrix of vertex mappings
+    m = len(pending_u)
+    n = len(pending_v)
+    C = np.zeros((m + n, m + n))
+    if node_subst_cost:
+        C[0:m, 0:n] = np.array(
+            [
+                node_subst_cost(G1.nodes[u], G2.nodes[v])
+                for u in pending_u
+                for v in pending_v
+            ]
+        ).reshape(m, n)
+        if roots:
+            initial_cost = node_subst_cost(G1.nodes[root_u], G2.nodes[root_v])
+    elif node_match:
+        C[0:m, 0:n] = np.array(
+            [
+                1 - int(node_match(G1.nodes[u], G2.nodes[v]))
+                for u in pending_u
+                for v in pending_v
+            ]
+        ).reshape(m, n)
+        if roots:
+            initial_cost = 1 - node_match(G1.nodes[root_u], G2.nodes[root_v])
+    else:
+        # all zeroes
+        pass
+    # assert not min(m, n) or C[0:m, 0:n].min() >= 0
+    if node_del_cost:
+        del_costs = [node_del_cost(G1.nodes[u]) for u in pending_u]
+    else:
+        del_costs = [1] * len(pending_u)
+    # assert not m or min(del_costs) >= 0
+    if node_ins_cost:
+        ins_costs = [node_ins_cost(G2.nodes[v]) for v in pending_v]
+    else:
+        ins_costs = [1] * len(pending_v)
+    # assert not n or min(ins_costs) >= 0
+    inf = C[0:m, 0:n].sum() + sum(del_costs) + sum(ins_costs) + 1
+    C[0:m, n : n + m] = np.array(
+        [del_costs[i] if i == j else inf for i in range(m) for j in range(m)]
+    ).reshape(m, m)
+    C[m : m + n, 0:n] = np.array(
+        [ins_costs[i] if i == j else inf for i in range(n) for j in range(n)]
+    ).reshape(n, n)
+    Cv = make_CostMatrix(C, m, n)
+
+    pending_g = list(G1.edges)
+    pending_h = list(G2.edges)
+
+    # cost matrix of edge mappings
+    m = len(pending_g)
+    n = len(pending_h)
+    C = np.zeros((m + n, m + n))
+    if edge_subst_cost:
+        C[0:m, 0:n] = np.array(
+            [
+                edge_subst_cost(G1.edges[g], G2.edges[h])
+                for g in pending_g
+                for h in pending_h
+            ]
+        ).reshape(m, n)
+    elif edge_match:
+        C[0:m, 0:n] = np.array(
+            [
+                1 - int(edge_match(G1.edges[g], G2.edges[h]))
+                for g in pending_g
+                for h in pending_h
+            ]
+        ).reshape(m, n)
+    else:
+        # all zeroes
+        pass
+    # assert not min(m, n) or C[0:m, 0:n].min() >= 0
+    if edge_del_cost:
+        del_costs = [edge_del_cost(G1.edges[g]) for g in pending_g]
+    else:
+        del_costs = [1] * len(pending_g)
+    # assert not m or min(del_costs) >= 0
+    if edge_ins_cost:
+        ins_costs = [edge_ins_cost(G2.edges[h]) for h in pending_h]
+    else:
+        ins_costs = [1] * len(pending_h)
+    # assert not n or min(ins_costs) >= 0
+    inf = C[0:m, 0:n].sum() + sum(del_costs) + sum(ins_costs) + 1
+    C[0:m, n : n + m] = np.array(
+        [del_costs[i] if i == j else inf for i in range(m) for j in range(m)]
+    ).reshape(m, m)
+    C[m : m + n, 0:n] = np.array(
+        [ins_costs[i] if i == j else inf for i in range(n) for j in range(n)]
+    ).reshape(n, n)
+    Ce = make_CostMatrix(C, m, n)
+
+    maxcost_value = Cv.C.sum() + Ce.C.sum() + 1
+
+    if timeout is not None:
+        if timeout <= 0:
+            raise nx.NetworkXError("Timeout value must be greater than 0")
+        start = time.perf_counter()
+
+    def prune(cost):
+        if timeout is not None:
+            if time.perf_counter() - start > timeout:
+                return True
+        if upper_bound is not None:
+            if cost > upper_bound:
+                return True
+        if cost > maxcost_value:
+            return True
+        if strictly_decreasing and cost >= maxcost_value:
+            return True
+        return False
+
+    # Now go!
+
+    done_uv = [] if roots is None else [roots]
+
+    for vertex_path, edge_path, cost in get_edit_paths(
+        done_uv, pending_u, pending_v, Cv, [], pending_g, pending_h, Ce, initial_cost
+    ):
+        # assert sorted(G1.nodes) == sorted(u for u, v in vertex_path if u is not None)
+        # assert sorted(G2.nodes) == sorted(v for u, v in vertex_path if v is not None)
+        # assert sorted(G1.edges) == sorted(g for g, h in edge_path if g is not None)
+        # assert sorted(G2.edges) == sorted(h for g, h in edge_path if h is not None)
+        # print(vertex_path, edge_path, cost, file = sys.stderr)
+        # assert cost == maxcost_value
+        yield list(vertex_path), list(edge_path), float(cost)
+
+
+@nx._dispatchable
+def simrank_similarity(
+    G,
+    source=None,
+    target=None,
+    importance_factor=0.9,
+    max_iterations=1000,
+    tolerance=1e-4,
+):
+    """Returns the SimRank similarity of nodes in the graph ``G``.
+
+    SimRank is a similarity metric that says "two objects are considered
+    to be similar if they are referenced by similar objects." [1]_.
+
+    The pseudo-code definition from the paper is::
+
+        def simrank(G, u, v):
+            in_neighbors_u = G.predecessors(u)
+            in_neighbors_v = G.predecessors(v)
+            scale = C / (len(in_neighbors_u) * len(in_neighbors_v))
+            return scale * sum(
+                simrank(G, w, x) for w, x in product(in_neighbors_u, in_neighbors_v)
+            )
+
+    where ``G`` is the graph, ``u`` is the source, ``v`` is the target,
+    and ``C`` is a float decay or importance factor between 0 and 1.
+
+    The SimRank algorithm for determining node similarity is defined in
+    [2]_.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A NetworkX graph
+
+    source : node
+        If this is specified, the returned dictionary maps each node
+        ``v`` in the graph to the similarity between ``source`` and
+        ``v``.
+
+    target : node
+        If both ``source`` and ``target`` are specified, the similarity
+        value between ``source`` and ``target`` is returned. If
+        ``target`` is specified but ``source`` is not, this argument is
+        ignored.
+
+    importance_factor : float
+        The relative importance of indirect neighbors with respect to
+        direct neighbors.
+
+    max_iterations : integer
+        Maximum number of iterations.
+
+    tolerance : float
+        Error tolerance used to check convergence. When an iteration of
+        the algorithm finds that no similarity value changes more than
+        this amount, the algorithm halts.
+
+    Returns
+    -------
+    similarity : dictionary or float
+        If ``source`` and ``target`` are both ``None``, this returns a
+        dictionary of dictionaries, where keys are node pairs and value
+        are similarity of the pair of nodes.
+
+        If ``source`` is not ``None`` but ``target`` is, this returns a
+        dictionary mapping node to the similarity of ``source`` and that
+        node.
+
+        If neither ``source`` nor ``target`` is ``None``, this returns
+        the similarity value for the given pair of nodes.
+
+    Raises
+    ------
+    ExceededMaxIterations
+        If the algorithm does not converge within ``max_iterations``.
+
+    NodeNotFound
+        If either ``source`` or ``target`` is not in `G`.
+
+    Examples
+    --------
+    >>> G = nx.cycle_graph(2)
+    >>> nx.simrank_similarity(G)
+    {0: {0: 1.0, 1: 0.0}, 1: {0: 0.0, 1: 1.0}}
+    >>> nx.simrank_similarity(G, source=0)
+    {0: 1.0, 1: 0.0}
+    >>> nx.simrank_similarity(G, source=0, target=0)
+    1.0
+
+    The result of this function can be converted to a numpy array
+    representing the SimRank matrix by using the node order of the
+    graph to determine which row and column represent each node.
+    Other ordering of nodes is also possible.
+
+    >>> import numpy as np
+    >>> sim = nx.simrank_similarity(G)
+    >>> np.array([[sim[u][v] for v in G] for u in G])
+    array([[1., 0.],
+           [0., 1.]])
+    >>> sim_1d = nx.simrank_similarity(G, source=0)
+    >>> np.array([sim[0][v] for v in G])
+    array([1., 0.])
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/SimRank
+    .. [2] G. Jeh and J. Widom.
+           "SimRank: a measure of structural-context similarity",
+           In KDD'02: Proceedings of the Eighth ACM SIGKDD
+           International Conference on Knowledge Discovery and Data Mining,
+           pp. 538--543. ACM Press, 2002.
+    """
+    import numpy as np
+
+    nodelist = list(G)
+    if source is not None:
+        if source not in nodelist:
+            raise nx.NodeNotFound(f"Source node {source} not in G")
+        else:
+            s_indx = nodelist.index(source)
+    else:
+        s_indx = None
+
+    if target is not None:
+        if target not in nodelist:
+            raise nx.NodeNotFound(f"Target node {target} not in G")
+        else:
+            t_indx = nodelist.index(target)
+    else:
+        t_indx = None
+
+    x = _simrank_similarity_numpy(
+        G, s_indx, t_indx, importance_factor, max_iterations, tolerance
+    )
+
+    if isinstance(x, np.ndarray):
+        if x.ndim == 1:
+            return dict(zip(G, x.tolist()))
+        # else x.ndim == 2
+        return {u: dict(zip(G, row)) for u, row in zip(G, x.tolist())}
+    return float(x)
+
+
+def _simrank_similarity_python(
+    G,
+    source=None,
+    target=None,
+    importance_factor=0.9,
+    max_iterations=1000,
+    tolerance=1e-4,
+):
+    """Returns the SimRank similarity of nodes in the graph ``G``.
+
+    This pure Python version is provided for pedagogical purposes.
+
+    Examples
+    --------
+    >>> G = nx.cycle_graph(2)
+    >>> nx.similarity._simrank_similarity_python(G)
+    {0: {0: 1, 1: 0.0}, 1: {0: 0.0, 1: 1}}
+    >>> nx.similarity._simrank_similarity_python(G, source=0)
+    {0: 1, 1: 0.0}
+    >>> nx.similarity._simrank_similarity_python(G, source=0, target=0)
+    1
+    """
+    # build up our similarity adjacency dictionary output
+    newsim = {u: {v: 1 if u == v else 0 for v in G} for u in G}
+
+    # These functions compute the update to the similarity value of the nodes
+    # `u` and `v` with respect to the previous similarity values.
+    def avg_sim(s):
+        return sum(newsim[w][x] for (w, x) in s) / len(s) if s else 0.0
+
+    Gadj = G.pred if G.is_directed() else G.adj
+
+    def sim(u, v):
+        return importance_factor * avg_sim(list(product(Gadj[u], Gadj[v])))
+
+    for its in range(max_iterations):
+        oldsim = newsim
+        newsim = {u: {v: sim(u, v) if u != v else 1 for v in G} for u in G}
+        is_close = all(
+            all(
+                abs(newsim[u][v] - old) <= tolerance * (1 + abs(old))
+                for v, old in nbrs.items()
+            )
+            for u, nbrs in oldsim.items()
+        )
+        if is_close:
+            break
+
+    if its + 1 == max_iterations:
+        raise nx.ExceededMaxIterations(
+            f"simrank did not converge after {max_iterations} iterations."
+        )
+
+    if source is not None and target is not None:
+        return newsim[source][target]
+    if source is not None:
+        return newsim[source]
+    return newsim
+
+
+def _simrank_similarity_numpy(
+    G,
+    source=None,
+    target=None,
+    importance_factor=0.9,
+    max_iterations=1000,
+    tolerance=1e-4,
+):
+    """Calculate SimRank of nodes in ``G`` using matrices with ``numpy``.
+
+    The SimRank algorithm for determining node similarity is defined in
+    [1]_.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A NetworkX graph
+
+    source : node
+        If this is specified, the returned dictionary maps each node
+        ``v`` in the graph to the similarity between ``source`` and
+        ``v``.
+
+    target : node
+        If both ``source`` and ``target`` are specified, the similarity
+        value between ``source`` and ``target`` is returned. If
+        ``target`` is specified but ``source`` is not, this argument is
+        ignored.
+
+    importance_factor : float
+        The relative importance of indirect neighbors with respect to
+        direct neighbors.
+
+    max_iterations : integer
+        Maximum number of iterations.
+
+    tolerance : float
+        Error tolerance used to check convergence. When an iteration of
+        the algorithm finds that no similarity value changes more than
+        this amount, the algorithm halts.
+
+    Returns
+    -------
+    similarity : numpy array or float
+        If ``source`` and ``target`` are both ``None``, this returns a
+        2D array containing SimRank scores of the nodes.
+
+        If ``source`` is not ``None`` but ``target`` is, this returns an
+        1D array containing SimRank scores of ``source`` and that
+        node.
+
+        If neither ``source`` nor ``target`` is ``None``, this returns
+        the similarity value for the given pair of nodes.
+
+    Examples
+    --------
+    >>> G = nx.cycle_graph(2)
+    >>> nx.similarity._simrank_similarity_numpy(G)
+    array([[1., 0.],
+           [0., 1.]])
+    >>> nx.similarity._simrank_similarity_numpy(G, source=0)
+    array([1., 0.])
+    >>> nx.similarity._simrank_similarity_numpy(G, source=0, target=0)
+    1.0
+
+    References
+    ----------
+    .. [1] G. Jeh and J. Widom.
+           "SimRank: a measure of structural-context similarity",
+           In KDD'02: Proceedings of the Eighth ACM SIGKDD
+           International Conference on Knowledge Discovery and Data Mining,
+           pp. 538--543. ACM Press, 2002.
+    """
+    # This algorithm follows roughly
+    #
+    #     S = max{C * (A.T * S * A), I}
+    #
+    # where C is the importance factor, A is the column normalized
+    # adjacency matrix, and I is the identity matrix.
+    import numpy as np
+
+    adjacency_matrix = nx.to_numpy_array(G)
+
+    # column-normalize the ``adjacency_matrix``
+    s = np.array(adjacency_matrix.sum(axis=0))
+    s[s == 0] = 1
+    adjacency_matrix /= s  # adjacency_matrix.sum(axis=0)
+
+    newsim = np.eye(len(G), dtype=np.float64)
+    for its in range(max_iterations):
+        prevsim = newsim.copy()
+        newsim = importance_factor * ((adjacency_matrix.T @ prevsim) @ adjacency_matrix)
+        np.fill_diagonal(newsim, 1.0)
+
+        if np.allclose(prevsim, newsim, atol=tolerance):
+            break
+
+    if its + 1 == max_iterations:
+        raise nx.ExceededMaxIterations(
+            f"simrank did not converge after {max_iterations} iterations."
+        )
+
+    if source is not None and target is not None:
+        return float(newsim[source, target])
+    if source is not None:
+        return newsim[source]
+    return newsim
+
+
+@nx._dispatchable(edge_attrs="weight")
+def panther_similarity(
+    G, source, k=5, path_length=5, c=0.5, delta=0.1, eps=None, weight="weight"
+):
+    r"""Returns the Panther similarity of nodes in the graph `G` to node ``v``.
+
+    Panther is a similarity metric that says "two objects are considered
+    to be similar if they frequently appear on the same paths." [1]_.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A NetworkX graph
+    source : node
+        Source node for which to find the top `k` similar other nodes
+    k : int (default = 5)
+        The number of most similar nodes to return.
+    path_length : int (default = 5)
+        How long the randomly generated paths should be (``T`` in [1]_)
+    c : float (default = 0.5)
+        A universal positive constant used to scale the number
+        of sample random paths to generate.
+    delta : float (default = 0.1)
+        The probability that the similarity $S$ is not an epsilon-approximation to (R, phi),
+        where $R$ is the number of random paths and $\phi$ is the probability
+        that an element sampled from a set $A \subseteq D$, where $D$ is the domain.
+    eps : float or None (default = None)
+        The error bound. Per [1]_, a good value is ``sqrt(1/|E|)``. Therefore,
+        if no value is provided, the recommended computed value will be used.
+    weight : string or None, optional (default="weight")
+        The name of an edge attribute that holds the numerical value
+        used as a weight. If None then each edge has weight 1.
+
+    Returns
+    -------
+    similarity : dictionary
+        Dictionary of nodes to similarity scores (as floats). Note:
+        the self-similarity (i.e., ``v``) will not be included in
+        the returned dictionary. So, for ``k = 5``, a dictionary of
+        top 4 nodes and their similarity scores will be returned.
+
+    Raises
+    ------
+    NetworkXUnfeasible
+        If `source` is an isolated node.
+
+    NodeNotFound
+        If `source` is not in `G`.
+
+    Notes
+    -----
+        The isolated nodes in `G` are ignored.
+
+    Examples
+    --------
+    >>> G = nx.star_graph(10)
+    >>> sim = nx.panther_similarity(G, 0)
+
+    References
+    ----------
+    .. [1] Zhang, J., Tang, J., Ma, C., Tong, H., Jing, Y., & Li, J.
+           Panther: Fast top-k similarity search on large networks.
+           In Proceedings of the ACM SIGKDD International Conference
+           on Knowledge Discovery and Data Mining (Vol. 2015-August, pp. 1445–1454).
+           Association for Computing Machinery. https://doi.org/10.1145/2783258.2783267.
+    """
+    import numpy as np
+
+    if source not in G:
+        raise nx.NodeNotFound(f"Source node {source} not in G")
+
+    isolates = set(nx.isolates(G))
+
+    if source in isolates:
+        raise nx.NetworkXUnfeasible(
+            f"Panther similarity is not defined for the isolated source node {source}."
+        )
+
+    G = G.subgraph([node for node in G.nodes if node not in isolates]).copy()
+
+    num_nodes = G.number_of_nodes()
+    if num_nodes < k:
+        warnings.warn(
+            f"Number of nodes is {num_nodes}, but requested k is {k}. "
+            "Setting k to number of nodes."
+        )
+        k = num_nodes
+    # According to [1], they empirically determined
+    # a good value for ``eps`` to be sqrt( 1 / |E| )
+    if eps is None:
+        eps = np.sqrt(1.0 / G.number_of_edges())
+
+    inv_node_map = {name: index for index, name in enumerate(G.nodes)}
+    node_map = np.array(G)
+
+    # Calculate the sample size ``R`` for how many paths
+    # to randomly generate
+    t_choose_2 = math.comb(path_length, 2)
+    sample_size = int((c / eps**2) * (np.log2(t_choose_2) + 1 + np.log(1 / delta)))
+    index_map = {}
+    _ = list(
+        generate_random_paths(
+            G, sample_size, path_length=path_length, index_map=index_map, weight=weight
+        )
+    )
+    S = np.zeros(num_nodes)
+
+    inv_sample_size = 1 / sample_size
+
+    source_paths = set(index_map[source])
+
+    # Calculate the path similarities
+    # between ``source`` (v) and ``node`` (v_j)
+    # using our inverted index mapping of
+    # vertices to paths
+    for node, paths in index_map.items():
+        # Only consider paths where both
+        # ``node`` and ``source`` are present
+        common_paths = source_paths.intersection(paths)
+        S[inv_node_map[node]] = len(common_paths) * inv_sample_size
+
+    # Retrieve top ``k`` similar
+    # Note: the below performed anywhere from 4-10x faster
+    # (depending on input sizes) vs the equivalent ``np.argsort(S)[::-1]``
+    top_k_unsorted = np.argpartition(S, -k)[-k:]
+    top_k_sorted = top_k_unsorted[np.argsort(S[top_k_unsorted])][::-1]
+
+    # Add back the similarity scores
+    top_k_with_val = dict(
+        zip(node_map[top_k_sorted].tolist(), S[top_k_sorted].tolist())
+    )
+
+    # Remove the self-similarity
+    top_k_with_val.pop(source, None)
+    return top_k_with_val
+
+
+@np_random_state(5)
+@nx._dispatchable(edge_attrs="weight")
+def generate_random_paths(
+    G,
+    sample_size,
+    path_length=5,
+    index_map=None,
+    weight="weight",
+    seed=None,
+    *,
+    source=None,
+):
+    """Randomly generate `sample_size` paths of length `path_length`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A NetworkX graph
+    sample_size : integer
+        The number of paths to generate. This is ``R`` in [1]_.
+    path_length : integer (default = 5)
+        The maximum size of the path to randomly generate.
+        This is ``T`` in [1]_. According to the paper, ``T >= 5`` is
+        recommended.
+    index_map : dictionary, optional
+        If provided, this will be populated with the inverted
+        index of nodes mapped to the set of generated random path
+        indices within ``paths``.
+    weight : string or None, optional (default="weight")
+        The name of an edge attribute that holds the numerical value
+        used as a weight. If None then each edge has weight 1.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    source : node, optional
+        Node to use as the starting point for all generated paths.
+        If None then starting nodes are selected at random with uniform probability.
+
+    Returns
+    -------
+    paths : generator of lists
+        Generator of `sample_size` paths each with length `path_length`.
+
+    Examples
+    --------
+    The generator yields `sample_size` number of paths of length `path_length`
+    drawn from `G`:
+
+    >>> G = nx.complete_graph(5)
+    >>> next(nx.generate_random_paths(G, sample_size=1, path_length=3, seed=42))
+    [3, 4, 2, 3]
+    >>> list(nx.generate_random_paths(G, sample_size=3, path_length=4, seed=42))
+    [[3, 4, 2, 3, 0], [2, 0, 2, 1, 0], [2, 0, 4, 3, 0]]
+
+    By passing a dictionary into `index_map`, it will build an
+    inverted index mapping of nodes to the paths in which that node is present:
+
+    >>> G = nx.wheel_graph(10)
+    >>> index_map = {}
+    >>> random_paths = list(
+    ...     nx.generate_random_paths(G, sample_size=3, index_map=index_map, seed=2771)
+    ... )
+    >>> random_paths
+    [[3, 2, 1, 9, 8, 7], [4, 0, 5, 6, 7, 8], [3, 0, 5, 0, 9, 8]]
+    >>> paths_containing_node_0 = [
+    ...     random_paths[path_idx] for path_idx in index_map.get(0, [])
+    ... ]
+    >>> paths_containing_node_0
+    [[4, 0, 5, 6, 7, 8], [3, 0, 5, 0, 9, 8]]
+
+    References
+    ----------
+    .. [1] Zhang, J., Tang, J., Ma, C., Tong, H., Jing, Y., & Li, J.
+           Panther: Fast top-k similarity search on large networks.
+           In Proceedings of the ACM SIGKDD International Conference
+           on Knowledge Discovery and Data Mining (Vol. 2015-August, pp. 1445–1454).
+           Association for Computing Machinery. https://doi.org/10.1145/2783258.2783267.
+    """
+    import numpy as np
+
+    randint_fn = (
+        seed.integers if isinstance(seed, np.random.Generator) else seed.randint
+    )
+
+    # Calculate transition probabilities between
+    # every pair of vertices according to Eq. (3)
+    adj_mat = nx.to_numpy_array(G, weight=weight)
+    inv_row_sums = np.reciprocal(adj_mat.sum(axis=1)).reshape(-1, 1)
+    transition_probabilities = adj_mat * inv_row_sums
+
+    node_map = list(G)
+    num_nodes = G.number_of_nodes()
+
+    for path_index in range(sample_size):
+        if source is None:
+            # Sample current vertex v = v_i uniformly at random
+            node_index = randint_fn(num_nodes)
+            node = node_map[node_index]
+        else:
+            if source not in node_map:
+                raise nx.NodeNotFound(f"Initial node {source} not in G")
+
+            node = source
+            node_index = node_map.index(node)
+
+        # Add v into p_r and add p_r into the path set
+        # of v, i.e., P_v
+        path = [node]
+
+        # Build the inverted index (P_v) of vertices to paths
+        if index_map is not None:
+            if node in index_map:
+                index_map[node].add(path_index)
+            else:
+                index_map[node] = {path_index}
+
+        starting_index = node_index
+        for _ in range(path_length):
+            # Randomly sample a neighbor (v_j) according
+            # to transition probabilities from ``node`` (v) to its neighbors
+            nbr_index = seed.choice(
+                num_nodes, p=transition_probabilities[starting_index]
+            )
+
+            # Set current vertex (v = v_j)
+            starting_index = nbr_index
+
+            # Add v into p_r
+            nbr_node = node_map[nbr_index]
+            path.append(nbr_node)
+
+            # Add p_r into P_v
+            if index_map is not None:
+                if nbr_node in index_map:
+                    index_map[nbr_node].add(path_index)
+                else:
+                    index_map[nbr_node] = {path_index}
+
+        yield path
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/simple_paths.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/simple_paths.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8cf1d105dc52990096b642c04532b1fbb6dc082
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/simple_paths.py
@@ -0,0 +1,948 @@
+from heapq import heappop, heappush
+from itertools import count
+
+import networkx as nx
+from networkx.algorithms.shortest_paths.weighted import _weight_function
+from networkx.utils import not_implemented_for, pairwise
+
+__all__ = [
+    "all_simple_paths",
+    "is_simple_path",
+    "shortest_simple_paths",
+    "all_simple_edge_paths",
+]
+
+
+@nx._dispatchable
+def is_simple_path(G, nodes):
+    """Returns True if and only if `nodes` form a simple path in `G`.
+
+    A *simple path* in a graph is a nonempty sequence of nodes in which
+    no node appears more than once in the sequence, and each adjacent
+    pair of nodes in the sequence is adjacent in the graph.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+    nodes : list
+        A list of one or more nodes in the graph `G`.
+
+    Returns
+    -------
+    bool
+        Whether the given list of nodes represents a simple path in `G`.
+
+    Notes
+    -----
+    An empty list of nodes is not a path but a list of one node is a
+    path. Here's an explanation why.
+
+    This function operates on *node paths*. One could also consider
+    *edge paths*. There is a bijection between node paths and edge
+    paths.
+
+    The *length of a path* is the number of edges in the path, so a list
+    of nodes of length *n* corresponds to a path of length *n* - 1.
+    Thus the smallest edge path would be a list of zero edges, the empty
+    path. This corresponds to a list of one node.
+
+    To convert between a node path and an edge path, you can use code
+    like the following::
+
+        >>> from networkx.utils import pairwise
+        >>> nodes = [0, 1, 2, 3]
+        >>> edges = list(pairwise(nodes))
+        >>> edges
+        [(0, 1), (1, 2), (2, 3)]
+        >>> nodes = [edges[0][0]] + [v for u, v in edges]
+        >>> nodes
+        [0, 1, 2, 3]
+
+    Examples
+    --------
+    >>> G = nx.cycle_graph(4)
+    >>> nx.is_simple_path(G, [2, 3, 0])
+    True
+    >>> nx.is_simple_path(G, [0, 2])
+    False
+
+    """
+    # The empty list is not a valid path. Could also return
+    # NetworkXPointlessConcept here.
+    if len(nodes) == 0:
+        return False
+
+    # If the list is a single node, just check that the node is actually
+    # in the graph.
+    if len(nodes) == 1:
+        return nodes[0] in G
+
+    # check that all nodes in the list are in the graph, if at least one
+    # is not in the graph, then this is not a simple path
+    if not all(n in G for n in nodes):
+        return False
+
+    # If the list contains repeated nodes, then it's not a simple path
+    if len(set(nodes)) != len(nodes):
+        return False
+
+    # Test that each adjacent pair of nodes is adjacent.
+    return all(v in G[u] for u, v in pairwise(nodes))
+
+
+@nx._dispatchable
+def all_simple_paths(G, source, target, cutoff=None):
+    """Generate all simple paths in the graph G from source to target.
+
+    A simple path is a path with no repeated nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path
+
+    target : nodes
+       Single node or iterable of nodes at which to end path
+
+    cutoff : integer, optional
+        Depth to stop the search. Only paths of length <= cutoff are returned.
+
+    Returns
+    -------
+    path_generator: generator
+       A generator that produces lists of simple paths.  If there are no paths
+       between the source and target within the given cutoff the generator
+       produces no output. If it is possible to traverse the same sequence of
+       nodes in multiple ways, namely through parallel edges, then it will be
+       returned multiple times (once for each viable edge combination).
+
+    Examples
+    --------
+    This iterator generates lists of nodes::
+
+        >>> G = nx.complete_graph(4)
+        >>> for path in nx.all_simple_paths(G, source=0, target=3):
+        ...     print(path)
+        ...
+        [0, 1, 2, 3]
+        [0, 1, 3]
+        [0, 2, 1, 3]
+        [0, 2, 3]
+        [0, 3]
+
+    You can generate only those paths that are shorter than a certain
+    length by using the `cutoff` keyword argument::
+
+        >>> paths = nx.all_simple_paths(G, source=0, target=3, cutoff=2)
+        >>> print(list(paths))
+        [[0, 1, 3], [0, 2, 3], [0, 3]]
+
+    To get each path as the corresponding list of edges, you can use the
+    :func:`networkx.utils.pairwise` helper function::
+
+        >>> paths = nx.all_simple_paths(G, source=0, target=3)
+        >>> for path in map(nx.utils.pairwise, paths):
+        ...     print(list(path))
+        [(0, 1), (1, 2), (2, 3)]
+        [(0, 1), (1, 3)]
+        [(0, 2), (2, 1), (1, 3)]
+        [(0, 2), (2, 3)]
+        [(0, 3)]
+
+    Pass an iterable of nodes as target to generate all paths ending in any of several nodes::
+
+        >>> G = nx.complete_graph(4)
+        >>> for path in nx.all_simple_paths(G, source=0, target=[3, 2]):
+        ...     print(path)
+        ...
+        [0, 1, 2]
+        [0, 1, 2, 3]
+        [0, 1, 3]
+        [0, 1, 3, 2]
+        [0, 2]
+        [0, 2, 1, 3]
+        [0, 2, 3]
+        [0, 3]
+        [0, 3, 1, 2]
+        [0, 3, 2]
+
+    The singleton path from ``source`` to itself is considered a simple path and is
+    included in the results:
+
+        >>> G = nx.empty_graph(5)
+        >>> list(nx.all_simple_paths(G, source=0, target=0))
+        [[0]]
+
+        >>> G = nx.path_graph(3)
+        >>> list(nx.all_simple_paths(G, source=0, target={0, 1, 2}))
+        [[0], [0, 1], [0, 1, 2]]
+
+    Iterate over each path from the root nodes to the leaf nodes in a
+    directed acyclic graph using a functional programming approach::
+
+        >>> from itertools import chain
+        >>> from itertools import product
+        >>> from itertools import starmap
+        >>> from functools import partial
+        >>>
+        >>> chaini = chain.from_iterable
+        >>>
+        >>> G = nx.DiGraph([(0, 1), (1, 2), (0, 3), (3, 2)])
+        >>> roots = (v for v, d in G.in_degree() if d == 0)
+        >>> leaves = (v for v, d in G.out_degree() if d == 0)
+        >>> all_paths = partial(nx.all_simple_paths, G)
+        >>> list(chaini(starmap(all_paths, product(roots, leaves))))
+        [[0, 1, 2], [0, 3, 2]]
+
+    The same list computed using an iterative approach::
+
+        >>> G = nx.DiGraph([(0, 1), (1, 2), (0, 3), (3, 2)])
+        >>> roots = (v for v, d in G.in_degree() if d == 0)
+        >>> leaves = (v for v, d in G.out_degree() if d == 0)
+        >>> all_paths = []
+        >>> for root in roots:
+        ...     for leaf in leaves:
+        ...         paths = nx.all_simple_paths(G, root, leaf)
+        ...         all_paths.extend(paths)
+        >>> all_paths
+        [[0, 1, 2], [0, 3, 2]]
+
+    Iterate over each path from the root nodes to the leaf nodes in a
+    directed acyclic graph passing all leaves together to avoid unnecessary
+    compute::
+
+        >>> G = nx.DiGraph([(0, 1), (2, 1), (1, 3), (1, 4)])
+        >>> roots = (v for v, d in G.in_degree() if d == 0)
+        >>> leaves = [v for v, d in G.out_degree() if d == 0]
+        >>> all_paths = []
+        >>> for root in roots:
+        ...     paths = nx.all_simple_paths(G, root, leaves)
+        ...     all_paths.extend(paths)
+        >>> all_paths
+        [[0, 1, 3], [0, 1, 4], [2, 1, 3], [2, 1, 4]]
+
+    If parallel edges offer multiple ways to traverse a given sequence of
+    nodes, this sequence of nodes will be returned multiple times:
+
+        >>> G = nx.MultiDiGraph([(0, 1), (0, 1), (1, 2)])
+        >>> list(nx.all_simple_paths(G, 0, 2))
+        [[0, 1, 2], [0, 1, 2]]
+
+    Notes
+    -----
+    This algorithm uses a modified depth-first search to generate the
+    paths [1]_.  A single path can be found in $O(V+E)$ time but the
+    number of simple paths in a graph can be very large, e.g. $O(n!)$ in
+    the complete graph of order $n$.
+
+    This function does not check that a path exists between `source` and
+    `target`. For large graphs, this may result in very long runtimes.
+    Consider using `has_path` to check that a path exists between `source` and
+    `target` before calling this function on large graphs.
+
+    References
+    ----------
+    .. [1] R. Sedgewick, "Algorithms in C, Part 5: Graph Algorithms",
+       Addison Wesley Professional, 3rd ed., 2001.
+
+    See Also
+    --------
+    all_shortest_paths, shortest_path, has_path
+
+    """
+    for edge_path in all_simple_edge_paths(G, source, target, cutoff):
+        yield [source] + [edge[1] for edge in edge_path]
+
+
+@nx._dispatchable
+def all_simple_edge_paths(G, source, target, cutoff=None):
+    """Generate lists of edges for all simple paths in G from source to target.
+
+    A simple path is a path with no repeated nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path
+
+    target : nodes
+       Single node or iterable of nodes at which to end path
+
+    cutoff : integer, optional
+        Depth to stop the search. Only paths of length <= cutoff are returned.
+
+    Returns
+    -------
+    path_generator: generator
+       A generator that produces lists of simple paths.  If there are no paths
+       between the source and target within the given cutoff the generator
+       produces no output.
+       For multigraphs, the list of edges have elements of the form `(u,v,k)`.
+       Where `k` corresponds to the edge key.
+
+    Examples
+    --------
+
+    Print the simple path edges of a Graph::
+
+        >>> g = nx.Graph([(1, 2), (2, 4), (1, 3), (3, 4)])
+        >>> for path in sorted(nx.all_simple_edge_paths(g, 1, 4)):
+        ...     print(path)
+        [(1, 2), (2, 4)]
+        [(1, 3), (3, 4)]
+
+    Print the simple path edges of a MultiGraph. Returned edges come with
+    their associated keys::
+
+        >>> mg = nx.MultiGraph()
+        >>> mg.add_edge(1, 2, key="k0")
+        'k0'
+        >>> mg.add_edge(1, 2, key="k1")
+        'k1'
+        >>> mg.add_edge(2, 3, key="k0")
+        'k0'
+        >>> for path in sorted(nx.all_simple_edge_paths(mg, 1, 3)):
+        ...     print(path)
+        [(1, 2, 'k0'), (2, 3, 'k0')]
+        [(1, 2, 'k1'), (2, 3, 'k0')]
+
+    When ``source`` is one of the targets, the empty path starting and ending at
+    ``source`` without traversing any edge is considered a valid simple edge path
+    and is included in the results:
+
+        >>> G = nx.Graph()
+        >>> G.add_node(0)
+        >>> paths = list(nx.all_simple_edge_paths(G, 0, 0))
+        >>> for path in paths:
+        ...     print(path)
+        []
+        >>> len(paths)
+        1
+
+
+    Notes
+    -----
+    This algorithm uses a modified depth-first search to generate the
+    paths [1]_.  A single path can be found in $O(V+E)$ time but the
+    number of simple paths in a graph can be very large, e.g. $O(n!)$ in
+    the complete graph of order $n$.
+
+    References
+    ----------
+    .. [1] R. Sedgewick, "Algorithms in C, Part 5: Graph Algorithms",
+       Addison Wesley Professional, 3rd ed., 2001.
+
+    See Also
+    --------
+    all_shortest_paths, shortest_path, all_simple_paths
+
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"source node {source} not in graph")
+
+    if target in G:
+        targets = {target}
+    else:
+        try:
+            targets = set(target)
+        except TypeError as err:
+            raise nx.NodeNotFound(f"target node {target} not in graph") from err
+
+    cutoff = cutoff if cutoff is not None else len(G) - 1
+
+    if cutoff >= 0 and targets:
+        yield from _all_simple_edge_paths(G, source, targets, cutoff)
+
+
+def _all_simple_edge_paths(G, source, targets, cutoff):
+    # We simulate recursion with a stack, keeping the current path being explored
+    # and the outgoing edge iterators at each point in the stack.
+    # To avoid unnecessary checks, the loop is structured in a way such that a path
+    # is considered for yielding only after a new node/edge is added.
+    # We bootstrap the search by adding a dummy iterator to the stack that only yields
+    # a dummy edge to source (so that the trivial path has a chance of being included).
+
+    get_edges = (
+        (lambda node: G.edges(node, keys=True))
+        if G.is_multigraph()
+        else (lambda node: G.edges(node))
+    )
+
+    # The current_path is a dictionary that maps nodes in the path to the edge that was
+    # used to enter that node (instead of a list of edges) because we want both a fast
+    # membership test for nodes in the path and the preservation of insertion order.
+    current_path = {None: None}
+    stack = [iter([(None, source)])]
+
+    while stack:
+        # 1. Try to extend the current path.
+        next_edge = next((e for e in stack[-1] if e[1] not in current_path), None)
+        if next_edge is None:
+            # All edges of the last node in the current path have been explored.
+            stack.pop()
+            current_path.popitem()
+            continue
+        previous_node, next_node, *_ = next_edge
+
+        # 2. Check if we've reached a target.
+        if next_node in targets:
+            yield (list(current_path.values()) + [next_edge])[2:]  # remove dummy edge
+
+        # 3. Only expand the search through the next node if it makes sense.
+        if len(current_path) - 1 < cutoff and (
+            targets - current_path.keys() - {next_node}
+        ):
+            current_path[next_node] = next_edge
+            stack.append(iter(get_edges(next_node)))
+
+
+@not_implemented_for("multigraph")
+@nx._dispatchable(edge_attrs="weight")
+def shortest_simple_paths(G, source, target, weight=None):
+    """Generate all simple paths in the graph G from source to target,
+       starting from shortest ones.
+
+    A simple path is a path with no repeated nodes.
+
+    If a weighted shortest path search is to be used, no negative weights
+    are allowed.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path
+
+    target : node
+       Ending node for path
+
+    weight : string or function
+        If it is a string, it is the name of the edge attribute to be
+        used as a weight.
+
+        If it is a function, the weight of an edge is the value returned
+        by the function. The function must accept exactly three positional
+        arguments: the two endpoints of an edge and the dictionary of edge
+        attributes for that edge. The function must return a number or None.
+        The weight function can be used to hide edges by returning None.
+        So ``weight = lambda u, v, d: 1 if d['color']=="red" else None``
+        will find the shortest red path.
+
+        If None all edges are considered to have unit weight. Default
+        value None.
+
+    Returns
+    -------
+    path_generator: generator
+       A generator that produces lists of simple paths, in order from
+       shortest to longest.
+
+    Raises
+    ------
+    NetworkXNoPath
+       If no path exists between source and target.
+
+    NetworkXError
+       If source or target nodes are not in the input graph.
+
+    NetworkXNotImplemented
+       If the input graph is a Multi[Di]Graph.
+
+    Examples
+    --------
+
+    >>> G = nx.cycle_graph(7)
+    >>> paths = list(nx.shortest_simple_paths(G, 0, 3))
+    >>> print(paths)
+    [[0, 1, 2, 3], [0, 6, 5, 4, 3]]
+
+    You can use this function to efficiently compute the k shortest/best
+    paths between two nodes.
+
+    >>> from itertools import islice
+    >>> def k_shortest_paths(G, source, target, k, weight=None):
+    ...     return list(
+    ...         islice(nx.shortest_simple_paths(G, source, target, weight=weight), k)
+    ...     )
+    >>> for path in k_shortest_paths(G, 0, 3, 2):
+    ...     print(path)
+    [0, 1, 2, 3]
+    [0, 6, 5, 4, 3]
+
+    Notes
+    -----
+    This procedure is based on algorithm by Jin Y. Yen [1]_.  Finding
+    the first $K$ paths requires $O(KN^3)$ operations.
+
+    See Also
+    --------
+    all_shortest_paths
+    shortest_path
+    all_simple_paths
+
+    References
+    ----------
+    .. [1] Jin Y. Yen, "Finding the K Shortest Loopless Paths in a
+       Network", Management Science, Vol. 17, No. 11, Theory Series
+       (Jul., 1971), pp. 712-716.
+
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"source node {source} not in graph")
+
+    if target not in G:
+        raise nx.NodeNotFound(f"target node {target} not in graph")
+
+    if weight is None:
+        length_func = len
+        shortest_path_func = _bidirectional_shortest_path
+    else:
+        wt = _weight_function(G, weight)
+
+        def length_func(path):
+            return sum(
+                wt(u, v, G.get_edge_data(u, v)) for (u, v) in zip(path, path[1:])
+            )
+
+        shortest_path_func = _bidirectional_dijkstra
+
+    listA = []
+    listB = PathBuffer()
+    prev_path = None
+    while True:
+        if not prev_path:
+            length, path = shortest_path_func(G, source, target, weight=weight)
+            listB.push(length, path)
+        else:
+            ignore_nodes = set()
+            ignore_edges = set()
+            for i in range(1, len(prev_path)):
+                root = prev_path[:i]
+                root_length = length_func(root)
+                for path in listA:
+                    if path[:i] == root:
+                        ignore_edges.add((path[i - 1], path[i]))
+                try:
+                    length, spur = shortest_path_func(
+                        G,
+                        root[-1],
+                        target,
+                        ignore_nodes=ignore_nodes,
+                        ignore_edges=ignore_edges,
+                        weight=weight,
+                    )
+                    path = root[:-1] + spur
+                    listB.push(root_length + length, path)
+                except nx.NetworkXNoPath:
+                    pass
+                ignore_nodes.add(root[-1])
+
+        if listB:
+            path = listB.pop()
+            yield path
+            listA.append(path)
+            prev_path = path
+        else:
+            break
+
+
+class PathBuffer:
+    def __init__(self):
+        self.paths = set()
+        self.sortedpaths = []
+        self.counter = count()
+
+    def __len__(self):
+        return len(self.sortedpaths)
+
+    def push(self, cost, path):
+        hashable_path = tuple(path)
+        if hashable_path not in self.paths:
+            heappush(self.sortedpaths, (cost, next(self.counter), path))
+            self.paths.add(hashable_path)
+
+    def pop(self):
+        (cost, num, path) = heappop(self.sortedpaths)
+        hashable_path = tuple(path)
+        self.paths.remove(hashable_path)
+        return path
+
+
+def _bidirectional_shortest_path(
+    G, source, target, ignore_nodes=None, ignore_edges=None, weight=None
+):
+    """Returns the shortest path between source and target ignoring
+       nodes and edges in the containers ignore_nodes and ignore_edges.
+
+    This is a custom modification of the standard bidirectional shortest
+    path implementation at networkx.algorithms.unweighted
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       starting node for path
+
+    target : node
+       ending node for path
+
+    ignore_nodes : container of nodes
+       nodes to ignore, optional
+
+    ignore_edges : container of edges
+       edges to ignore, optional
+
+    weight : None
+       This function accepts a weight argument for convenience of
+       shortest_simple_paths function. It will be ignored.
+
+    Returns
+    -------
+    path: list
+       List of nodes in a path from source to target.
+
+    Raises
+    ------
+    NetworkXNoPath
+       If no path exists between source and target.
+
+    See Also
+    --------
+    shortest_path
+
+    """
+    # call helper to do the real work
+    results = _bidirectional_pred_succ(G, source, target, ignore_nodes, ignore_edges)
+    pred, succ, w = results
+
+    # build path from pred+w+succ
+    path = []
+    # from w to target
+    while w is not None:
+        path.append(w)
+        w = succ[w]
+    # from source to w
+    w = pred[path[0]]
+    while w is not None:
+        path.insert(0, w)
+        w = pred[w]
+
+    return len(path), path
+
+
+def _bidirectional_pred_succ(G, source, target, ignore_nodes=None, ignore_edges=None):
+    """Bidirectional shortest path helper.
+    Returns (pred,succ,w) where
+    pred is a dictionary of predecessors from w to the source, and
+    succ is a dictionary of successors from w to the target.
+    """
+    # does BFS from both source and target and meets in the middle
+    if ignore_nodes and (source in ignore_nodes or target in ignore_nodes):
+        raise nx.NetworkXNoPath(f"No path between {source} and {target}.")
+    if target == source:
+        return ({target: None}, {source: None}, source)
+
+    # handle either directed or undirected
+    if G.is_directed():
+        Gpred = G.predecessors
+        Gsucc = G.successors
+    else:
+        Gpred = G.neighbors
+        Gsucc = G.neighbors
+
+    # support optional nodes filter
+    if ignore_nodes:
+
+        def filter_iter(nodes):
+            def iterate(v):
+                for w in nodes(v):
+                    if w not in ignore_nodes:
+                        yield w
+
+            return iterate
+
+        Gpred = filter_iter(Gpred)
+        Gsucc = filter_iter(Gsucc)
+
+    # support optional edges filter
+    if ignore_edges:
+        if G.is_directed():
+
+            def filter_pred_iter(pred_iter):
+                def iterate(v):
+                    for w in pred_iter(v):
+                        if (w, v) not in ignore_edges:
+                            yield w
+
+                return iterate
+
+            def filter_succ_iter(succ_iter):
+                def iterate(v):
+                    for w in succ_iter(v):
+                        if (v, w) not in ignore_edges:
+                            yield w
+
+                return iterate
+
+            Gpred = filter_pred_iter(Gpred)
+            Gsucc = filter_succ_iter(Gsucc)
+
+        else:
+
+            def filter_iter(nodes):
+                def iterate(v):
+                    for w in nodes(v):
+                        if (v, w) not in ignore_edges and (w, v) not in ignore_edges:
+                            yield w
+
+                return iterate
+
+            Gpred = filter_iter(Gpred)
+            Gsucc = filter_iter(Gsucc)
+
+    # predecessor and successors in search
+    pred = {source: None}
+    succ = {target: None}
+
+    # initialize fringes, start with forward
+    forward_fringe = [source]
+    reverse_fringe = [target]
+
+    while forward_fringe and reverse_fringe:
+        if len(forward_fringe) <= len(reverse_fringe):
+            this_level = forward_fringe
+            forward_fringe = []
+            for v in this_level:
+                for w in Gsucc(v):
+                    if w not in pred:
+                        forward_fringe.append(w)
+                        pred[w] = v
+                    if w in succ:
+                        # found path
+                        return pred, succ, w
+        else:
+            this_level = reverse_fringe
+            reverse_fringe = []
+            for v in this_level:
+                for w in Gpred(v):
+                    if w not in succ:
+                        succ[w] = v
+                        reverse_fringe.append(w)
+                    if w in pred:
+                        # found path
+                        return pred, succ, w
+
+    raise nx.NetworkXNoPath(f"No path between {source} and {target}.")
+
+
+def _bidirectional_dijkstra(
+    G, source, target, weight="weight", ignore_nodes=None, ignore_edges=None
+):
+    """Dijkstra's algorithm for shortest paths using bidirectional search.
+
+    This function returns the shortest path between source and target
+    ignoring nodes and edges in the containers ignore_nodes and
+    ignore_edges.
+
+    This is a custom modification of the standard Dijkstra bidirectional
+    shortest path implementation at networkx.algorithms.weighted
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+        Starting node.
+
+    target : node
+        Ending node.
+
+    weight: string, function, optional (default='weight')
+        Edge data key or weight function corresponding to the edge weight
+        If this is a function, the weight of an edge is the value
+        returned by the function. The function must accept exactly three
+        positional arguments: the two endpoints of an edge and the
+        dictionary of edge attributes for that edge. The function must
+        return a number or None to indicate a hidden edge.
+
+    ignore_nodes : container of nodes
+        nodes to ignore, optional
+
+    ignore_edges : container of edges
+        edges to ignore, optional
+
+    Returns
+    -------
+    length : number
+        Shortest path length.
+
+    Returns a tuple of two dictionaries keyed by node.
+    The first dictionary stores distance from the source.
+    The second stores the path from the source to that node.
+
+    Raises
+    ------
+    NetworkXNoPath
+        If no path exists between source and target.
+
+    Notes
+    -----
+    Edge weight attributes must be numerical.
+    Distances are calculated as sums of weighted edges traversed.
+
+    The weight function can be used to hide edges by returning None.
+    So ``weight = lambda u, v, d: 1 if d['color']=="red" else None``
+    will find the shortest red path.
+
+    In practice  bidirectional Dijkstra is much more than twice as fast as
+    ordinary Dijkstra.
+
+    Ordinary Dijkstra expands nodes in a sphere-like manner from the
+    source. The radius of this sphere will eventually be the length
+    of the shortest path. Bidirectional Dijkstra will expand nodes
+    from both the source and the target, making two spheres of half
+    this radius. Volume of the first sphere is pi*r*r while the
+    others are 2*pi*r/2*r/2, making up half the volume.
+
+    This algorithm is not guaranteed to work if edge weights
+    are negative or are floating point numbers
+    (overflows and roundoff errors can cause problems).
+
+    See Also
+    --------
+    shortest_path
+    shortest_path_length
+    """
+    if ignore_nodes and (source in ignore_nodes or target in ignore_nodes):
+        raise nx.NetworkXNoPath(f"No path between {source} and {target}.")
+    if source == target:
+        if source not in G:
+            raise nx.NodeNotFound(f"Node {source} not in graph")
+        return (0, [source])
+
+    # handle either directed or undirected
+    if G.is_directed():
+        Gpred = G.predecessors
+        Gsucc = G.successors
+    else:
+        Gpred = G.neighbors
+        Gsucc = G.neighbors
+
+    # support optional nodes filter
+    if ignore_nodes:
+
+        def filter_iter(nodes):
+            def iterate(v):
+                for w in nodes(v):
+                    if w not in ignore_nodes:
+                        yield w
+
+            return iterate
+
+        Gpred = filter_iter(Gpred)
+        Gsucc = filter_iter(Gsucc)
+
+    # support optional edges filter
+    if ignore_edges:
+        if G.is_directed():
+
+            def filter_pred_iter(pred_iter):
+                def iterate(v):
+                    for w in pred_iter(v):
+                        if (w, v) not in ignore_edges:
+                            yield w
+
+                return iterate
+
+            def filter_succ_iter(succ_iter):
+                def iterate(v):
+                    for w in succ_iter(v):
+                        if (v, w) not in ignore_edges:
+                            yield w
+
+                return iterate
+
+            Gpred = filter_pred_iter(Gpred)
+            Gsucc = filter_succ_iter(Gsucc)
+
+        else:
+
+            def filter_iter(nodes):
+                def iterate(v):
+                    for w in nodes(v):
+                        if (v, w) not in ignore_edges and (w, v) not in ignore_edges:
+                            yield w
+
+                return iterate
+
+            Gpred = filter_iter(Gpred)
+            Gsucc = filter_iter(Gsucc)
+
+    wt = _weight_function(G, weight)
+    # Init:   Forward             Backward
+    dists = [{}, {}]  # dictionary of final distances
+    paths = [{source: [source]}, {target: [target]}]  # dictionary of paths
+    fringe = [[], []]  # heap of (distance, node) tuples for
+    # extracting next node to expand
+    seen = [{source: 0}, {target: 0}]  # dictionary of distances to
+    # nodes seen
+    c = count()
+    # initialize fringe heap
+    heappush(fringe[0], (0, next(c), source))
+    heappush(fringe[1], (0, next(c), target))
+    # neighs for extracting correct neighbor information
+    neighs = [Gsucc, Gpred]
+    # variables to hold shortest discovered path
+    # finaldist = 1e30000
+    finalpath = []
+    dir = 1
+    while fringe[0] and fringe[1]:
+        # choose direction
+        # dir == 0 is forward direction and dir == 1 is back
+        dir = 1 - dir
+        # extract closest to expand
+        (dist, _, v) = heappop(fringe[dir])
+        if v in dists[dir]:
+            # Shortest path to v has already been found
+            continue
+        # update distance
+        dists[dir][v] = dist  # equal to seen[dir][v]
+        if v in dists[1 - dir]:
+            # if we have scanned v in both directions we are done
+            # we have now discovered the shortest path
+            return (finaldist, finalpath)
+
+        for w in neighs[dir](v):
+            if dir == 0:  # forward
+                minweight = wt(v, w, G.get_edge_data(v, w))
+            else:  # back, must remember to change v,w->w,v
+                minweight = wt(w, v, G.get_edge_data(w, v))
+            if minweight is None:
+                continue
+            vwLength = dists[dir][v] + minweight
+
+            if w in dists[dir]:
+                if vwLength < dists[dir][w]:
+                    raise ValueError("Contradictory paths found: negative weights?")
+            elif w not in seen[dir] or vwLength < seen[dir][w]:
+                # relaxing
+                seen[dir][w] = vwLength
+                heappush(fringe[dir], (vwLength, next(c), w))
+                paths[dir][w] = paths[dir][v] + [w]
+                if w in seen[0] and w in seen[1]:
+                    # see if this path is better than the already
+                    # discovered shortest path
+                    totaldist = seen[0][w] + seen[1][w]
+                    if finalpath == [] or finaldist > totaldist:
+                        finaldist = totaldist
+                        revpath = paths[1][w][:]
+                        revpath.reverse()
+                        finalpath = paths[0][w] + revpath[1:]
+    raise nx.NetworkXNoPath(f"No path between {source} and {target}.")
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/smallworld.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/smallworld.py
new file mode 100644
index 0000000000000000000000000000000000000000..456a4ca11c0aa19d1d770bf90e5713ce80e270d8
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/smallworld.py
@@ -0,0 +1,404 @@
+"""Functions for estimating the small-world-ness of graphs.
+
+A small world network is characterized by a small average shortest path length,
+and a large clustering coefficient.
+
+Small-worldness is commonly measured with the coefficient sigma or omega.
+
+Both coefficients compare the average clustering coefficient and shortest path
+length of a given graph against the same quantities for an equivalent random
+or lattice graph.
+
+For more information, see the Wikipedia article on small-world network [1]_.
+
+.. [1] Small-world network:: https://en.wikipedia.org/wiki/Small-world_network
+
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for, py_random_state
+
+__all__ = ["random_reference", "lattice_reference", "sigma", "omega"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@py_random_state(3)
+@nx._dispatchable(returns_graph=True)
+def random_reference(G, niter=1, connectivity=True, seed=None):
+    """Compute a random graph by swapping edges of a given graph.
+
+    Parameters
+    ----------
+    G : graph
+        An undirected graph with 4 or more nodes.
+
+    niter : integer (optional, default=1)
+        An edge is rewired approximately `niter` times.
+
+    connectivity : boolean (optional, default=True)
+        When True, ensure connectivity for the randomized graph.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : graph
+        The randomized graph.
+
+    Raises
+    ------
+    NetworkXError
+        If there are fewer than 4 nodes or 2 edges in `G`
+
+    Notes
+    -----
+    The implementation is adapted from the algorithm by Maslov and Sneppen
+    (2002) [1]_.
+
+    References
+    ----------
+    .. [1] Maslov, Sergei, and Kim Sneppen.
+           "Specificity and stability in topology of protein networks."
+           Science 296.5569 (2002): 910-913.
+    """
+    if len(G) < 4:
+        raise nx.NetworkXError("Graph has fewer than four nodes.")
+    if len(G.edges) < 2:
+        raise nx.NetworkXError("Graph has fewer that 2 edges")
+
+    from networkx.utils import cumulative_distribution, discrete_sequence
+
+    local_conn = nx.connectivity.local_edge_connectivity
+
+    G = G.copy()
+    keys, degrees = zip(*G.degree())  # keys, degree
+    cdf = cumulative_distribution(degrees)  # cdf of degree
+    nnodes = len(G)
+    nedges = nx.number_of_edges(G)
+    niter = niter * nedges
+    ntries = int(nnodes * nedges / (nnodes * (nnodes - 1) / 2))
+    swapcount = 0
+
+    for i in range(niter):
+        n = 0
+        while n < ntries:
+            # pick two random edges without creating edge list
+            # choose source node indices from discrete distribution
+            (ai, ci) = discrete_sequence(2, cdistribution=cdf, seed=seed)
+            if ai == ci:
+                continue  # same source, skip
+            a = keys[ai]  # convert index to label
+            c = keys[ci]
+            # choose target uniformly from neighbors
+            b = seed.choice(list(G.neighbors(a)))
+            d = seed.choice(list(G.neighbors(c)))
+            if b in [a, c, d] or d in [a, b, c]:
+                continue  # all vertices should be different
+
+            # don't create parallel edges
+            if (d not in G[a]) and (b not in G[c]):
+                G.add_edge(a, d)
+                G.add_edge(c, b)
+                G.remove_edge(a, b)
+                G.remove_edge(c, d)
+
+                # Check if the graph is still connected
+                if connectivity and local_conn(G, a, b) == 0:
+                    # Not connected, revert the swap
+                    G.remove_edge(a, d)
+                    G.remove_edge(c, b)
+                    G.add_edge(a, b)
+                    G.add_edge(c, d)
+                else:
+                    swapcount += 1
+                    break
+            n += 1
+    return G
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@py_random_state(4)
+@nx._dispatchable(returns_graph=True)
+def lattice_reference(G, niter=5, D=None, connectivity=True, seed=None):
+    """Latticize the given graph by swapping edges.
+
+    Parameters
+    ----------
+    G : graph
+        An undirected graph.
+
+    niter : integer (optional, default=1)
+        An edge is rewired approximately niter times.
+
+    D : numpy.array (optional, default=None)
+        Distance to the diagonal matrix.
+
+    connectivity : boolean (optional, default=True)
+        Ensure connectivity for the latticized graph when set to True.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : graph
+        The latticized graph.
+
+    Raises
+    ------
+    NetworkXError
+        If there are fewer than 4 nodes or 2 edges in `G`
+
+    Notes
+    -----
+    The implementation is adapted from the algorithm by Sporns et al. [1]_.
+    which is inspired from the original work by Maslov and Sneppen(2002) [2]_.
+
+    References
+    ----------
+    .. [1] Sporns, Olaf, and Jonathan D. Zwi.
+       "The small world of the cerebral cortex."
+       Neuroinformatics 2.2 (2004): 145-162.
+    .. [2] Maslov, Sergei, and Kim Sneppen.
+       "Specificity and stability in topology of protein networks."
+       Science 296.5569 (2002): 910-913.
+    """
+    import numpy as np
+
+    from networkx.utils import cumulative_distribution, discrete_sequence
+
+    local_conn = nx.connectivity.local_edge_connectivity
+
+    if len(G) < 4:
+        raise nx.NetworkXError("Graph has fewer than four nodes.")
+    if len(G.edges) < 2:
+        raise nx.NetworkXError("Graph has fewer that 2 edges")
+    # Instead of choosing uniformly at random from a generated edge list,
+    # this algorithm chooses nonuniformly from the set of nodes with
+    # probability weighted by degree.
+    G = G.copy()
+    keys, degrees = zip(*G.degree())  # keys, degree
+    cdf = cumulative_distribution(degrees)  # cdf of degree
+
+    nnodes = len(G)
+    nedges = nx.number_of_edges(G)
+    if D is None:
+        D = np.zeros((nnodes, nnodes))
+        un = np.arange(1, nnodes)
+        um = np.arange(nnodes - 1, 0, -1)
+        u = np.append((0,), np.where(un < um, un, um))
+
+        for v in range(int(np.ceil(nnodes / 2))):
+            D[nnodes - v - 1, :] = np.append(u[v + 1 :], u[: v + 1])
+            D[v, :] = D[nnodes - v - 1, :][::-1]
+
+    niter = niter * nedges
+    # maximal number of rewiring attempts per 'niter'
+    max_attempts = int(nnodes * nedges / (nnodes * (nnodes - 1) / 2))
+
+    for _ in range(niter):
+        n = 0
+        while n < max_attempts:
+            # pick two random edges without creating edge list
+            # choose source node indices from discrete distribution
+            (ai, ci) = discrete_sequence(2, cdistribution=cdf, seed=seed)
+            if ai == ci:
+                continue  # same source, skip
+            a = keys[ai]  # convert index to label
+            c = keys[ci]
+            # choose target uniformly from neighbors
+            b = seed.choice(list(G.neighbors(a)))
+            d = seed.choice(list(G.neighbors(c)))
+            bi = keys.index(b)
+            di = keys.index(d)
+
+            if b in [a, c, d] or d in [a, b, c]:
+                continue  # all vertices should be different
+
+            # don't create parallel edges
+            if (d not in G[a]) and (b not in G[c]):
+                if D[ai, bi] + D[ci, di] >= D[ai, ci] + D[bi, di]:
+                    # only swap if we get closer to the diagonal
+                    G.add_edge(a, d)
+                    G.add_edge(c, b)
+                    G.remove_edge(a, b)
+                    G.remove_edge(c, d)
+
+                    # Check if the graph is still connected
+                    if connectivity and local_conn(G, a, b) == 0:
+                        # Not connected, revert the swap
+                        G.remove_edge(a, d)
+                        G.remove_edge(c, b)
+                        G.add_edge(a, b)
+                        G.add_edge(c, d)
+                    else:
+                        break
+            n += 1
+
+    return G
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@py_random_state(3)
+@nx._dispatchable
+def sigma(G, niter=100, nrand=10, seed=None):
+    """Returns the small-world coefficient (sigma) of the given graph.
+
+    The small-world coefficient is defined as:
+    sigma = C/Cr / L/Lr
+    where C and L are respectively the average clustering coefficient and
+    average shortest path length of G. Cr and Lr are respectively the average
+    clustering coefficient and average shortest path length of an equivalent
+    random graph.
+
+    A graph is commonly classified as small-world if sigma>1.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+    niter : integer (optional, default=100)
+        Approximate number of rewiring per edge to compute the equivalent
+        random graph.
+    nrand : integer (optional, default=10)
+        Number of random graphs generated to compute the average clustering
+        coefficient (Cr) and average shortest path length (Lr).
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    sigma : float
+        The small-world coefficient of G.
+
+    Notes
+    -----
+    The implementation is adapted from Humphries et al. [1]_ [2]_.
+
+    References
+    ----------
+    .. [1] The brainstem reticular formation is a small-world, not scale-free,
+           network M. D. Humphries, K. Gurney and T. J. Prescott,
+           Proc. Roy. Soc. B 2006 273, 503-511, doi:10.1098/rspb.2005.3354.
+    .. [2] Humphries and Gurney (2008).
+           "Network 'Small-World-Ness': A Quantitative Method for Determining
+           Canonical Network Equivalence".
+           PLoS One. 3 (4). PMID 18446219. doi:10.1371/journal.pone.0002051.
+    """
+    import numpy as np
+
+    # Compute the mean clustering coefficient and average shortest path length
+    # for an equivalent random graph
+    randMetrics = {"C": [], "L": []}
+    for i in range(nrand):
+        Gr = random_reference(G, niter=niter, seed=seed)
+        randMetrics["C"].append(nx.transitivity(Gr))
+        randMetrics["L"].append(nx.average_shortest_path_length(Gr))
+
+    C = nx.transitivity(G)
+    L = nx.average_shortest_path_length(G)
+    Cr = np.mean(randMetrics["C"])
+    Lr = np.mean(randMetrics["L"])
+
+    sigma = (C / Cr) / (L / Lr)
+
+    return float(sigma)
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@py_random_state(3)
+@nx._dispatchable
+def omega(G, niter=5, nrand=10, seed=None):
+    """Returns the small-world coefficient (omega) of a graph
+
+    The small-world coefficient of a graph G is:
+
+    omega = Lr/L - C/Cl
+
+    where C and L are respectively the average clustering coefficient and
+    average shortest path length of G. Lr is the average shortest path length
+    of an equivalent random graph and Cl is the average clustering coefficient
+    of an equivalent lattice graph.
+
+    The small-world coefficient (omega) measures how much G is like a lattice
+    or a random graph. Negative values mean G is similar to a lattice whereas
+    positive values mean G is a random graph.
+    Values close to 0 mean that G has small-world characteristics.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    niter: integer (optional, default=5)
+        Approximate number of rewiring per edge to compute the equivalent
+        random graph.
+
+    nrand: integer (optional, default=10)
+        Number of random graphs generated to compute the maximal clustering
+        coefficient (Cr) and average shortest path length (Lr).
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+
+    Returns
+    -------
+    omega : float
+        The small-world coefficient (omega)
+
+    Notes
+    -----
+    The implementation is adapted from the algorithm by Telesford et al. [1]_.
+
+    References
+    ----------
+    .. [1] Telesford, Joyce, Hayasaka, Burdette, and Laurienti (2011).
+           "The Ubiquity of Small-World Networks".
+           Brain Connectivity. 1 (0038): 367-75.  PMC 3604768. PMID 22432451.
+           doi:10.1089/brain.2011.0038.
+    """
+    import numpy as np
+
+    # Compute the mean clustering coefficient and average shortest path length
+    # for an equivalent random graph
+    randMetrics = {"C": [], "L": []}
+
+    # Calculate initial average clustering coefficient which potentially will
+    # get replaced by higher clustering coefficients from generated lattice
+    # reference graphs
+    Cl = nx.average_clustering(G)
+
+    niter_lattice_reference = niter
+    niter_random_reference = niter * 2
+
+    for _ in range(nrand):
+        # Generate random graph
+        Gr = random_reference(G, niter=niter_random_reference, seed=seed)
+        randMetrics["L"].append(nx.average_shortest_path_length(Gr))
+
+        # Generate lattice graph
+        Gl = lattice_reference(G, niter=niter_lattice_reference, seed=seed)
+
+        # Replace old clustering coefficient, if clustering is higher in
+        # generated lattice reference
+        Cl_temp = nx.average_clustering(Gl)
+        if Cl_temp > Cl:
+            Cl = Cl_temp
+
+    C = nx.average_clustering(G)
+    L = nx.average_shortest_path_length(G)
+    Lr = np.mean(randMetrics["L"])
+
+    omega = (Lr / L) - (C / Cl)
+
+    return float(omega)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/smetric.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/smetric.py
new file mode 100644
index 0000000000000000000000000000000000000000..d985aa805b4fb21300680afe389aae4732793a73
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/smetric.py
@@ -0,0 +1,30 @@
+import networkx as nx
+
+__all__ = ["s_metric"]
+
+
+@nx._dispatchable
+def s_metric(G):
+    """Returns the s-metric [1]_ of graph.
+
+    The s-metric is defined as the sum of the products ``deg(u) * deg(v)``
+    for every edge ``(u, v)`` in `G`.
+
+    Parameters
+    ----------
+    G : graph
+        The graph used to compute the s-metric.
+
+    Returns
+    -------
+    s : float
+        The s-metric of the graph.
+
+    References
+    ----------
+    .. [1] Lun Li, David Alderson, John C. Doyle, and Walter Willinger,
+           Towards a Theory of Scale-Free Graphs:
+           Definition, Properties, and  Implications (Extended Version), 2005.
+           https://arxiv.org/abs/cond-mat/0501169
+    """
+    return float(sum(G.degree(u) * G.degree(v) for (u, v) in G.edges()))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/sparsifiers.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/sparsifiers.py
new file mode 100644
index 0000000000000000000000000000000000000000..59322372e6c1e06d595d8dff0f8680d1daa8a99e
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/sparsifiers.py
@@ -0,0 +1,296 @@
+"""Functions for computing sparsifiers of graphs."""
+
+import math
+
+import networkx as nx
+from networkx.utils import not_implemented_for, py_random_state
+
+__all__ = ["spanner"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@py_random_state(3)
+@nx._dispatchable(edge_attrs="weight", returns_graph=True)
+def spanner(G, stretch, weight=None, seed=None):
+    """Returns a spanner of the given graph with the given stretch.
+
+    A spanner of a graph G = (V, E) with stretch t is a subgraph
+    H = (V, E_S) such that E_S is a subset of E and the distance between
+    any pair of nodes in H is at most t times the distance between the
+    nodes in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected simple graph.
+
+    stretch : float
+        The stretch of the spanner.
+
+    weight : object
+        The edge attribute to use as distance.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    NetworkX graph
+        A spanner of the given graph with the given stretch.
+
+    Raises
+    ------
+    ValueError
+        If a stretch less than 1 is given.
+
+    Notes
+    -----
+    This function implements the spanner algorithm by Baswana and Sen,
+    see [1].
+
+    This algorithm is a randomized las vegas algorithm: The expected
+    running time is O(km) where k = (stretch + 1) // 2 and m is the
+    number of edges in G. The returned graph is always a spanner of the
+    given graph with the specified stretch. For weighted graphs the
+    number of edges in the spanner is O(k * n^(1 + 1 / k)) where k is
+    defined as above and n is the number of nodes in G. For unweighted
+    graphs the number of edges is O(n^(1 + 1 / k) + kn).
+
+    References
+    ----------
+    [1] S. Baswana, S. Sen. A Simple and Linear Time Randomized
+    Algorithm for Computing Sparse Spanners in Weighted Graphs.
+    Random Struct. Algorithms 30(4): 532-563 (2007).
+    """
+    if stretch < 1:
+        raise ValueError("stretch must be at least 1")
+
+    k = (stretch + 1) // 2
+
+    # initialize spanner H with empty edge set
+    H = nx.empty_graph()
+    H.add_nodes_from(G.nodes)
+
+    # phase 1: forming the clusters
+    # the residual graph has V' from the paper as its node set
+    # and E' from the paper as its edge set
+    residual_graph = _setup_residual_graph(G, weight)
+    # clustering is a dictionary that maps nodes in a cluster to the
+    # cluster center
+    clustering = {v: v for v in G.nodes}
+    sample_prob = math.pow(G.number_of_nodes(), -1 / k)
+    size_limit = 2 * math.pow(G.number_of_nodes(), 1 + 1 / k)
+
+    i = 0
+    while i < k - 1:
+        # step 1: sample centers
+        sampled_centers = set()
+        for center in set(clustering.values()):
+            if seed.random() < sample_prob:
+                sampled_centers.add(center)
+
+        # combined loop for steps 2 and 3
+        edges_to_add = set()
+        edges_to_remove = set()
+        new_clustering = {}
+        for v in residual_graph.nodes:
+            if clustering[v] in sampled_centers:
+                continue
+
+            # step 2: find neighboring (sampled) clusters and
+            # lightest edges to them
+            lightest_edge_neighbor, lightest_edge_weight = _lightest_edge_dicts(
+                residual_graph, clustering, v
+            )
+            neighboring_sampled_centers = (
+                set(lightest_edge_weight.keys()) & sampled_centers
+            )
+
+            # step 3: add edges to spanner
+            if not neighboring_sampled_centers:
+                # connect to each neighboring center via lightest edge
+                for neighbor in lightest_edge_neighbor.values():
+                    edges_to_add.add((v, neighbor))
+                # remove all incident edges
+                for neighbor in residual_graph.adj[v]:
+                    edges_to_remove.add((v, neighbor))
+
+            else:  # there is a neighboring sampled center
+                closest_center = min(
+                    neighboring_sampled_centers, key=lightest_edge_weight.get
+                )
+                closest_center_weight = lightest_edge_weight[closest_center]
+                closest_center_neighbor = lightest_edge_neighbor[closest_center]
+
+                edges_to_add.add((v, closest_center_neighbor))
+                new_clustering[v] = closest_center
+
+                # connect to centers with edge weight less than
+                # closest_center_weight
+                for center, edge_weight in lightest_edge_weight.items():
+                    if edge_weight < closest_center_weight:
+                        neighbor = lightest_edge_neighbor[center]
+                        edges_to_add.add((v, neighbor))
+
+                # remove edges to centers with edge weight less than
+                # closest_center_weight
+                for neighbor in residual_graph.adj[v]:
+                    nbr_cluster = clustering[neighbor]
+                    nbr_weight = lightest_edge_weight[nbr_cluster]
+                    if (
+                        nbr_cluster == closest_center
+                        or nbr_weight < closest_center_weight
+                    ):
+                        edges_to_remove.add((v, neighbor))
+
+        # check whether iteration added too many edges to spanner,
+        # if so repeat
+        if len(edges_to_add) > size_limit:
+            # an iteration is repeated O(1) times on expectation
+            continue
+
+        # iteration succeeded
+        i = i + 1
+
+        # actually add edges to spanner
+        for u, v in edges_to_add:
+            _add_edge_to_spanner(H, residual_graph, u, v, weight)
+
+        # actually delete edges from residual graph
+        residual_graph.remove_edges_from(edges_to_remove)
+
+        # copy old clustering data to new_clustering
+        for node, center in clustering.items():
+            if center in sampled_centers:
+                new_clustering[node] = center
+        clustering = new_clustering
+
+        # step 4: remove intra-cluster edges
+        for u in residual_graph.nodes:
+            for v in list(residual_graph.adj[u]):
+                if clustering[u] == clustering[v]:
+                    residual_graph.remove_edge(u, v)
+
+        # update residual graph node set
+        for v in list(residual_graph.nodes):
+            if v not in clustering:
+                residual_graph.remove_node(v)
+
+    # phase 2: vertex-cluster joining
+    for v in residual_graph.nodes:
+        lightest_edge_neighbor, _ = _lightest_edge_dicts(residual_graph, clustering, v)
+        for neighbor in lightest_edge_neighbor.values():
+            _add_edge_to_spanner(H, residual_graph, v, neighbor, weight)
+
+    return H
+
+
+def _setup_residual_graph(G, weight):
+    """Setup residual graph as a copy of G with unique edges weights.
+
+    The node set of the residual graph corresponds to the set V' from
+    the Baswana-Sen paper and the edge set corresponds to the set E'
+    from the paper.
+
+    This function associates distinct weights to the edges of the
+    residual graph (even for unweighted input graphs), as required by
+    the algorithm.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected simple graph.
+
+    weight : object
+        The edge attribute to use as distance.
+
+    Returns
+    -------
+    NetworkX graph
+        The residual graph used for the Baswana-Sen algorithm.
+    """
+    residual_graph = G.copy()
+
+    # establish unique edge weights, even for unweighted graphs
+    for u, v in G.edges():
+        if not weight:
+            residual_graph[u][v]["weight"] = (id(u), id(v))
+        else:
+            residual_graph[u][v]["weight"] = (G[u][v][weight], id(u), id(v))
+
+    return residual_graph
+
+
+def _lightest_edge_dicts(residual_graph, clustering, node):
+    """Find the lightest edge to each cluster.
+
+    Searches for the minimum-weight edge to each cluster adjacent to
+    the given node.
+
+    Parameters
+    ----------
+    residual_graph : NetworkX graph
+        The residual graph used by the Baswana-Sen algorithm.
+
+    clustering : dictionary
+        The current clustering of the nodes.
+
+    node : node
+        The node from which the search originates.
+
+    Returns
+    -------
+    lightest_edge_neighbor, lightest_edge_weight : dictionary, dictionary
+        lightest_edge_neighbor is a dictionary that maps a center C to
+        a node v in the corresponding cluster such that the edge from
+        the given node to v is the lightest edge from the given node to
+        any node in cluster. lightest_edge_weight maps a center C to the
+        weight of the aforementioned edge.
+
+    Notes
+    -----
+    If a cluster has no node that is adjacent to the given node in the
+    residual graph then the center of the cluster is not a key in the
+    returned dictionaries.
+    """
+    lightest_edge_neighbor = {}
+    lightest_edge_weight = {}
+    for neighbor in residual_graph.adj[node]:
+        nbr_center = clustering[neighbor]
+        weight = residual_graph[node][neighbor]["weight"]
+        if (
+            nbr_center not in lightest_edge_weight
+            or weight < lightest_edge_weight[nbr_center]
+        ):
+            lightest_edge_neighbor[nbr_center] = neighbor
+            lightest_edge_weight[nbr_center] = weight
+    return lightest_edge_neighbor, lightest_edge_weight
+
+
+def _add_edge_to_spanner(H, residual_graph, u, v, weight):
+    """Add the edge {u, v} to the spanner H and take weight from
+    the residual graph.
+
+    Parameters
+    ----------
+    H : NetworkX graph
+        The spanner under construction.
+
+    residual_graph : NetworkX graph
+        The residual graph used by the Baswana-Sen algorithm. The weight
+        for the edge is taken from this graph.
+
+    u : node
+        One endpoint of the edge.
+
+    v : node
+        The other endpoint of the edge.
+
+    weight : object
+        The edge attribute to use as distance.
+    """
+    H.add_edge(u, v)
+    if weight:
+        H[u][v][weight] = residual_graph[u][v]["weight"][0]
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/structuralholes.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/structuralholes.py
new file mode 100644
index 0000000000000000000000000000000000000000..f43c58c5fd91f13ec10c5adba9c20decb26b708b
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/structuralholes.py
@@ -0,0 +1,374 @@
+"""Functions for computing measures of structural holes."""
+
+import networkx as nx
+
+__all__ = ["constraint", "local_constraint", "effective_size"]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def mutual_weight(G, u, v, weight=None):
+    """Returns the sum of the weights of the edge from `u` to `v` and
+    the edge from `v` to `u` in `G`.
+
+    `weight` is the edge data key that represents the edge weight. If
+    the specified key is `None` or is not in the edge data for an edge,
+    that edge is assumed to have weight 1.
+
+    Pre-conditions: `u` and `v` must both be in `G`.
+
+    """
+    try:
+        a_uv = G[u][v].get(weight, 1)
+    except KeyError:
+        a_uv = 0
+    try:
+        a_vu = G[v][u].get(weight, 1)
+    except KeyError:
+        a_vu = 0
+    return a_uv + a_vu
+
+
+@nx._dispatchable(edge_attrs="weight")
+def normalized_mutual_weight(G, u, v, norm=sum, weight=None):
+    """Returns normalized mutual weight of the edges from `u` to `v`
+    with respect to the mutual weights of the neighbors of `u` in `G`.
+
+    `norm` specifies how the normalization factor is computed. It must
+    be a function that takes a single argument and returns a number.
+    The argument will be an iterable of mutual weights
+    of pairs ``(u, w)``, where ``w`` ranges over each (in- and
+    out-)neighbor of ``u``. Commons values for `normalization` are
+    ``sum`` and ``max``.
+
+    `weight` can be ``None`` or a string, if None, all edge weights
+    are considered equal. Otherwise holds the name of the edge
+    attribute used as weight.
+
+    """
+    scale = norm(mutual_weight(G, u, w, weight) for w in set(nx.all_neighbors(G, u)))
+    return 0 if scale == 0 else mutual_weight(G, u, v, weight) / scale
+
+
+@nx._dispatchable(edge_attrs="weight")
+def effective_size(G, nodes=None, weight=None):
+    r"""Returns the effective size of all nodes in the graph ``G``.
+
+    The *effective size* of a node's ego network is based on the concept
+    of redundancy. A person's ego network has redundancy to the extent
+    that her contacts are connected to each other as well. The
+    nonredundant part of a person's relationships is the effective
+    size of her ego network [1]_.  Formally, the effective size of a
+    node $u$, denoted $e(u)$, is defined by
+
+    .. math::
+
+       e(u) = \sum_{v \in N(u) \setminus \{u\}}
+       \left(1 - \sum_{w \in N(v)} p_{uw} m_{vw}\right)
+
+    where $N(u)$ is the set of neighbors of $u$ and $p_{uw}$ is the
+    normalized mutual weight of the (directed or undirected) edges
+    joining $u$ and $v$, for each vertex $u$ and $v$ [1]_. And $m_{vw}$
+    is the mutual weight of $v$ and $w$ divided by $v$ highest mutual
+    weight with any of its neighbors. The *mutual weight* of $u$ and $v$
+    is the sum of the weights of edges joining them (edge weights are
+    assumed to be one if the graph is unweighted).
+
+    For the case of unweighted and undirected graphs, Borgatti proposed
+    a simplified formula to compute effective size [2]_
+
+    .. math::
+
+       e(u) = n - \frac{2t}{n}
+
+    where `t` is the number of ties in the ego network (not including
+    ties to ego) and `n` is the number of nodes (excluding ego).
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The graph containing ``v``. Directed graphs are treated like
+        undirected graphs when computing neighbors of ``v``.
+
+    nodes : container, optional
+        Container of nodes in the graph ``G`` to compute the effective size.
+        If None, the effective size of every node is computed.
+
+    weight : None or string, optional
+      If None, all edge weights are considered equal.
+      Otherwise holds the name of the edge attribute used as weight.
+
+    Returns
+    -------
+    dict
+        Dictionary with nodes as keys and the effective size of the node as values.
+
+    Notes
+    -----
+    Isolated nodes, including nodes which only have self-loop edges, do not
+    have a well-defined effective size::
+
+        >>> G = nx.path_graph(3)
+        >>> G.add_edge(4, 4)
+        >>> nx.effective_size(G)
+        {0: 1.0, 1: 2.0, 2: 1.0, 4: nan}
+
+    Burt also defined the related concept of *efficiency* of a node's ego
+    network, which is its effective size divided by the degree of that
+    node [1]_. So you can easily compute efficiency:
+
+    >>> G = nx.DiGraph()
+    >>> G.add_edges_from([(0, 1), (0, 2), (1, 0), (2, 1)])
+    >>> esize = nx.effective_size(G)
+    >>> efficiency = {n: v / G.degree(n) for n, v in esize.items()}
+
+    See also
+    --------
+    constraint
+
+    References
+    ----------
+    .. [1] Burt, Ronald S.
+           *Structural Holes: The Social Structure of Competition.*
+           Cambridge: Harvard University Press, 1995.
+
+    .. [2] Borgatti, S.
+           "Structural Holes: Unpacking Burt's Redundancy Measures"
+           CONNECTIONS 20(1):35-38.
+           http://www.analytictech.com/connections/v20(1)/holes.htm
+
+    """
+
+    def redundancy(G, u, v, weight=None):
+        nmw = normalized_mutual_weight
+        r = sum(
+            nmw(G, u, w, weight=weight) * nmw(G, v, w, norm=max, weight=weight)
+            for w in set(nx.all_neighbors(G, u))
+        )
+        return 1 - r
+
+    # Check if scipy is available
+    try:
+        # Needed for errstate
+        import numpy as np
+
+        # make sure nx.adjacency_matrix will not raise
+        import scipy as sp
+
+        has_scipy = True
+    except:
+        has_scipy = False
+
+    if nodes is None and has_scipy:
+        # In order to compute constraint of all nodes,
+        # algorithms based on sparse matrices can be much faster
+
+        # Obtain the adjacency matrix
+        P = nx.adjacency_matrix(G, weight=weight)
+
+        # Calculate mutual weights
+        mutual_weights1 = P + P.T
+        mutual_weights2 = mutual_weights1.copy()
+
+        with np.errstate(divide="ignore"):
+            # Mutual_weights1 = Normalize mutual weights by row sums
+            mutual_weights1 /= mutual_weights1.sum(axis=1)[:, np.newaxis]
+
+            # Mutual_weights2 = Normalize mutual weights by row max
+            mutual_weights2 /= mutual_weights2.max(axis=1).toarray()
+
+        # Calculate effective sizes
+        r = 1 - (mutual_weights1 @ mutual_weights2.T).toarray()
+        effective_size = ((mutual_weights1 > 0) * r).sum(axis=1)
+
+        # Special treatment: isolated nodes (ignoring selfloops) marked with "nan"
+        sum_mutual_weights = mutual_weights1.sum(axis=1) - mutual_weights1.diagonal()
+        isolated_nodes = sum_mutual_weights == 0
+        effective_size[isolated_nodes] = float("nan")
+        # Use tolist() to automatically convert numpy scalars -> Python scalars
+        return dict(zip(G, effective_size.tolist()))
+
+    # Results for only requested nodes
+    effective_size = {}
+    if nodes is None:
+        nodes = G
+    # Use Borgatti's simplified formula for unweighted and undirected graphs
+    if not G.is_directed() and weight is None:
+        for v in nodes:
+            # Effective size is not defined for isolated nodes, including nodes
+            # with only self-edges
+            if all(u == v for u in G[v]):
+                effective_size[v] = float("nan")
+                continue
+            E = nx.ego_graph(G, v, center=False, undirected=True)
+            effective_size[v] = len(E) - (2 * E.size()) / len(E)
+    else:
+        for v in nodes:
+            # Effective size is not defined for isolated nodes, including nodes
+            # with only self-edges
+            if all(u == v for u in G[v]):
+                effective_size[v] = float("nan")
+                continue
+            effective_size[v] = sum(
+                redundancy(G, v, u, weight) for u in set(nx.all_neighbors(G, v))
+            )
+    return effective_size
+
+
+@nx._dispatchable(edge_attrs="weight")
+def constraint(G, nodes=None, weight=None):
+    r"""Returns the constraint on all nodes in the graph ``G``.
+
+    The *constraint* is a measure of the extent to which a node *v* is
+    invested in those nodes that are themselves invested in the
+    neighbors of *v*. Formally, the *constraint on v*, denoted `c(v)`,
+    is defined by
+
+    .. math::
+
+       c(v) = \sum_{w \in N(v) \setminus \{v\}} \ell(v, w)
+
+    where $N(v)$ is the subset of the neighbors of `v` that are either
+    predecessors or successors of `v` and $\ell(v, w)$ is the local
+    constraint on `v` with respect to `w` [1]_. For the definition of local
+    constraint, see :func:`local_constraint`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The graph containing ``v``. This can be either directed or undirected.
+
+    nodes : container, optional
+        Container of nodes in the graph ``G`` to compute the constraint. If
+        None, the constraint of every node is computed.
+
+    weight : None or string, optional
+      If None, all edge weights are considered equal.
+      Otherwise holds the name of the edge attribute used as weight.
+
+    Returns
+    -------
+    dict
+        Dictionary with nodes as keys and the constraint on the node as values.
+
+    See also
+    --------
+    local_constraint
+
+    References
+    ----------
+    .. [1] Burt, Ronald S.
+           "Structural holes and good ideas".
+           American Journal of Sociology (110): 349–399.
+
+    """
+
+    # Check if scipy is available
+    try:
+        # Needed for errstate
+        import numpy as np
+
+        # make sure nx.adjacency_matrix will not raise
+        import scipy as sp
+
+        has_scipy = True
+    except:
+        has_scipy = False
+
+    if nodes is None and has_scipy:
+        # In order to compute constraint of all nodes,
+        # algorithms based on sparse matrices can be much faster
+
+        # Obtain the adjacency matrix
+        P = nx.adjacency_matrix(G, weight=weight)
+
+        # Calculate mutual weights
+        mutual_weights = P + P.T
+
+        # Normalize mutual weights by row sums
+        sum_mutual_weights = mutual_weights.sum(axis=1)
+        with np.errstate(divide="ignore"):
+            mutual_weights /= sum_mutual_weights[:, np.newaxis]
+
+        # Calculate local constraints and constraints
+        local_constraints = (mutual_weights + mutual_weights @ mutual_weights) ** 2
+        constraints = ((mutual_weights > 0) * local_constraints).sum(axis=1)
+
+        # Special treatment: isolated nodes marked with "nan"
+        isolated_nodes = sum_mutual_weights - 2 * mutual_weights.diagonal() == 0
+        constraints[isolated_nodes] = float("nan")
+        # Use tolist() to automatically convert numpy scalars -> Python scalars
+        return dict(zip(G, constraints.tolist()))
+
+    # Result for only requested nodes
+    constraint = {}
+    if nodes is None:
+        nodes = G
+    for v in nodes:
+        # Constraint is not defined for isolated nodes
+        if len(G[v]) == 0:
+            constraint[v] = float("nan")
+            continue
+        constraint[v] = sum(
+            local_constraint(G, v, n, weight) for n in set(nx.all_neighbors(G, v))
+        )
+    return constraint
+
+
+@nx._dispatchable(edge_attrs="weight")
+def local_constraint(G, u, v, weight=None):
+    r"""Returns the local constraint on the node ``u`` with respect to
+    the node ``v`` in the graph ``G``.
+
+    Formally, the *local constraint on u with respect to v*, denoted
+    $\ell(u, v)$, is defined by
+
+    .. math::
+
+       \ell(u, v) = \left(p_{uv} + \sum_{w \in N(v)} p_{uw} p_{wv}\right)^2,
+
+    where $N(v)$ is the set of neighbors of $v$ and $p_{uv}$ is the
+    normalized mutual weight of the (directed or undirected) edges
+    joining $u$ and $v$, for each vertex $u$ and $v$ [1]_. The *mutual
+    weight* of $u$ and $v$ is the sum of the weights of edges joining
+    them (edge weights are assumed to be one if the graph is
+    unweighted).
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The graph containing ``u`` and ``v``. This can be either
+        directed or undirected.
+
+    u : node
+        A node in the graph ``G``.
+
+    v : node
+        A node in the graph ``G``.
+
+    weight : None or string, optional
+      If None, all edge weights are considered equal.
+      Otherwise holds the name of the edge attribute used as weight.
+
+    Returns
+    -------
+    float
+        The constraint of the node ``v`` in the graph ``G``.
+
+    See also
+    --------
+    constraint
+
+    References
+    ----------
+    .. [1] Burt, Ronald S.
+           "Structural holes and good ideas".
+           American Journal of Sociology (110): 349–399.
+
+    """
+    nmw = normalized_mutual_weight
+    direct = nmw(G, u, v, weight=weight)
+    indirect = sum(
+        nmw(G, u, w, weight=weight) * nmw(G, w, v, weight=weight)
+        for w in set(nx.all_neighbors(G, u))
+    )
+    return (direct + indirect) ** 2
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/summarization.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/summarization.py
new file mode 100644
index 0000000000000000000000000000000000000000..23db8da4efffa7dcbabfb75e031187d1b2b190dc
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/summarization.py
@@ -0,0 +1,564 @@
+"""
+Graph summarization finds smaller representations of graphs resulting in faster
+runtime of algorithms, reduced storage needs, and noise reduction.
+Summarization has applications in areas such as visualization, pattern mining,
+clustering and community detection, and more.  Core graph summarization
+techniques are grouping/aggregation, bit-compression,
+simplification/sparsification, and influence based. Graph summarization
+algorithms often produce either summary graphs in the form of supergraphs or
+sparsified graphs, or a list of independent structures. Supergraphs are the
+most common product, which consist of supernodes and original nodes and are
+connected by edges and superedges, which represent aggregate edges between
+nodes and supernodes.
+
+Grouping/aggregation based techniques compress graphs by representing
+close/connected nodes and edges in a graph by a single node/edge in a
+supergraph. Nodes can be grouped together into supernodes based on their
+structural similarities or proximity within a graph to reduce the total number
+of nodes in a graph. Edge-grouping techniques group edges into lossy/lossless
+nodes called compressor or virtual nodes to reduce the total number of edges in
+a graph. Edge-grouping techniques can be lossless, meaning that they can be
+used to re-create the original graph, or techniques can be lossy, requiring
+less space to store the summary graph, but at the expense of lower
+reconstruction accuracy of the original graph.
+
+Bit-compression techniques minimize the amount of information needed to
+describe the original graph, while revealing structural patterns in the
+original graph.  The two-part minimum description length (MDL) is often used to
+represent the model and the original graph in terms of the model.  A key
+difference between graph compression and graph summarization is that graph
+summarization focuses on finding structural patterns within the original graph,
+whereas graph compression focuses on compressions the original graph to be as
+small as possible.  **NOTE**: Some bit-compression methods exist solely to
+compress a graph without creating a summary graph or finding comprehensible
+structural patterns.
+
+Simplification/Sparsification techniques attempt to create a sparse
+representation of a graph by removing unimportant nodes and edges from the
+graph.  Sparsified graphs differ from supergraphs created by
+grouping/aggregation by only containing a subset of the original nodes and
+edges of the original graph.
+
+Influence based techniques aim to find a high-level description of influence
+propagation in a large graph.  These methods are scarce and have been mostly
+applied to social graphs.
+
+*dedensification* is a grouping/aggregation based technique to compress the
+neighborhoods around high-degree nodes in unweighted graphs by adding
+compressor nodes that summarize multiple edges of the same type to
+high-degree nodes (nodes with a degree greater than a given threshold).
+Dedensification was developed for the purpose of increasing performance of
+query processing around high-degree nodes in graph databases and enables direct
+operations on the compressed graph.  The structural patterns surrounding
+high-degree nodes in the original is preserved while using fewer edges and
+adding a small number of compressor nodes.  The degree of nodes present in the
+original graph is also preserved. The current implementation of dedensification
+supports graphs with one edge type.
+
+For more information on graph summarization, see `Graph Summarization Methods
+and Applications: A Survey <https://dl.acm.org/doi/abs/10.1145/3186727>`_
+"""
+
+from collections import Counter, defaultdict
+
+import networkx as nx
+
+__all__ = ["dedensify", "snap_aggregation"]
+
+
+@nx._dispatchable(mutates_input={"not copy": 3}, returns_graph=True)
+def dedensify(G, threshold, prefix=None, copy=True):
+    """Compresses neighborhoods around high-degree nodes
+
+    Reduces the number of edges to high-degree nodes by adding compressor nodes
+    that summarize multiple edges of the same type to high-degree nodes (nodes
+    with a degree greater than a given threshold).  Dedensification also has
+    the added benefit of reducing the number of edges around high-degree nodes.
+    The implementation currently supports graphs with a single edge type.
+
+    Parameters
+    ----------
+    G: graph
+       A networkx graph
+    threshold: int
+       Minimum degree threshold of a node to be considered a high degree node.
+       The threshold must be greater than or equal to 2.
+    prefix: str or None, optional (default: None)
+       An optional prefix for denoting compressor nodes
+    copy: bool, optional (default: True)
+       Indicates if dedensification should be done inplace
+
+    Returns
+    -------
+    dedensified networkx graph : (graph, set)
+        2-tuple of the dedensified graph and set of compressor nodes
+
+    Notes
+    -----
+    According to the algorithm in [1]_, removes edges in a graph by
+    compressing/decompressing the neighborhoods around high degree nodes by
+    adding compressor nodes that summarize multiple edges of the same type
+    to high-degree nodes.  Dedensification will only add a compressor node when
+    doing so will reduce the total number of edges in the given graph. This
+    implementation currently supports graphs with a single edge type.
+
+    Examples
+    --------
+    Dedensification will only add compressor nodes when doing so would result
+    in fewer edges::
+
+        >>> original_graph = nx.DiGraph()
+        >>> original_graph.add_nodes_from(
+        ...     ["1", "2", "3", "4", "5", "6", "A", "B", "C"]
+        ... )
+        >>> original_graph.add_edges_from(
+        ...     [
+        ...         ("1", "C"), ("1", "B"),
+        ...         ("2", "C"), ("2", "B"), ("2", "A"),
+        ...         ("3", "B"), ("3", "A"), ("3", "6"),
+        ...         ("4", "C"), ("4", "B"), ("4", "A"),
+        ...         ("5", "B"), ("5", "A"),
+        ...         ("6", "5"),
+        ...         ("A", "6")
+        ...     ]
+        ... )
+        >>> c_graph, c_nodes = nx.dedensify(original_graph, threshold=2)
+        >>> original_graph.number_of_edges()
+        15
+        >>> c_graph.number_of_edges()
+        14
+
+    A dedensified, directed graph can be "densified" to reconstruct the
+    original graph::
+
+        >>> original_graph = nx.DiGraph()
+        >>> original_graph.add_nodes_from(
+        ...     ["1", "2", "3", "4", "5", "6", "A", "B", "C"]
+        ... )
+        >>> original_graph.add_edges_from(
+        ...     [
+        ...         ("1", "C"), ("1", "B"),
+        ...         ("2", "C"), ("2", "B"), ("2", "A"),
+        ...         ("3", "B"), ("3", "A"), ("3", "6"),
+        ...         ("4", "C"), ("4", "B"), ("4", "A"),
+        ...         ("5", "B"), ("5", "A"),
+        ...         ("6", "5"),
+        ...         ("A", "6")
+        ...     ]
+        ... )
+        >>> c_graph, c_nodes = nx.dedensify(original_graph, threshold=2)
+        >>> # re-densifies the compressed graph into the original graph
+        >>> for c_node in c_nodes:
+        ...     all_neighbors = set(nx.all_neighbors(c_graph, c_node))
+        ...     out_neighbors = set(c_graph.neighbors(c_node))
+        ...     for out_neighbor in out_neighbors:
+        ...         c_graph.remove_edge(c_node, out_neighbor)
+        ...     in_neighbors = all_neighbors - out_neighbors
+        ...     for in_neighbor in in_neighbors:
+        ...         c_graph.remove_edge(in_neighbor, c_node)
+        ...         for out_neighbor in out_neighbors:
+        ...             c_graph.add_edge(in_neighbor, out_neighbor)
+        ...     c_graph.remove_node(c_node)
+        ...
+        >>> nx.is_isomorphic(original_graph, c_graph)
+        True
+
+    References
+    ----------
+    .. [1] Maccioni, A., & Abadi, D. J. (2016, August).
+       Scalable pattern matching over compressed graphs via dedensification.
+       In Proceedings of the 22nd ACM SIGKDD International Conference on
+       Knowledge Discovery and Data Mining (pp. 1755-1764).
+       http://www.cs.umd.edu/~abadi/papers/graph-dedense.pdf
+    """
+    if threshold < 2:
+        raise nx.NetworkXError("The degree threshold must be >= 2")
+
+    degrees = G.in_degree if G.is_directed() else G.degree
+    # Group nodes based on degree threshold
+    high_degree_nodes = {n for n, d in degrees if d > threshold}
+    low_degree_nodes = G.nodes() - high_degree_nodes
+
+    auxiliary = {}
+    for node in G:
+        high_degree_nbrs = frozenset(high_degree_nodes & set(G[node]))
+        if high_degree_nbrs:
+            if high_degree_nbrs in auxiliary:
+                auxiliary[high_degree_nbrs].add(node)
+            else:
+                auxiliary[high_degree_nbrs] = {node}
+
+    if copy:
+        G = G.copy()
+
+    compressor_nodes = set()
+    for index, (high_degree_nodes, low_degree_nodes) in enumerate(auxiliary.items()):
+        low_degree_node_count = len(low_degree_nodes)
+        high_degree_node_count = len(high_degree_nodes)
+        old_edges = high_degree_node_count * low_degree_node_count
+        new_edges = high_degree_node_count + low_degree_node_count
+        if old_edges <= new_edges:
+            continue
+        compression_node = "".join(str(node) for node in high_degree_nodes)
+        if prefix:
+            compression_node = str(prefix) + compression_node
+        for node in low_degree_nodes:
+            for high_node in high_degree_nodes:
+                if G.has_edge(node, high_node):
+                    G.remove_edge(node, high_node)
+
+            G.add_edge(node, compression_node)
+        for node in high_degree_nodes:
+            G.add_edge(compression_node, node)
+        compressor_nodes.add(compression_node)
+    return G, compressor_nodes
+
+
+def _snap_build_graph(
+    G,
+    groups,
+    node_attributes,
+    edge_attributes,
+    neighbor_info,
+    edge_types,
+    prefix,
+    supernode_attribute,
+    superedge_attribute,
+):
+    """
+    Build the summary graph from the data structures produced in the SNAP aggregation algorithm
+
+    Used in the SNAP aggregation algorithm to build the output summary graph and supernode
+    lookup dictionary.  This process uses the original graph and the data structures to
+    create the supernodes with the correct node attributes, and the superedges with the correct
+    edge attributes
+
+    Parameters
+    ----------
+    G: networkx.Graph
+        the original graph to be summarized
+    groups: dict
+        A dictionary of unique group IDs and their corresponding node groups
+    node_attributes: iterable
+        An iterable of the node attributes considered in the summarization process
+    edge_attributes: iterable
+        An iterable of the edge attributes considered in the summarization process
+    neighbor_info: dict
+        A data structure indicating the number of edges a node has with the
+        groups in the current summarization of each edge type
+    edge_types: dict
+        dictionary of edges in the graph and their corresponding attributes recognized
+        in the summarization
+    prefix: string
+        The prefix to be added to all supernodes
+    supernode_attribute: str
+        The node attribute for recording the supernode groupings of nodes
+    superedge_attribute: str
+        The edge attribute for recording the edge types represented by superedges
+
+    Returns
+    -------
+    summary graph: Networkx graph
+    """
+    output = G.__class__()
+    node_label_lookup = {}
+    for index, group_id in enumerate(groups):
+        group_set = groups[group_id]
+        supernode = f"{prefix}{index}"
+        node_label_lookup[group_id] = supernode
+        supernode_attributes = {
+            attr: G.nodes[next(iter(group_set))][attr] for attr in node_attributes
+        }
+        supernode_attributes[supernode_attribute] = group_set
+        output.add_node(supernode, **supernode_attributes)
+
+    for group_id in groups:
+        group_set = groups[group_id]
+        source_supernode = node_label_lookup[group_id]
+        for other_group, group_edge_types in neighbor_info[
+            next(iter(group_set))
+        ].items():
+            if group_edge_types:
+                target_supernode = node_label_lookup[other_group]
+                summary_graph_edge = (source_supernode, target_supernode)
+
+                edge_types = [
+                    dict(zip(edge_attributes, edge_type))
+                    for edge_type in group_edge_types
+                ]
+
+                has_edge = output.has_edge(*summary_graph_edge)
+                if output.is_multigraph():
+                    if not has_edge:
+                        for edge_type in edge_types:
+                            output.add_edge(*summary_graph_edge, **edge_type)
+                    elif not output.is_directed():
+                        existing_edge_data = output.get_edge_data(*summary_graph_edge)
+                        for edge_type in edge_types:
+                            if edge_type not in existing_edge_data.values():
+                                output.add_edge(*summary_graph_edge, **edge_type)
+                else:
+                    superedge_attributes = {superedge_attribute: edge_types}
+                    output.add_edge(*summary_graph_edge, **superedge_attributes)
+
+    return output
+
+
+def _snap_eligible_group(G, groups, group_lookup, edge_types):
+    """
+    Determines if a group is eligible to be split.
+
+    A group is eligible to be split if all nodes in the group have edges of the same type(s)
+    with the same other groups.
+
+    Parameters
+    ----------
+    G: graph
+        graph to be summarized
+    groups: dict
+        A dictionary of unique group IDs and their corresponding node groups
+    group_lookup: dict
+        dictionary of nodes and their current corresponding group ID
+    edge_types: dict
+        dictionary of edges in the graph and their corresponding attributes recognized
+        in the summarization
+
+    Returns
+    -------
+    tuple: group ID to split, and neighbor-groups participation_counts data structure
+    """
+    nbr_info = {node: {gid: Counter() for gid in groups} for node in group_lookup}
+    for group_id in groups:
+        current_group = groups[group_id]
+
+        # build nbr_info for nodes in group
+        for node in current_group:
+            nbr_info[node] = {group_id: Counter() for group_id in groups}
+            edges = G.edges(node, keys=True) if G.is_multigraph() else G.edges(node)
+            for edge in edges:
+                neighbor = edge[1]
+                edge_type = edge_types[edge]
+                neighbor_group_id = group_lookup[neighbor]
+                nbr_info[node][neighbor_group_id][edge_type] += 1
+
+        # check if group_id is eligible to be split
+        group_size = len(current_group)
+        for other_group_id in groups:
+            edge_counts = Counter()
+            for node in current_group:
+                edge_counts.update(nbr_info[node][other_group_id].keys())
+
+            if not all(count == group_size for count in edge_counts.values()):
+                # only the nbr_info of the returned group_id is required for handling group splits
+                return group_id, nbr_info
+
+    # if no eligible groups, complete nbr_info is calculated
+    return None, nbr_info
+
+
+def _snap_split(groups, neighbor_info, group_lookup, group_id):
+    """
+    Splits a group based on edge types and updates the groups accordingly
+
+    Splits the group with the given group_id based on the edge types
+    of the nodes so that each new grouping will all have the same
+    edges with other nodes.
+
+    Parameters
+    ----------
+    groups: dict
+        A dictionary of unique group IDs and their corresponding node groups
+    neighbor_info: dict
+        A data structure indicating the number of edges a node has with the
+        groups in the current summarization of each edge type
+    edge_types: dict
+        dictionary of edges in the graph and their corresponding attributes recognized
+        in the summarization
+    group_lookup: dict
+        dictionary of nodes and their current corresponding group ID
+    group_id: object
+        ID of group to be split
+
+    Returns
+    -------
+    dict
+        The updated groups based on the split
+    """
+    new_group_mappings = defaultdict(set)
+    for node in groups[group_id]:
+        signature = tuple(
+            frozenset(edge_types) for edge_types in neighbor_info[node].values()
+        )
+        new_group_mappings[signature].add(node)
+
+    # leave the biggest new_group as the original group
+    new_groups = sorted(new_group_mappings.values(), key=len)
+    for new_group in new_groups[:-1]:
+        # Assign unused integer as the new_group_id
+        # ids are tuples, so will not interact with the original group_ids
+        new_group_id = len(groups)
+        groups[new_group_id] = new_group
+        groups[group_id] -= new_group
+        for node in new_group:
+            group_lookup[node] = new_group_id
+
+    return groups
+
+
+@nx._dispatchable(
+    node_attrs="[node_attributes]", edge_attrs="[edge_attributes]", returns_graph=True
+)
+def snap_aggregation(
+    G,
+    node_attributes,
+    edge_attributes=(),
+    prefix="Supernode-",
+    supernode_attribute="group",
+    superedge_attribute="types",
+):
+    """Creates a summary graph based on attributes and connectivity.
+
+    This function uses the Summarization by Grouping Nodes on Attributes
+    and Pairwise edges (SNAP) algorithm for summarizing a given
+    graph by grouping nodes by node attributes and their edge attributes
+    into supernodes in a summary graph.  This name SNAP should not be
+    confused with the Stanford Network Analysis Project (SNAP).
+
+    Here is a high-level view of how this algorithm works:
+
+    1) Group nodes by node attribute values.
+
+    2) Iteratively split groups until all nodes in each group have edges
+    to nodes in the same groups. That is, until all the groups are homogeneous
+    in their member nodes' edges to other groups.  For example,
+    if all the nodes in group A only have edge to nodes in group B, then the
+    group is homogeneous and does not need to be split. If all nodes in group B
+    have edges with nodes in groups {A, C}, but some also have edges with other
+    nodes in B, then group B is not homogeneous and needs to be split into
+    groups have edges with {A, C} and a group of nodes having
+    edges with {A, B, C}.  This way, viewers of the summary graph can
+    assume that all nodes in the group have the exact same node attributes and
+    the exact same edges.
+
+    3) Build the output summary graph, where the groups are represented by
+    super-nodes. Edges represent the edges shared between all the nodes in each
+    respective groups.
+
+    A SNAP summary graph can be used to visualize graphs that are too large to display
+    or visually analyze, or to efficiently identify sets of similar nodes with similar connectivity
+    patterns to other sets of similar nodes based on specified node and/or edge attributes in a graph.
+
+    Parameters
+    ----------
+    G: graph
+        Networkx Graph to be summarized
+    node_attributes: iterable, required
+        An iterable of the node attributes used to group nodes in the summarization process. Nodes
+        with the same values for these attributes will be grouped together in the summary graph.
+    edge_attributes: iterable, optional
+        An iterable of the edge attributes considered in the summarization process.  If provided, unique
+        combinations of the attribute values found in the graph are used to
+        determine the edge types in the graph.  If not provided, all edges
+        are considered to be of the same type.
+    prefix: str
+        The prefix used to denote supernodes in the summary graph. Defaults to 'Supernode-'.
+    supernode_attribute: str
+        The node attribute for recording the supernode groupings of nodes. Defaults to 'group'.
+    superedge_attribute: str
+        The edge attribute for recording the edge types of multiple edges. Defaults to 'types'.
+
+    Returns
+    -------
+    networkx.Graph: summary graph
+
+    Examples
+    --------
+    SNAP aggregation takes a graph and summarizes it in the context of user-provided
+    node and edge attributes such that a viewer can more easily extract and
+    analyze the information represented by the graph
+
+    >>> nodes = {
+    ...     "A": dict(color="Red"),
+    ...     "B": dict(color="Red"),
+    ...     "C": dict(color="Red"),
+    ...     "D": dict(color="Red"),
+    ...     "E": dict(color="Blue"),
+    ...     "F": dict(color="Blue"),
+    ... }
+    >>> edges = [
+    ...     ("A", "E", "Strong"),
+    ...     ("B", "F", "Strong"),
+    ...     ("C", "E", "Weak"),
+    ...     ("D", "F", "Weak"),
+    ... ]
+    >>> G = nx.Graph()
+    >>> for node in nodes:
+    ...     attributes = nodes[node]
+    ...     G.add_node(node, **attributes)
+    >>> for source, target, type in edges:
+    ...     G.add_edge(source, target, type=type)
+    >>> node_attributes = ("color",)
+    >>> edge_attributes = ("type",)
+    >>> summary_graph = nx.snap_aggregation(
+    ...     G, node_attributes=node_attributes, edge_attributes=edge_attributes
+    ... )
+
+    Notes
+    -----
+    The summary graph produced is called a maximum Attribute-edge
+    compatible (AR-compatible) grouping.  According to [1]_, an
+    AR-compatible grouping means that all nodes in each group have the same
+    exact node attribute values and the same exact edges and
+    edge types to one or more nodes in the same groups.  The maximal
+    AR-compatible grouping is the grouping with the minimal cardinality.
+
+    The AR-compatible grouping is the most detailed grouping provided by
+    any of the SNAP algorithms.
+
+    References
+    ----------
+    .. [1] Y. Tian, R. A. Hankins, and J. M. Patel. Efficient aggregation
+       for graph summarization. In Proc. 2008 ACM-SIGMOD Int. Conf.
+       Management of Data (SIGMOD’08), pages 567–580, Vancouver, Canada,
+       June 2008.
+    """
+    edge_types = {
+        edge: tuple(attrs.get(attr) for attr in edge_attributes)
+        for edge, attrs in G.edges.items()
+    }
+    if not G.is_directed():
+        if G.is_multigraph():
+            # list is needed to avoid mutating while iterating
+            edges = [((v, u, k), etype) for (u, v, k), etype in edge_types.items()]
+        else:
+            # list is needed to avoid mutating while iterating
+            edges = [((v, u), etype) for (u, v), etype in edge_types.items()]
+        edge_types.update(edges)
+
+    group_lookup = {
+        node: tuple(attrs[attr] for attr in node_attributes)
+        for node, attrs in G.nodes.items()
+    }
+    groups = defaultdict(set)
+    for node, node_type in group_lookup.items():
+        groups[node_type].add(node)
+
+    eligible_group_id, nbr_info = _snap_eligible_group(
+        G, groups, group_lookup, edge_types
+    )
+    while eligible_group_id:
+        groups = _snap_split(groups, nbr_info, group_lookup, eligible_group_id)
+        eligible_group_id, nbr_info = _snap_eligible_group(
+            G, groups, group_lookup, edge_types
+        )
+    return _snap_build_graph(
+        G,
+        groups,
+        node_attributes,
+        edge_attributes,
+        nbr_info,
+        edge_types,
+        prefix,
+        supernode_attribute,
+        superedge_attribute,
+    )
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/swap.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/swap.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb3cc1c0e75c375ae49976e21fcccf2dc6c76231
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/swap.py
@@ -0,0 +1,406 @@
+"""Swap edges in a graph."""
+
+import math
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["double_edge_swap", "connected_double_edge_swap", "directed_edge_swap"]
+
+
+@nx.utils.not_implemented_for("undirected")
+@py_random_state(3)
+@nx._dispatchable(mutates_input=True, returns_graph=True)
+def directed_edge_swap(G, *, nswap=1, max_tries=100, seed=None):
+    """Swap three edges in a directed graph while keeping the node degrees fixed.
+
+    A directed edge swap swaps three edges such that a -> b -> c -> d becomes
+    a -> c -> b -> d. This pattern of swapping allows all possible states with the
+    same in- and out-degree distribution in a directed graph to be reached.
+
+    If the swap would create parallel edges (e.g. if a -> c already existed in the
+    previous example), another attempt is made to find a suitable trio of edges.
+
+    Parameters
+    ----------
+    G : DiGraph
+       A directed graph
+
+    nswap : integer (optional, default=1)
+       Number of three-edge (directed) swaps to perform
+
+    max_tries : integer (optional, default=100)
+       Maximum number of attempts to swap edges
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : DiGraph
+       The graph after the edges are swapped.
+
+    Raises
+    ------
+    NetworkXError
+        If `G` is not directed, or
+        If nswap > max_tries, or
+        If there are fewer than 4 nodes or 3 edges in `G`.
+    NetworkXAlgorithmError
+        If the number of swap attempts exceeds `max_tries` before `nswap` swaps are made
+
+    Notes
+    -----
+    Does not enforce any connectivity constraints.
+
+    The graph G is modified in place.
+
+    A later swap is allowed to undo a previous swap.
+
+    References
+    ----------
+    .. [1] Erdős, Péter L., et al. “A Simple Havel-Hakimi Type Algorithm to Realize
+           Graphical Degree Sequences of Directed Graphs.” ArXiv:0905.4913 [Math],
+           Jan. 2010. https://doi.org/10.48550/arXiv.0905.4913.
+           Published  2010 in Elec. J. Combinatorics (17(1)). R66.
+           http://www.combinatorics.org/Volume_17/PDF/v17i1r66.pdf
+    .. [2] “Combinatorics - Reaching All Possible Simple Directed Graphs with a given
+           Degree Sequence with 2-Edge Swaps.” Mathematics Stack Exchange,
+           https://math.stackexchange.com/questions/22272/. Accessed 30 May 2022.
+    """
+    if nswap > max_tries:
+        raise nx.NetworkXError("Number of swaps > number of tries allowed.")
+    if len(G) < 4:
+        raise nx.NetworkXError("DiGraph has fewer than four nodes.")
+    if len(G.edges) < 3:
+        raise nx.NetworkXError("DiGraph has fewer than 3 edges")
+
+    # Instead of choosing uniformly at random from a generated edge list,
+    # this algorithm chooses nonuniformly from the set of nodes with
+    # probability weighted by degree.
+    tries = 0
+    swapcount = 0
+    keys, degrees = zip(*G.degree())  # keys, degree
+    cdf = nx.utils.cumulative_distribution(degrees)  # cdf of degree
+    discrete_sequence = nx.utils.discrete_sequence
+
+    while swapcount < nswap:
+        # choose source node index from discrete distribution
+        start_index = discrete_sequence(1, cdistribution=cdf, seed=seed)[0]
+        start = keys[start_index]
+        tries += 1
+
+        if tries > max_tries:
+            msg = f"Maximum number of swap attempts ({tries}) exceeded before desired swaps achieved ({nswap})."
+            raise nx.NetworkXAlgorithmError(msg)
+
+        # If the given node doesn't have any out edges, then there isn't anything to swap
+        if G.out_degree(start) == 0:
+            continue
+        second = seed.choice(list(G.succ[start]))
+        if start == second:
+            continue
+
+        if G.out_degree(second) == 0:
+            continue
+        third = seed.choice(list(G.succ[second]))
+        if second == third:
+            continue
+
+        if G.out_degree(third) == 0:
+            continue
+        fourth = seed.choice(list(G.succ[third]))
+        if third == fourth:
+            continue
+
+        if (
+            third not in G.succ[start]
+            and fourth not in G.succ[second]
+            and second not in G.succ[third]
+        ):
+            # Swap nodes
+            G.add_edge(start, third)
+            G.add_edge(third, second)
+            G.add_edge(second, fourth)
+            G.remove_edge(start, second)
+            G.remove_edge(second, third)
+            G.remove_edge(third, fourth)
+            swapcount += 1
+
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(mutates_input=True, returns_graph=True)
+def double_edge_swap(G, nswap=1, max_tries=100, seed=None):
+    """Swap two edges in the graph while keeping the node degrees fixed.
+
+    A double-edge swap removes two randomly chosen edges u-v and x-y
+    and creates the new edges u-x and v-y::
+
+     u--v            u  v
+            becomes  |  |
+     x--y            x  y
+
+    If either the edge u-x or v-y already exist no swap is performed
+    and another attempt is made to find a suitable edge pair.
+
+    Parameters
+    ----------
+    G : graph
+       An undirected graph
+
+    nswap : integer (optional, default=1)
+       Number of double-edge swaps to perform
+
+    max_tries : integer (optional)
+       Maximum number of attempts to swap edges
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : graph
+       The graph after double edge swaps.
+
+    Raises
+    ------
+    NetworkXError
+        If `G` is directed, or
+        If `nswap` > `max_tries`, or
+        If there are fewer than 4 nodes or 2 edges in `G`.
+    NetworkXAlgorithmError
+        If the number of swap attempts exceeds `max_tries` before `nswap` swaps are made
+
+    Notes
+    -----
+    Does not enforce any connectivity constraints.
+
+    The graph G is modified in place.
+    """
+    if G.is_directed():
+        raise nx.NetworkXError(
+            "double_edge_swap() not defined for directed graphs. Use directed_edge_swap instead."
+        )
+    if nswap > max_tries:
+        raise nx.NetworkXError("Number of swaps > number of tries allowed.")
+    if len(G) < 4:
+        raise nx.NetworkXError("Graph has fewer than four nodes.")
+    if len(G.edges) < 2:
+        raise nx.NetworkXError("Graph has fewer than 2 edges")
+    # Instead of choosing uniformly at random from a generated edge list,
+    # this algorithm chooses nonuniformly from the set of nodes with
+    # probability weighted by degree.
+    n = 0
+    swapcount = 0
+    keys, degrees = zip(*G.degree())  # keys, degree
+    cdf = nx.utils.cumulative_distribution(degrees)  # cdf of degree
+    discrete_sequence = nx.utils.discrete_sequence
+    while swapcount < nswap:
+        #        if random.random() < 0.5: continue # trick to avoid periodicities?
+        # pick two random edges without creating edge list
+        # choose source node indices from discrete distribution
+        (ui, xi) = discrete_sequence(2, cdistribution=cdf, seed=seed)
+        if ui == xi:
+            continue  # same source, skip
+        u = keys[ui]  # convert index to label
+        x = keys[xi]
+        # choose target uniformly from neighbors
+        v = seed.choice(list(G[u]))
+        y = seed.choice(list(G[x]))
+        if v == y:
+            continue  # same target, skip
+        if (x not in G[u]) and (y not in G[v]):  # don't create parallel edges
+            G.add_edge(u, x)
+            G.add_edge(v, y)
+            G.remove_edge(u, v)
+            G.remove_edge(x, y)
+            swapcount += 1
+        if n >= max_tries:
+            e = (
+                f"Maximum number of swap attempts ({n}) exceeded "
+                f"before desired swaps achieved ({nswap})."
+            )
+            raise nx.NetworkXAlgorithmError(e)
+        n += 1
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(mutates_input=True)
+def connected_double_edge_swap(G, nswap=1, _window_threshold=3, seed=None):
+    """Attempts the specified number of double-edge swaps in the graph `G`.
+
+    A double-edge swap removes two randomly chosen edges `(u, v)` and `(x,
+    y)` and creates the new edges `(u, x)` and `(v, y)`::
+
+     u--v            u  v
+            becomes  |  |
+     x--y            x  y
+
+    If either `(u, x)` or `(v, y)` already exist, then no swap is performed
+    so the actual number of swapped edges is always *at most* `nswap`.
+
+    Parameters
+    ----------
+    G : graph
+       An undirected graph
+
+    nswap : integer (optional, default=1)
+       Number of double-edge swaps to perform
+
+    _window_threshold : integer
+
+       The window size below which connectedness of the graph will be checked
+       after each swap.
+
+       The "window" in this function is a dynamically updated integer that
+       represents the number of swap attempts to make before checking if the
+       graph remains connected. It is an optimization used to decrease the
+       running time of the algorithm in exchange for increased complexity of
+       implementation.
+
+       If the window size is below this threshold, then the algorithm checks
+       after each swap if the graph remains connected by checking if there is a
+       path joining the two nodes whose edge was just removed. If the window
+       size is above this threshold, then the algorithm performs do all the
+       swaps in the window and only then check if the graph is still connected.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    int
+       The number of successful swaps
+
+    Raises
+    ------
+
+    NetworkXError
+
+       If the input graph is not connected, or if the graph has fewer than four
+       nodes.
+
+    Notes
+    -----
+
+    The initial graph `G` must be connected, and the resulting graph is
+    connected. The graph `G` is modified in place.
+
+    References
+    ----------
+    .. [1] C. Gkantsidis and M. Mihail and E. Zegura,
+           The Markov chain simulation method for generating connected
+           power law random graphs, 2003.
+           http://citeseer.ist.psu.edu/gkantsidis03markov.html
+    """
+    if not nx.is_connected(G):
+        raise nx.NetworkXError("Graph not connected")
+    if len(G) < 4:
+        raise nx.NetworkXError("Graph has fewer than four nodes.")
+    n = 0
+    swapcount = 0
+    deg = G.degree()
+    # Label key for nodes
+    dk = [n for n, d in G.degree()]
+    cdf = nx.utils.cumulative_distribution([d for n, d in G.degree()])
+    discrete_sequence = nx.utils.discrete_sequence
+    window = 1
+    while n < nswap:
+        wcount = 0
+        swapped = []
+        # If the window is small, we just check each time whether the graph is
+        # connected by checking if the nodes that were just separated are still
+        # connected.
+        if window < _window_threshold:
+            # This Boolean keeps track of whether there was a failure or not.
+            fail = False
+            while wcount < window and n < nswap:
+                # Pick two random edges without creating the edge list. Choose
+                # source nodes from the discrete degree distribution.
+                (ui, xi) = discrete_sequence(2, cdistribution=cdf, seed=seed)
+                # If the source nodes are the same, skip this pair.
+                if ui == xi:
+                    continue
+                # Convert an index to a node label.
+                u = dk[ui]
+                x = dk[xi]
+                # Choose targets uniformly from neighbors.
+                v = seed.choice(list(G.neighbors(u)))
+                y = seed.choice(list(G.neighbors(x)))
+                # If the target nodes are the same, skip this pair.
+                if v == y:
+                    continue
+                if x not in G[u] and y not in G[v]:
+                    G.remove_edge(u, v)
+                    G.remove_edge(x, y)
+                    G.add_edge(u, x)
+                    G.add_edge(v, y)
+                    swapped.append((u, v, x, y))
+                    swapcount += 1
+                n += 1
+                # If G remains connected...
+                if nx.has_path(G, u, v):
+                    wcount += 1
+                # Otherwise, undo the changes.
+                else:
+                    G.add_edge(u, v)
+                    G.add_edge(x, y)
+                    G.remove_edge(u, x)
+                    G.remove_edge(v, y)
+                    swapcount -= 1
+                    fail = True
+            # If one of the swaps failed, reduce the window size.
+            if fail:
+                window = math.ceil(window / 2)
+            else:
+                window += 1
+        # If the window is large, then there is a good chance that a bunch of
+        # swaps will work. It's quicker to do all those swaps first and then
+        # check if the graph remains connected.
+        else:
+            while wcount < window and n < nswap:
+                # Pick two random edges without creating the edge list. Choose
+                # source nodes from the discrete degree distribution.
+                (ui, xi) = discrete_sequence(2, cdistribution=cdf, seed=seed)
+                # If the source nodes are the same, skip this pair.
+                if ui == xi:
+                    continue
+                # Convert an index to a node label.
+                u = dk[ui]
+                x = dk[xi]
+                # Choose targets uniformly from neighbors.
+                v = seed.choice(list(G.neighbors(u)))
+                y = seed.choice(list(G.neighbors(x)))
+                # If the target nodes are the same, skip this pair.
+                if v == y:
+                    continue
+                if x not in G[u] and y not in G[v]:
+                    G.remove_edge(u, v)
+                    G.remove_edge(x, y)
+                    G.add_edge(u, x)
+                    G.add_edge(v, y)
+                    swapped.append((u, v, x, y))
+                    swapcount += 1
+                n += 1
+                wcount += 1
+            # If the graph remains connected, increase the window size.
+            if nx.is_connected(G):
+                window += 1
+            # Otherwise, undo the changes from the previous window and decrease
+            # the window size.
+            else:
+                while swapped:
+                    (u, v, x, y) = swapped.pop()
+                    G.add_edge(u, v)
+                    G.add_edge(x, y)
+                    G.remove_edge(u, x)
+                    G.remove_edge(v, y)
+                    swapcount -= 1
+                window = math.ceil(window / 2)
+    return swapcount
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/threshold.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/threshold.py
new file mode 100644
index 0000000000000000000000000000000000000000..21705cc0e06f56e77d4fae8cab3618dea81a0603
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/threshold.py
@@ -0,0 +1,1035 @@
+"""
+Threshold Graphs - Creation, manipulation and identification.
+"""
+
+from math import sqrt
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["is_threshold_graph", "find_threshold_graph"]
+
+
+@nx._dispatchable
+def is_threshold_graph(G):
+    """
+    Returns `True` if `G` is a threshold graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph instance
+        An instance of `Graph`, `DiGraph`, `MultiGraph` or `MultiDiGraph`
+
+    Returns
+    -------
+    bool
+        `True` if `G` is a threshold graph, `False` otherwise.
+
+    Examples
+    --------
+    >>> from networkx.algorithms.threshold import is_threshold_graph
+    >>> G = nx.path_graph(3)
+    >>> is_threshold_graph(G)
+    True
+    >>> G = nx.barbell_graph(3, 3)
+    >>> is_threshold_graph(G)
+    False
+
+    References
+    ----------
+    .. [1] Threshold graphs: https://en.wikipedia.org/wiki/Threshold_graph
+    """
+    return is_threshold_sequence([d for n, d in G.degree()])
+
+
+def is_threshold_sequence(degree_sequence):
+    """
+    Returns True if the sequence is a threshold degree sequence.
+
+    Uses the property that a threshold graph must be constructed by
+    adding either dominating or isolated nodes. Thus, it can be
+    deconstructed iteratively by removing a node of degree zero or a
+    node that connects to the remaining nodes.  If this deconstruction
+    fails then the sequence is not a threshold sequence.
+    """
+    ds = degree_sequence[:]  # get a copy so we don't destroy original
+    ds.sort()
+    while ds:
+        if ds[0] == 0:  # if isolated node
+            ds.pop(0)  # remove it
+            continue
+        if ds[-1] != len(ds) - 1:  # is the largest degree node dominating?
+            return False  # no, not a threshold degree sequence
+        ds.pop()  # yes, largest is the dominating node
+        ds = [d - 1 for d in ds]  # remove it and decrement all degrees
+    return True
+
+
+def creation_sequence(degree_sequence, with_labels=False, compact=False):
+    """
+    Determines the creation sequence for the given threshold degree sequence.
+
+    The creation sequence is a list of single characters 'd'
+    or 'i': 'd' for dominating or 'i' for isolated vertices.
+    Dominating vertices are connected to all vertices present when it
+    is added.  The first node added is by convention 'd'.
+    This list can be converted to a string if desired using "".join(cs)
+
+    If with_labels==True:
+    Returns a list of 2-tuples containing the vertex number
+    and a character 'd' or 'i' which describes the type of vertex.
+
+    If compact==True:
+    Returns the creation sequence in a compact form that is the number
+    of 'i's and 'd's alternating.
+    Examples:
+    [1,2,2,3] represents d,i,i,d,d,i,i,i
+    [3,1,2] represents d,d,d,i,d,d
+
+    Notice that the first number is the first vertex to be used for
+    construction and so is always 'd'.
+
+    with_labels and compact cannot both be True.
+
+    Returns None if the sequence is not a threshold sequence
+    """
+    if with_labels and compact:
+        raise ValueError("compact sequences cannot be labeled")
+
+    # make an indexed copy
+    if isinstance(degree_sequence, dict):  # labeled degree sequence
+        ds = [[degree, label] for (label, degree) in degree_sequence.items()]
+    else:
+        ds = [[d, i] for i, d in enumerate(degree_sequence)]
+    ds.sort()
+    cs = []  # creation sequence
+    while ds:
+        if ds[0][0] == 0:  # isolated node
+            (d, v) = ds.pop(0)
+            if len(ds) > 0:  # make sure we start with a d
+                cs.insert(0, (v, "i"))
+            else:
+                cs.insert(0, (v, "d"))
+            continue
+        if ds[-1][0] != len(ds) - 1:  # Not dominating node
+            return None  # not a threshold degree sequence
+        (d, v) = ds.pop()
+        cs.insert(0, (v, "d"))
+        ds = [[d[0] - 1, d[1]] for d in ds]  # decrement due to removing node
+
+    if with_labels:
+        return cs
+    if compact:
+        return make_compact(cs)
+    return [v[1] for v in cs]  # not labeled
+
+
+def make_compact(creation_sequence):
+    """
+    Returns the creation sequence in a compact form
+    that is the number of 'i's and 'd's alternating.
+
+    Examples
+    --------
+    >>> from networkx.algorithms.threshold import make_compact
+    >>> make_compact(["d", "i", "i", "d", "d", "i", "i", "i"])
+    [1, 2, 2, 3]
+    >>> make_compact(["d", "d", "d", "i", "d", "d"])
+    [3, 1, 2]
+
+    Notice that the first number is the first vertex
+    to be used for construction and so is always 'd'.
+
+    Labeled creation sequences lose their labels in the
+    compact representation.
+
+    >>> make_compact([3, 1, 2])
+    [3, 1, 2]
+    """
+    first = creation_sequence[0]
+    if isinstance(first, str):  # creation sequence
+        cs = creation_sequence[:]
+    elif isinstance(first, tuple):  # labeled creation sequence
+        cs = [s[1] for s in creation_sequence]
+    elif isinstance(first, int):  # compact creation sequence
+        return creation_sequence
+    else:
+        raise TypeError("Not a valid creation sequence type")
+
+    ccs = []
+    count = 1  # count the run lengths of d's or i's.
+    for i in range(1, len(cs)):
+        if cs[i] == cs[i - 1]:
+            count += 1
+        else:
+            ccs.append(count)
+            count = 1
+    ccs.append(count)  # don't forget the last one
+    return ccs
+
+
+def uncompact(creation_sequence):
+    """
+    Converts a compact creation sequence for a threshold
+    graph to a standard creation sequence (unlabeled).
+    If the creation_sequence is already standard, return it.
+    See creation_sequence.
+    """
+    first = creation_sequence[0]
+    if isinstance(first, str):  # creation sequence
+        return creation_sequence
+    elif isinstance(first, tuple):  # labeled creation sequence
+        return creation_sequence
+    elif isinstance(first, int):  # compact creation sequence
+        ccscopy = creation_sequence[:]
+    else:
+        raise TypeError("Not a valid creation sequence type")
+    cs = []
+    while ccscopy:
+        cs.extend(ccscopy.pop(0) * ["d"])
+        if ccscopy:
+            cs.extend(ccscopy.pop(0) * ["i"])
+    return cs
+
+
+def creation_sequence_to_weights(creation_sequence):
+    """
+    Returns a list of node weights which create the threshold
+    graph designated by the creation sequence.  The weights
+    are scaled so that the threshold is 1.0.  The order of the
+    nodes is the same as that in the creation sequence.
+    """
+    # Turn input sequence into a labeled creation sequence
+    first = creation_sequence[0]
+    if isinstance(first, str):  # creation sequence
+        if isinstance(creation_sequence, list):
+            wseq = creation_sequence[:]
+        else:
+            wseq = list(creation_sequence)  # string like 'ddidid'
+    elif isinstance(first, tuple):  # labeled creation sequence
+        wseq = [v[1] for v in creation_sequence]
+    elif isinstance(first, int):  # compact creation sequence
+        wseq = uncompact(creation_sequence)
+    else:
+        raise TypeError("Not a valid creation sequence type")
+    # pass through twice--first backwards
+    wseq.reverse()
+    w = 0
+    prev = "i"
+    for j, s in enumerate(wseq):
+        if s == "i":
+            wseq[j] = w
+            prev = s
+        elif prev == "i":
+            prev = s
+            w += 1
+    wseq.reverse()  # now pass through forwards
+    for j, s in enumerate(wseq):
+        if s == "d":
+            wseq[j] = w
+            prev = s
+        elif prev == "d":
+            prev = s
+            w += 1
+    # Now scale weights
+    if prev == "d":
+        w += 1
+    wscale = 1 / w
+    return [ww * wscale for ww in wseq]
+    # return wseq
+
+
+def weights_to_creation_sequence(
+    weights, threshold=1, with_labels=False, compact=False
+):
+    """
+    Returns a creation sequence for a threshold graph
+    determined by the weights and threshold given as input.
+    If the sum of two node weights is greater than the
+    threshold value, an edge is created between these nodes.
+
+    The creation sequence is a list of single characters 'd'
+    or 'i': 'd' for dominating or 'i' for isolated vertices.
+    Dominating vertices are connected to all vertices present
+    when it is added.  The first node added is by convention 'd'.
+
+    If with_labels==True:
+    Returns a list of 2-tuples containing the vertex number
+    and a character 'd' or 'i' which describes the type of vertex.
+
+    If compact==True:
+    Returns the creation sequence in a compact form that is the number
+    of 'i's and 'd's alternating.
+    Examples:
+    [1,2,2,3] represents d,i,i,d,d,i,i,i
+    [3,1,2] represents d,d,d,i,d,d
+
+    Notice that the first number is the first vertex to be used for
+    construction and so is always 'd'.
+
+    with_labels and compact cannot both be True.
+    """
+    if with_labels and compact:
+        raise ValueError("compact sequences cannot be labeled")
+
+    # make an indexed copy
+    if isinstance(weights, dict):  # labeled weights
+        wseq = [[w, label] for (label, w) in weights.items()]
+    else:
+        wseq = [[w, i] for i, w in enumerate(weights)]
+    wseq.sort()
+    cs = []  # creation sequence
+    cutoff = threshold - wseq[-1][0]
+    while wseq:
+        if wseq[0][0] < cutoff:  # isolated node
+            (w, label) = wseq.pop(0)
+            cs.append((label, "i"))
+        else:
+            (w, label) = wseq.pop()
+            cs.append((label, "d"))
+            cutoff = threshold - wseq[-1][0]
+        if len(wseq) == 1:  # make sure we start with a d
+            (w, label) = wseq.pop()
+            cs.append((label, "d"))
+    # put in correct order
+    cs.reverse()
+
+    if with_labels:
+        return cs
+    if compact:
+        return make_compact(cs)
+    return [v[1] for v in cs]  # not labeled
+
+
+# Manipulating NetworkX.Graphs in context of threshold graphs
+@nx._dispatchable(graphs=None, returns_graph=True)
+def threshold_graph(creation_sequence, create_using=None):
+    """
+    Create a threshold graph from the creation sequence or compact
+    creation_sequence.
+
+    The input sequence can be a
+
+    creation sequence (e.g. ['d','i','d','d','d','i'])
+    labeled creation sequence (e.g. [(0,'d'),(2,'d'),(1,'i')])
+    compact creation sequence (e.g. [2,1,1,2,0])
+
+    Use cs=creation_sequence(degree_sequence,labeled=True)
+    to convert a degree sequence to a creation sequence.
+
+    Returns None if the sequence is not valid
+    """
+    # Turn input sequence into a labeled creation sequence
+    first = creation_sequence[0]
+    if isinstance(first, str):  # creation sequence
+        ci = list(enumerate(creation_sequence))
+    elif isinstance(first, tuple):  # labeled creation sequence
+        ci = creation_sequence[:]
+    elif isinstance(first, int):  # compact creation sequence
+        cs = uncompact(creation_sequence)
+        ci = list(enumerate(cs))
+    else:
+        print("not a valid creation sequence type")
+        return None
+
+    G = nx.empty_graph(0, create_using)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    G.name = "Threshold Graph"
+
+    # add nodes and edges
+    # if type is 'i' just add nodea
+    # if type is a d connect to everything previous
+    while ci:
+        (v, node_type) = ci.pop(0)
+        if node_type == "d":  # dominating type, connect to all existing nodes
+            # We use `for u in list(G):` instead of
+            # `for u in G:` because we edit the graph `G` in
+            # the loop. Hence using an iterator will result in
+            # `RuntimeError: dictionary changed size during iteration`
+            for u in list(G):
+                G.add_edge(v, u)
+        G.add_node(v)
+    return G
+
+
+@nx._dispatchable
+def find_alternating_4_cycle(G):
+    """
+    Returns False if there aren't any alternating 4 cycles.
+    Otherwise returns the cycle as [a,b,c,d] where (a,b)
+    and (c,d) are edges and (a,c) and (b,d) are not.
+    """
+    for u, v in G.edges():
+        for w in G.nodes():
+            if not G.has_edge(u, w) and u != w:
+                for x in G.neighbors(w):
+                    if not G.has_edge(v, x) and v != x:
+                        return [u, v, w, x]
+    return False
+
+
+@nx._dispatchable(returns_graph=True)
+def find_threshold_graph(G, create_using=None):
+    """
+    Returns a threshold subgraph that is close to largest in `G`.
+
+    The threshold graph will contain the largest degree node in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph instance
+        An instance of `Graph`, or `MultiDiGraph`
+    create_using : NetworkX graph class or `None` (default), optional
+        Type of graph to use when constructing the threshold graph.
+        If `None`, infer the appropriate graph type from the input.
+
+    Returns
+    -------
+    graph :
+        A graph instance representing the threshold graph
+
+    Examples
+    --------
+    >>> from networkx.algorithms.threshold import find_threshold_graph
+    >>> G = nx.barbell_graph(3, 3)
+    >>> T = find_threshold_graph(G)
+    >>> T.nodes  # may vary
+    NodeView((7, 8, 5, 6))
+
+    References
+    ----------
+    .. [1] Threshold graphs: https://en.wikipedia.org/wiki/Threshold_graph
+    """
+    return threshold_graph(find_creation_sequence(G), create_using)
+
+
+@nx._dispatchable
+def find_creation_sequence(G):
+    """
+    Find a threshold subgraph that is close to largest in G.
+    Returns the labeled creation sequence of that threshold graph.
+    """
+    cs = []
+    # get a local pointer to the working part of the graph
+    H = G
+    while H.order() > 0:
+        # get new degree sequence on subgraph
+        dsdict = dict(H.degree())
+        ds = [(d, v) for v, d in dsdict.items()]
+        ds.sort()
+        # Update threshold graph nodes
+        if ds[-1][0] == 0:  # all are isolated
+            cs.extend(zip(dsdict, ["i"] * (len(ds) - 1) + ["d"]))
+            break  # Done!
+        # pull off isolated nodes
+        while ds[0][0] == 0:
+            (d, iso) = ds.pop(0)
+            cs.append((iso, "i"))
+        # find new biggest node
+        (d, bigv) = ds.pop()
+        # add edges of star to t_g
+        cs.append((bigv, "d"))
+        # form subgraph of neighbors of big node
+        H = H.subgraph(H.neighbors(bigv))
+    cs.reverse()
+    return cs
+
+
+# Properties of Threshold Graphs
+def triangles(creation_sequence):
+    """
+    Compute number of triangles in the threshold graph with the
+    given creation sequence.
+    """
+    # shortcut algorithm that doesn't require computing number
+    # of triangles at each node.
+    cs = creation_sequence  # alias
+    dr = cs.count("d")  # number of d's in sequence
+    ntri = dr * (dr - 1) * (dr - 2) / 6  # number of triangles in clique of nd d's
+    # now add dr choose 2 triangles for every 'i' in sequence where
+    # dr is the number of d's to the right of the current i
+    for i, typ in enumerate(cs):
+        if typ == "i":
+            ntri += dr * (dr - 1) / 2
+        else:
+            dr -= 1
+    return ntri
+
+
+def triangle_sequence(creation_sequence):
+    """
+    Return triangle sequence for the given threshold graph creation sequence.
+
+    """
+    cs = creation_sequence
+    seq = []
+    dr = cs.count("d")  # number of d's to the right of the current pos
+    dcur = (dr - 1) * (dr - 2) // 2  # number of triangles through a node of clique dr
+    irun = 0  # number of i's in the last run
+    drun = 0  # number of d's in the last run
+    for i, sym in enumerate(cs):
+        if sym == "d":
+            drun += 1
+            tri = dcur + (dr - 1) * irun  # new triangles at this d
+        else:  # cs[i]="i":
+            if prevsym == "d":  # new string of i's
+                dcur += (dr - 1) * irun  # accumulate shared shortest paths
+                irun = 0  # reset i run counter
+                dr -= drun  # reduce number of d's to right
+                drun = 0  # reset d run counter
+            irun += 1
+            tri = dr * (dr - 1) // 2  # new triangles at this i
+        seq.append(tri)
+        prevsym = sym
+    return seq
+
+
+def cluster_sequence(creation_sequence):
+    """
+    Return cluster sequence for the given threshold graph creation sequence.
+    """
+    triseq = triangle_sequence(creation_sequence)
+    degseq = degree_sequence(creation_sequence)
+    cseq = []
+    for i, deg in enumerate(degseq):
+        tri = triseq[i]
+        if deg <= 1:  # isolated vertex or single pair gets cc 0
+            cseq.append(0)
+            continue
+        max_size = (deg * (deg - 1)) // 2
+        cseq.append(tri / max_size)
+    return cseq
+
+
+def degree_sequence(creation_sequence):
+    """
+    Return degree sequence for the threshold graph with the given
+    creation sequence
+    """
+    cs = creation_sequence  # alias
+    seq = []
+    rd = cs.count("d")  # number of d to the right
+    for i, sym in enumerate(cs):
+        if sym == "d":
+            rd -= 1
+            seq.append(rd + i)
+        else:
+            seq.append(rd)
+    return seq
+
+
+def density(creation_sequence):
+    """
+    Return the density of the graph with this creation_sequence.
+    The density is the fraction of possible edges present.
+    """
+    N = len(creation_sequence)
+    two_size = sum(degree_sequence(creation_sequence))
+    two_possible = N * (N - 1)
+    den = two_size / two_possible
+    return den
+
+
+def degree_correlation(creation_sequence):
+    """
+    Return the degree-degree correlation over all edges.
+    """
+    cs = creation_sequence
+    s1 = 0  # deg_i*deg_j
+    s2 = 0  # deg_i^2+deg_j^2
+    s3 = 0  # deg_i+deg_j
+    m = 0  # number of edges
+    rd = cs.count("d")  # number of d nodes to the right
+    rdi = [i for i, sym in enumerate(cs) if sym == "d"]  # index of "d"s
+    ds = degree_sequence(cs)
+    for i, sym in enumerate(cs):
+        if sym == "d":
+            if i != rdi[0]:
+                print("Logic error in degree_correlation", i, rdi)
+                raise ValueError
+            rdi.pop(0)
+        degi = ds[i]
+        for dj in rdi:
+            degj = ds[dj]
+            s1 += degj * degi
+            s2 += degi**2 + degj**2
+            s3 += degi + degj
+            m += 1
+    denom = 2 * m * s2 - s3 * s3
+    numer = 4 * m * s1 - s3 * s3
+    if denom == 0:
+        if numer == 0:
+            return 1
+        raise ValueError(f"Zero Denominator but Numerator is {numer}")
+    return numer / denom
+
+
+def shortest_path(creation_sequence, u, v):
+    """
+    Find the shortest path between u and v in a
+    threshold graph G with the given creation_sequence.
+
+    For an unlabeled creation_sequence, the vertices
+    u and v must be integers in (0,len(sequence)) referring
+    to the position of the desired vertices in the sequence.
+
+    For a labeled creation_sequence, u and v are labels of vertices.
+
+    Use cs=creation_sequence(degree_sequence,with_labels=True)
+    to convert a degree sequence to a creation sequence.
+
+    Returns a list of vertices from u to v.
+    Example: if they are neighbors, it returns [u,v]
+    """
+    # Turn input sequence into a labeled creation sequence
+    first = creation_sequence[0]
+    if isinstance(first, str):  # creation sequence
+        cs = [(i, creation_sequence[i]) for i in range(len(creation_sequence))]
+    elif isinstance(first, tuple):  # labeled creation sequence
+        cs = creation_sequence[:]
+    elif isinstance(first, int):  # compact creation sequence
+        ci = uncompact(creation_sequence)
+        cs = [(i, ci[i]) for i in range(len(ci))]
+    else:
+        raise TypeError("Not a valid creation sequence type")
+
+    verts = [s[0] for s in cs]
+    if v not in verts:
+        raise ValueError(f"Vertex {v} not in graph from creation_sequence")
+    if u not in verts:
+        raise ValueError(f"Vertex {u} not in graph from creation_sequence")
+    # Done checking
+    if u == v:
+        return [u]
+
+    uindex = verts.index(u)
+    vindex = verts.index(v)
+    bigind = max(uindex, vindex)
+    if cs[bigind][1] == "d":
+        return [u, v]
+    # must be that cs[bigind][1]=='i'
+    cs = cs[bigind:]
+    while cs:
+        vert = cs.pop()
+        if vert[1] == "d":
+            return [u, vert[0], v]
+    # All after u are type 'i' so no connection
+    return -1
+
+
+def shortest_path_length(creation_sequence, i):
+    """
+    Return the shortest path length from indicated node to
+    every other node for the threshold graph with the given
+    creation sequence.
+    Node is indicated by index i in creation_sequence unless
+    creation_sequence is labeled in which case, i is taken to
+    be the label of the node.
+
+    Paths lengths in threshold graphs are at most 2.
+    Length to unreachable nodes is set to -1.
+    """
+    # Turn input sequence into a labeled creation sequence
+    first = creation_sequence[0]
+    if isinstance(first, str):  # creation sequence
+        if isinstance(creation_sequence, list):
+            cs = creation_sequence[:]
+        else:
+            cs = list(creation_sequence)
+    elif isinstance(first, tuple):  # labeled creation sequence
+        cs = [v[1] for v in creation_sequence]
+        i = [v[0] for v in creation_sequence].index(i)
+    elif isinstance(first, int):  # compact creation sequence
+        cs = uncompact(creation_sequence)
+    else:
+        raise TypeError("Not a valid creation sequence type")
+
+    # Compute
+    N = len(cs)
+    spl = [2] * N  # length 2 to every node
+    spl[i] = 0  # except self which is 0
+    # 1 for all d's to the right
+    for j in range(i + 1, N):
+        if cs[j] == "d":
+            spl[j] = 1
+    if cs[i] == "d":  # 1 for all nodes to the left
+        for j in range(i):
+            spl[j] = 1
+    # and -1 for any trailing i to indicate unreachable
+    for j in range(N - 1, 0, -1):
+        if cs[j] == "d":
+            break
+        spl[j] = -1
+    return spl
+
+
+def betweenness_sequence(creation_sequence, normalized=True):
+    """
+    Return betweenness for the threshold graph with the given creation
+    sequence.  The result is unscaled.  To scale the values
+    to the interval [0,1] divide by (n-1)*(n-2).
+    """
+    cs = creation_sequence
+    seq = []  # betweenness
+    lastchar = "d"  # first node is always a 'd'
+    dr = float(cs.count("d"))  # number of d's to the right of current pos
+    irun = 0  # number of i's in the last run
+    drun = 0  # number of d's in the last run
+    dlast = 0.0  # betweenness of last d
+    for i, c in enumerate(cs):
+        if c == "d":  # cs[i]=="d":
+            # betweenness = amt shared with earlier d's and i's
+            #             + new isolated nodes covered
+            #             + new paths to all previous nodes
+            b = dlast + (irun - 1) * irun / dr + 2 * irun * (i - drun - irun) / dr
+            drun += 1  # update counter
+        else:  # cs[i]="i":
+            if lastchar == "d":  # if this is a new run of i's
+                dlast = b  # accumulate betweenness
+                dr -= drun  # update number of d's to the right
+                drun = 0  # reset d counter
+                irun = 0  # reset i counter
+            b = 0  # isolated nodes have zero betweenness
+            irun += 1  # add another i to the run
+        seq.append(float(b))
+        lastchar = c
+
+    # normalize by the number of possible shortest paths
+    if normalized:
+        order = len(cs)
+        scale = 1.0 / ((order - 1) * (order - 2))
+        seq = [s * scale for s in seq]
+
+    return seq
+
+
+def eigenvectors(creation_sequence):
+    """
+    Return a 2-tuple of Laplacian eigenvalues and eigenvectors
+    for the threshold network with creation_sequence.
+    The first value is a list of eigenvalues.
+    The second value is a list of eigenvectors.
+    The lists are in the same order so corresponding eigenvectors
+    and eigenvalues are in the same position in the two lists.
+
+    Notice that the order of the eigenvalues returned by eigenvalues(cs)
+    may not correspond to the order of these eigenvectors.
+    """
+    ccs = make_compact(creation_sequence)
+    N = sum(ccs)
+    vec = [0] * N
+    val = vec[:]
+    # get number of type d nodes to the right (all for first node)
+    dr = sum(ccs[::2])
+
+    nn = ccs[0]
+    vec[0] = [1.0 / sqrt(N)] * N
+    val[0] = 0
+    e = dr
+    dr -= nn
+    type_d = True
+    i = 1
+    dd = 1
+    while dd < nn:
+        scale = 1.0 / sqrt(dd * dd + i)
+        vec[i] = i * [-scale] + [dd * scale] + [0] * (N - i - 1)
+        val[i] = e
+        i += 1
+        dd += 1
+    if len(ccs) == 1:
+        return (val, vec)
+    for nn in ccs[1:]:
+        scale = 1.0 / sqrt(nn * i * (i + nn))
+        vec[i] = i * [-nn * scale] + nn * [i * scale] + [0] * (N - i - nn)
+        # find eigenvalue
+        type_d = not type_d
+        if type_d:
+            e = i + dr
+            dr -= nn
+        else:
+            e = dr
+        val[i] = e
+        st = i
+        i += 1
+        dd = 1
+        while dd < nn:
+            scale = 1.0 / sqrt(i - st + dd * dd)
+            vec[i] = [0] * st + (i - st) * [-scale] + [dd * scale] + [0] * (N - i - 1)
+            val[i] = e
+            i += 1
+            dd += 1
+    return (val, vec)
+
+
+def spectral_projection(u, eigenpairs):
+    """
+    Returns the coefficients of each eigenvector
+    in a projection of the vector u onto the normalized
+    eigenvectors which are contained in eigenpairs.
+
+    eigenpairs should be a list of two objects.  The
+    first is a list of eigenvalues and the second a list
+    of eigenvectors.  The eigenvectors should be lists.
+
+    There's not a lot of error checking on lengths of
+    arrays, etc. so be careful.
+    """
+    coeff = []
+    evect = eigenpairs[1]
+    for ev in evect:
+        c = sum(evv * uv for (evv, uv) in zip(ev, u))
+        coeff.append(c)
+    return coeff
+
+
+def eigenvalues(creation_sequence):
+    """
+    Return sequence of eigenvalues of the Laplacian of the threshold
+    graph for the given creation_sequence.
+
+    Based on the Ferrer's diagram method.  The spectrum is integral
+    and is the conjugate of the degree sequence.
+
+    See::
+
+      @Article{degree-merris-1994,
+       author = {Russel Merris},
+       title = {Degree maximal graphs are Laplacian integral},
+       journal = {Linear Algebra Appl.},
+       year = {1994},
+       volume = {199},
+       pages = {381--389},
+      }
+
+    """
+    degseq = degree_sequence(creation_sequence)
+    degseq.sort()
+    eiglist = []  # zero is always one eigenvalue
+    eig = 0
+    row = len(degseq)
+    bigdeg = degseq.pop()
+    while row:
+        if bigdeg < row:
+            eiglist.append(eig)
+            row -= 1
+        else:
+            eig += 1
+            if degseq:
+                bigdeg = degseq.pop()
+            else:
+                bigdeg = 0
+    return eiglist
+
+
+# Threshold graph creation routines
+
+
+@py_random_state(2)
+def random_threshold_sequence(n, p, seed=None):
+    """
+    Create a random threshold sequence of size n.
+    A creation sequence is built by randomly choosing d's with
+    probability p and i's with probability 1-p.
+
+    s=nx.random_threshold_sequence(10,0.5)
+
+    returns a threshold sequence of length 10 with equal
+    probably of an i or a d at each position.
+
+    A "random" threshold graph can be built with
+
+    G=nx.threshold_graph(s)
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    """
+    if not (0 <= p <= 1):
+        raise ValueError("p must be in [0,1]")
+
+    cs = ["d"]  # threshold sequences always start with a d
+    for i in range(1, n):
+        if seed.random() < p:
+            cs.append("d")
+        else:
+            cs.append("i")
+    return cs
+
+
+# maybe *_d_threshold_sequence routines should
+# be (or be called from) a single routine with a more descriptive name
+# and a keyword parameter?
+def right_d_threshold_sequence(n, m):
+    """
+    Returns a "right-dominated" threshold sequence with `n` vertices and `m` edges.
+
+    Each vertex in the sequence is either dominant or isolated.
+    In the "right-dominated" version, once the basic sequence is formed,
+    isolated vertices may be flipped to dominant from the right in order
+    to reach the target number of edges.
+
+    Parameters
+    ----------
+    n : int
+        Number of vertices.
+    m : int
+        Number of edges.
+
+    Returns
+    -------
+    A list of 'd' (dominant) and 'i' (isolated) forming a right-dominated threshold sequence.
+
+    Raises
+    ------
+    ValueError
+        If `m` exceeds the maximum number of edges.
+
+    Examples
+    --------
+    >>> from networkx.algorithms.threshold import right_d_threshold_sequence
+    >>> right_d_threshold_sequence(5, 3)
+    ['d', 'i', 'i', 'd', 'i']
+    """
+
+    cs = ["d"] + ["i"] * (n - 1)  # create sequence with n insolated nodes
+
+    #  m <n : not enough edges, make disconnected
+    if m < n:
+        cs[m] = "d"
+        return cs
+
+    # too many edges
+    if m > n * (n - 1) / 2:
+        raise ValueError("Too many edges for this many nodes.")
+
+    # connected case m >n-1
+    ind = n - 1
+    sum = n - 1
+    while sum < m:
+        cs[ind] = "d"
+        ind -= 1
+        sum += ind
+    ind = m - (sum - ind)
+    cs[ind] = "d"
+    return cs
+
+
+def left_d_threshold_sequence(n, m):
+    """
+    Returns a "left-dominated" threshold sequence with `n` vertices and `m` edges.
+
+    Each vertex in the sequence is either dominant or isolated.
+    In the "left-dominated" version, once the basic sequence is formed,
+    isolated vertices may be flipped to dominant from the left in order
+    to reach the target number of edges.
+
+    Parameters
+    ----------
+    n : int
+        Number of vertices.
+    m : int
+        Number of edges.
+
+    Returns
+    -------
+    A list of 'd' (dominant) and 'i' (isolated) forming a left-dominated threshold sequence.
+
+    Raises
+    ------
+    ValueError
+        If `m` exceeds the maximum number of edges.
+
+    Examples
+    --------
+    For certain small cases, both left and right dominated versions produce
+    the same sequence. However, for larger values of `m`, the difference in
+    flipping order becomes evident. For instance, compare the sequences for
+    ``n=6, m=8``:
+
+    >>> from networkx.algorithms.threshold import left_d_threshold_sequence
+    >>> seq = left_d_threshold_sequence(6, 8)
+    >>> seq
+    ['d', 'd', 'd', 'i', 'i', 'd']
+
+    In contrast, the right-dominated version yields:
+
+    >>> from networkx.algorithms.threshold import right_d_threshold_sequence
+    >>> right_seq = right_d_threshold_sequence(6, 8)
+    >>> right_seq
+    ['d', 'i', 'i', 'd', 'i', 'd']
+    """
+
+    cs = ["d"] + ["i"] * (n - 1)  # create sequence with n insolated nodes
+
+    #  m <n : not enough edges, make disconnected
+    if m < n:
+        cs[m] = "d"
+        return cs
+
+    # too many edges
+    if m > n * (n - 1) / 2:
+        raise ValueError("Too many edges for this many nodes.")
+
+    # Connected case when M>N-1
+    cs[n - 1] = "d"
+    sum = n - 1
+    ind = 1
+    while sum < m:
+        cs[ind] = "d"
+        sum += ind
+        ind += 1
+    if sum > m:  # be sure not to change the first vertex
+        cs[sum - m] = "i"
+    return cs
+
+
+@py_random_state(3)
+def swap_d(cs, p_split=1.0, p_combine=1.0, seed=None):
+    """
+    Perform a "swap" operation on a threshold sequence.
+
+    The swap preserves the number of nodes and edges
+    in the graph for the given sequence.
+    The resulting sequence is still a threshold sequence.
+
+    Perform one split and one combine operation on the
+    'd's of a creation sequence for a threshold graph.
+    This operation maintains the number of nodes and edges
+    in the graph, but shifts the edges from node to node
+    maintaining the threshold quality of the graph.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    """
+    # preprocess the creation sequence
+    dlist = [i for (i, node_type) in enumerate(cs[1:-1]) if node_type == "d"]
+    # split
+    if seed.random() < p_split:
+        choice = seed.choice(dlist)
+        split_to = seed.choice(range(choice))
+        flip_side = choice - split_to
+        if split_to != flip_side and cs[split_to] == "i" and cs[flip_side] == "i":
+            cs[choice] = "i"
+            cs[split_to] = "d"
+            cs[flip_side] = "d"
+            dlist.remove(choice)
+            # don't add or combine may reverse this action
+            # dlist.extend([split_to,flip_side])
+    #            print >>sys.stderr,"split at %s to %s and %s"%(choice,split_to,flip_side)
+    # combine
+    if seed.random() < p_combine and dlist:
+        first_choice = seed.choice(dlist)
+        second_choice = seed.choice(dlist)
+        target = first_choice + second_choice
+        if target >= len(cs) or cs[target] == "d" or first_choice == second_choice:
+            return cs
+        # OK to combine
+        cs[first_choice] = "i"
+        cs[second_choice] = "i"
+        cs[target] = "d"
+    #        print >>sys.stderr,"combine %s and %s to make %s."%(first_choice,second_choice,target)
+
+    return cs
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/time_dependent.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/time_dependent.py
new file mode 100644
index 0000000000000000000000000000000000000000..d67cdcf0b8eaecdef8497c77edd3144e96501173
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/time_dependent.py
@@ -0,0 +1,142 @@
+"""Time dependent algorithms."""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["cd_index"]
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable(node_attrs={"time": None, "weight": 1})
+def cd_index(G, node, time_delta, *, time="time", weight=None):
+    r"""Compute the CD index for `node` within the graph `G`.
+
+    Calculates the CD index for the given node of the graph,
+    considering only its predecessors who have the `time` attribute
+    smaller than or equal to the `time` attribute of the `node`
+    plus `time_delta`.
+
+    Parameters
+    ----------
+    G : graph
+       A directed networkx graph whose nodes have `time` attributes and optionally
+       `weight` attributes (if a weight is not given, it is considered 1).
+    node : node
+       The node for which the CD index is calculated.
+    time_delta : numeric or timedelta
+       Amount of time after the `time` attribute of the `node`. The value of
+       `time_delta` must support comparison with the `time` node attribute. For
+       example, if the `time` attribute of the nodes are `datetime.datetime`
+       objects, then `time_delta` should be a `datetime.timedelta` object.
+    time : string (Optional, default is "time")
+        The name of the node attribute that will be used for the calculations.
+    weight : string (Optional, default is None)
+        The name of the node attribute used as weight.
+
+    Returns
+    -------
+    float
+       The CD index calculated for the node `node` within the graph `G`.
+
+    Raises
+    ------
+    NetworkXError
+       If not all nodes have a `time` attribute or
+       `time_delta` and `time` attribute types are not compatible or
+       `n` equals 0.
+
+    NetworkXNotImplemented
+        If `G` is a non-directed graph or a multigraph.
+
+    Examples
+    --------
+    >>> from datetime import datetime, timedelta
+    >>> G = nx.DiGraph()
+    >>> nodes = {
+    ...     1: {"time": datetime(2015, 1, 1)},
+    ...     2: {"time": datetime(2012, 1, 1), "weight": 4},
+    ...     3: {"time": datetime(2010, 1, 1)},
+    ...     4: {"time": datetime(2008, 1, 1)},
+    ...     5: {"time": datetime(2014, 1, 1)},
+    ... }
+    >>> G.add_nodes_from([(n, nodes[n]) for n in nodes])
+    >>> edges = [(1, 3), (1, 4), (2, 3), (3, 4), (3, 5)]
+    >>> G.add_edges_from(edges)
+    >>> delta = timedelta(days=5 * 365)
+    >>> nx.cd_index(G, 3, time_delta=delta, time="time")
+    0.5
+    >>> nx.cd_index(G, 3, time_delta=delta, time="time", weight="weight")
+    0.12
+
+    Integers can also be used for the time values:
+    >>> node_times = {1: 2015, 2: 2012, 3: 2010, 4: 2008, 5: 2014}
+    >>> nx.set_node_attributes(G, node_times, "new_time")
+    >>> nx.cd_index(G, 3, time_delta=4, time="new_time")
+    0.5
+    >>> nx.cd_index(G, 3, time_delta=4, time="new_time", weight="weight")
+    0.12
+
+    Notes
+    -----
+    This method implements the algorithm for calculating the CD index,
+    as described in the paper by Funk and Owen-Smith [1]_. The CD index
+    is used in order to check how consolidating or destabilizing a patent
+    is, hence the nodes of the graph represent patents and the edges show
+    the citations between these patents. The mathematical model is given
+    below:
+
+    .. math::
+        CD_{t}=\frac{1}{n_{t}}\sum_{i=1}^{n}\frac{-2f_{it}b_{it}+f_{it}}{w_{it}},
+
+    where `f_{it}` equals 1 if `i` cites the focal patent else 0, `b_{it}` equals
+    1 if `i` cites any of the focal patents successors else 0, `n_{t}` is the number
+    of forward citations in `i` and `w_{it}` is a matrix of weight for patent `i`
+    at time `t`.
+
+    The `datetime.timedelta` package can lead to off-by-one issues when converting
+    from years to days. In the example above `timedelta(days=5 * 365)` looks like
+    5 years, but it isn't because of leap year days. So it gives the same result
+    as `timedelta(days=4 * 365)`. But using `timedelta(days=5 * 365 + 1)` gives
+    a 5 year delta **for this choice of years** but may not if the 5 year gap has
+    more than 1 leap year. To avoid these issues, use integers to represent years,
+    or be very careful when you convert units of time.
+
+    References
+    ----------
+    .. [1] Funk, Russell J., and Jason Owen-Smith.
+           "A dynamic network measure of technological change."
+           Management science 63, no. 3 (2017): 791-817.
+           http://russellfunk.org/cdindex/static/papers/funk_ms_2017.pdf
+
+    """
+    if not all(time in G.nodes[n] for n in G):
+        raise nx.NetworkXError("Not all nodes have a 'time' attribute.")
+
+    try:
+        # get target_date
+        target_date = G.nodes[node][time] + time_delta
+        # keep the predecessors that existed before the target date
+        pred = {i for i in G.pred[node] if G.nodes[i][time] <= target_date}
+    except:
+        raise nx.NetworkXError(
+            "Addition and comparison are not supported between 'time_delta' "
+            "and 'time' types."
+        )
+
+    # -1 if any edge between node's predecessors and node's successors, else 1
+    b = [-1 if any(j in G[i] for j in G[node]) else 1 for i in pred]
+
+    # n is size of the union of the focal node's predecessors and its successors' predecessors
+    n = len(pred.union(*(G.pred[s].keys() - {node} for s in G[node])))
+    if n == 0:
+        raise nx.NetworkXError("The cd index cannot be defined.")
+
+    # calculate cd index
+    if weight is None:
+        return round(sum(bi for bi in b) / n, 2)
+    else:
+        # If a node has the specified weight attribute, its weight is used in the calculation
+        # otherwise, a weight of 1 is assumed for that node
+        weights = [G.nodes[i].get(weight, 1) for i in pred]
+        return round(sum(bi / wt for bi, wt in zip(b, weights)) / n, 2)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/tournament.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/tournament.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccb19a4a2980a99c334b2daf30e187208bbab634
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/tournament.py
@@ -0,0 +1,404 @@
+"""Functions concerning tournament graphs.
+
+A `tournament graph`_ is a complete oriented graph. In other words, it
+is a directed graph in which there is exactly one directed edge joining
+each pair of distinct nodes. For each function in this module that
+accepts a graph as input, you must provide a tournament graph. The
+responsibility is on the caller to ensure that the graph is a tournament
+graph:
+
+    >>> G = nx.DiGraph([(0, 1), (1, 2), (2, 0)])
+    >>> nx.is_tournament(G)
+    True
+
+To access the functions in this module, you must access them through the
+:mod:`networkx.tournament` module::
+
+    >>> nx.tournament.is_reachable(G, 0, 1)
+    True
+
+.. _tournament graph: https://en.wikipedia.org/wiki/Tournament_%28graph_theory%29
+
+"""
+
+from itertools import combinations
+
+import networkx as nx
+from networkx.algorithms.simple_paths import is_simple_path as is_path
+from networkx.utils import arbitrary_element, not_implemented_for, py_random_state
+
+__all__ = [
+    "hamiltonian_path",
+    "is_reachable",
+    "is_strongly_connected",
+    "is_tournament",
+    "random_tournament",
+    "score_sequence",
+    "tournament_matrix",
+]
+
+
+def index_satisfying(iterable, condition):
+    """Returns the index of the first element in `iterable` that
+    satisfies the given condition.
+
+    If no such element is found (that is, when the iterable is
+    exhausted), this returns the length of the iterable (that is, one
+    greater than the last index of the iterable).
+
+    `iterable` must not be empty. If `iterable` is empty, this
+    function raises :exc:`ValueError`.
+
+    """
+    # Pre-condition: iterable must not be empty.
+    for i, x in enumerate(iterable):
+        if condition(x):
+            return i
+    # If we reach the end of the iterable without finding an element
+    # that satisfies the condition, return the length of the iterable,
+    # which is one greater than the index of its last element. If the
+    # iterable was empty, `i` will not be defined, so we raise an
+    # exception.
+    try:
+        return i + 1
+    except NameError as err:
+        raise ValueError("iterable must be non-empty") from err
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def is_tournament(G):
+    """Returns True if and only if `G` is a tournament.
+
+    A tournament is a directed graph, with neither self-loops nor
+    multi-edges, in which there is exactly one directed edge joining
+    each pair of distinct nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A directed graph representing a tournament.
+
+    Returns
+    -------
+    bool
+        Whether the given graph is a tournament graph.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(0, 1), (1, 2), (2, 0)])
+    >>> nx.is_tournament(G)
+    True
+
+    Notes
+    -----
+    Some definitions require a self-loop on each node, but that is not
+    the convention used here.
+
+    """
+    # In a tournament, there is exactly one directed edge joining each pair.
+    return (
+        all((v in G[u]) ^ (u in G[v]) for u, v in combinations(G, 2))
+        and nx.number_of_selfloops(G) == 0
+    )
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def hamiltonian_path(G):
+    """Returns a Hamiltonian path in the given tournament graph.
+
+    Each tournament has a Hamiltonian path. If furthermore, the
+    tournament is strongly connected, then the returned Hamiltonian path
+    is a Hamiltonian cycle (by joining the endpoints of the path).
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A directed graph representing a tournament.
+
+    Returns
+    -------
+    path : list
+        A list of nodes which form a Hamiltonian path in `G`.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)])
+    >>> nx.is_tournament(G)
+    True
+    >>> nx.tournament.hamiltonian_path(G)
+    [0, 1, 2, 3]
+
+    Notes
+    -----
+    This is a recursive implementation with an asymptotic running time
+    of $O(n^2)$, ignoring multiplicative polylogarithmic factors, where
+    $n$ is the number of nodes in the graph.
+
+    """
+    if len(G) == 0:
+        return []
+    if len(G) == 1:
+        return [arbitrary_element(G)]
+    v = arbitrary_element(G)
+    hampath = hamiltonian_path(G.subgraph(set(G) - {v}))
+    # Get the index of the first node in the path that does *not* have
+    # an edge to `v`, then insert `v` before that node.
+    index = index_satisfying(hampath, lambda u: v not in G[u])
+    hampath.insert(index, v)
+    return hampath
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_tournament(n, seed=None):
+    r"""Returns a random tournament graph on `n` nodes.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the returned graph.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : DiGraph
+        A tournament on `n` nodes, with exactly one directed edge joining
+        each pair of distinct nodes.
+
+    Notes
+    -----
+    This algorithm adds, for each pair of distinct nodes, an edge with
+    uniformly random orientation. In other words, `\binom{n}{2}` flips
+    of an unbiased coin decide the orientations of the edges in the
+    graph.
+
+    """
+    # Flip an unbiased coin for each pair of distinct nodes.
+    coins = (seed.random() for i in range((n * (n - 1)) // 2))
+    pairs = combinations(range(n), 2)
+    edges = ((u, v) if r < 0.5 else (v, u) for (u, v), r in zip(pairs, coins))
+    return nx.DiGraph(edges)
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def score_sequence(G):
+    """Returns the score sequence for the given tournament graph.
+
+    The score sequence is the sorted list of the out-degrees of the
+    nodes of the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A directed graph representing a tournament.
+
+    Returns
+    -------
+    list
+        A sorted list of the out-degrees of the nodes of `G`.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 0), (1, 3), (0, 2), (0, 3), (2, 1), (3, 2)])
+    >>> nx.is_tournament(G)
+    True
+    >>> nx.tournament.score_sequence(G)
+    [1, 1, 2, 2]
+
+    """
+    return sorted(d for v, d in G.out_degree())
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable(preserve_edge_attrs={"G": {"weight": 1}})
+def tournament_matrix(G):
+    r"""Returns the tournament matrix for the given tournament graph.
+
+    This function requires SciPy.
+
+    The *tournament matrix* of a tournament graph with edge set *E* is
+    the matrix *T* defined by
+
+    .. math::
+
+       T_{i j} =
+       \begin{cases}
+       +1 & \text{if } (i, j) \in E \\
+       -1 & \text{if } (j, i) \in E \\
+       0 & \text{if } i == j.
+       \end{cases}
+
+    An equivalent definition is `T = A - A^T`, where *A* is the
+    adjacency matrix of the graph `G`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A directed graph representing a tournament.
+
+    Returns
+    -------
+    SciPy sparse array
+        The tournament matrix of the tournament graph `G`.
+
+    Raises
+    ------
+    ImportError
+        If SciPy is not available.
+
+    """
+    A = nx.adjacency_matrix(G)
+    return A - A.T
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def is_reachable(G, s, t):
+    """Decides whether there is a path from `s` to `t` in the
+    tournament.
+
+    This function is more theoretically efficient than the reachability
+    checks than the shortest path algorithms in
+    :mod:`networkx.algorithms.shortest_paths`.
+
+    The given graph **must** be a tournament, otherwise this function's
+    behavior is undefined.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A directed graph representing a tournament.
+
+    s : node
+        A node in the graph.
+
+    t : node
+        A node in the graph.
+
+    Returns
+    -------
+    bool
+        Whether there is a path from `s` to `t` in `G`.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 0), (1, 3), (1, 2), (2, 3), (2, 0), (3, 0)])
+    >>> nx.is_tournament(G)
+    True
+    >>> nx.tournament.is_reachable(G, 1, 3)
+    True
+    >>> nx.tournament.is_reachable(G, 3, 2)
+    False
+
+    Notes
+    -----
+    Although this function is more theoretically efficient than the
+    generic shortest path functions, a speedup requires the use of
+    parallelism. Though it may in the future, the current implementation
+    does not use parallelism, thus you may not see much of a speedup.
+
+    This algorithm comes from [1].
+
+    References
+    ----------
+    .. [1] Tantau, Till.
+           "A note on the complexity of the reachability problem for
+           tournaments."
+           *Electronic Colloquium on Computational Complexity*. 2001.
+           <http://eccc.hpi-web.de/report/2001/092/>
+    """
+
+    def two_neighborhood(G, v):
+        """Returns the set of nodes at distance at most two from `v`.
+
+        `G` must be a graph and `v` a node in that graph.
+
+        The returned set includes the nodes at distance zero (that is,
+        the node `v` itself), the nodes at distance one (that is, the
+        out-neighbors of `v`), and the nodes at distance two.
+
+        """
+        return {
+            x for x in G if x == v or x in G[v] or any(is_path(G, [v, z, x]) for z in G)
+        }
+
+    def is_closed(G, nodes):
+        """Decides whether the given set of nodes is closed.
+
+        A set *S* of nodes is *closed* if for each node *u* in the graph
+        not in *S* and for each node *v* in *S*, there is an edge from
+        *u* to *v*.
+
+        """
+        return all(v in G[u] for u in set(G) - nodes for v in nodes)
+
+    neighborhoods = [two_neighborhood(G, v) for v in G]
+    return all(not (is_closed(G, S) and s in S and t not in S) for S in neighborhoods)
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable(name="tournament_is_strongly_connected")
+def is_strongly_connected(G):
+    """Decides whether the given tournament is strongly connected.
+
+    This function is more theoretically efficient than the
+    :func:`~networkx.algorithms.components.is_strongly_connected`
+    function.
+
+    The given graph **must** be a tournament, otherwise this function's
+    behavior is undefined.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A directed graph representing a tournament.
+
+    Returns
+    -------
+    bool
+        Whether the tournament is strongly connected.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(0, 1), (0, 2), (1, 2), (1, 3), (2, 3), (3, 0)])
+    >>> nx.is_tournament(G)
+    True
+    >>> nx.tournament.is_strongly_connected(G)
+    True
+    >>> G.remove_edge(3, 0)
+    >>> G.add_edge(0, 3)
+    >>> nx.is_tournament(G)
+    True
+    >>> nx.tournament.is_strongly_connected(G)
+    False
+
+    Notes
+    -----
+    Although this function is more theoretically efficient than the
+    generic strong connectivity function, a speedup requires the use of
+    parallelism. Though it may in the future, the current implementation
+    does not use parallelism, thus you may not see much of a speedup.
+
+    This algorithm comes from [1].
+
+    References
+    ----------
+    .. [1] Tantau, Till.
+           "A note on the complexity of the reachability problem for
+           tournaments."
+           *Electronic Colloquium on Computational Complexity*. 2001.
+           <http://eccc.hpi-web.de/report/2001/092/>
+
+    """
+    return all(is_reachable(G, u, v) for u in G for v in G)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/triads.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/triads.py
new file mode 100644
index 0000000000000000000000000000000000000000..2442d6fc8d868fa59949cf336850ba1c63d3d0fa
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/triads.py
@@ -0,0 +1,500 @@
+# See https://github.com/networkx/networkx/pull/1474
+# Copyright 2011 Reya Group <http://www.reyagroup.com>
+# Copyright 2011 Alex Levenson <alex@isnotinvain.com>
+# Copyright 2011 Diederik van Liere <diederik.vanliere@rotman.utoronto.ca>
+"""Functions for analyzing triads of a graph."""
+
+from collections import defaultdict
+from itertools import combinations, permutations
+
+import networkx as nx
+from networkx.utils import not_implemented_for, py_random_state
+
+__all__ = [
+    "triadic_census",
+    "is_triad",
+    "all_triads",
+    "triads_by_type",
+    "triad_type",
+]
+
+#: The integer codes representing each type of triad.
+#:
+#: Triads that are the same up to symmetry have the same code.
+TRICODES = (
+    1,
+    2,
+    2,
+    3,
+    2,
+    4,
+    6,
+    8,
+    2,
+    6,
+    5,
+    7,
+    3,
+    8,
+    7,
+    11,
+    2,
+    6,
+    4,
+    8,
+    5,
+    9,
+    9,
+    13,
+    6,
+    10,
+    9,
+    14,
+    7,
+    14,
+    12,
+    15,
+    2,
+    5,
+    6,
+    7,
+    6,
+    9,
+    10,
+    14,
+    4,
+    9,
+    9,
+    12,
+    8,
+    13,
+    14,
+    15,
+    3,
+    7,
+    8,
+    11,
+    7,
+    12,
+    14,
+    15,
+    8,
+    14,
+    13,
+    15,
+    11,
+    15,
+    15,
+    16,
+)
+
+#: The names of each type of triad. The order of the elements is
+#: important: it corresponds to the tricodes given in :data:`TRICODES`.
+TRIAD_NAMES = (
+    "003",
+    "012",
+    "102",
+    "021D",
+    "021U",
+    "021C",
+    "111D",
+    "111U",
+    "030T",
+    "030C",
+    "201",
+    "120D",
+    "120U",
+    "120C",
+    "210",
+    "300",
+)
+
+
+#: A dictionary mapping triad code to triad name.
+TRICODE_TO_NAME = {i: TRIAD_NAMES[code - 1] for i, code in enumerate(TRICODES)}
+
+
+def _tricode(G, v, u, w):
+    """Returns the integer code of the given triad.
+
+    This is some fancy magic that comes from Batagelj and Mrvar's paper. It
+    treats each edge joining a pair of `v`, `u`, and `w` as a bit in
+    the binary representation of an integer.
+
+    """
+    combos = ((v, u, 1), (u, v, 2), (v, w, 4), (w, v, 8), (u, w, 16), (w, u, 32))
+    return sum(x for u, v, x in combos if v in G[u])
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def triadic_census(G, nodelist=None):
+    """Determines the triadic census of a directed graph.
+
+    The triadic census is a count of how many of the 16 possible types of
+    triads are present in a directed graph. If a list of nodes is passed, then
+    only those triads are taken into account which have elements of nodelist in them.
+
+    Parameters
+    ----------
+    G : digraph
+       A NetworkX DiGraph
+    nodelist : list
+        List of nodes for which you want to calculate triadic census
+
+    Returns
+    -------
+    census : dict
+       Dictionary with triad type as keys and number of occurrences as values.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 1), (3, 4), (4, 1), (4, 2)])
+    >>> triadic_census = nx.triadic_census(G)
+    >>> for key, value in triadic_census.items():
+    ...     print(f"{key}: {value}")
+    003: 0
+    012: 0
+    102: 0
+    021D: 0
+    021U: 0
+    021C: 0
+    111D: 0
+    111U: 0
+    030T: 2
+    030C: 2
+    201: 0
+    120D: 0
+    120U: 0
+    120C: 0
+    210: 0
+    300: 0
+
+    Notes
+    -----
+    This algorithm has complexity $O(m)$ where $m$ is the number of edges in
+    the graph.
+
+    For undirected graphs, the triadic census can be computed by first converting
+    the graph into a directed graph using the ``G.to_directed()`` method.
+    After this conversion, only the triad types 003, 102, 201 and 300 will be
+    present in the undirected scenario.
+
+    Raises
+    ------
+    ValueError
+        If `nodelist` contains duplicate nodes or nodes not in `G`.
+        If you want to ignore this you can preprocess with `set(nodelist) & G.nodes`
+
+    See also
+    --------
+    triad_graph
+
+    References
+    ----------
+    .. [1] Vladimir Batagelj and Andrej Mrvar, A subquadratic triad census
+        algorithm for large sparse networks with small maximum degree,
+        University of Ljubljana,
+        http://vlado.fmf.uni-lj.si/pub/networks/doc/triads/triads.pdf
+
+    """
+    nodeset = set(G.nbunch_iter(nodelist))
+    if nodelist is not None and len(nodelist) != len(nodeset):
+        raise ValueError("nodelist includes duplicate nodes or nodes not in G")
+
+    N = len(G)
+    Nnot = N - len(nodeset)  # can signal special counting for subset of nodes
+
+    # create an ordering of nodes with nodeset nodes first
+    m = {n: i for i, n in enumerate(nodeset)}
+    if Nnot:
+        # add non-nodeset nodes later in the ordering
+        not_nodeset = G.nodes - nodeset
+        m.update((n, i + N) for i, n in enumerate(not_nodeset))
+
+    # build all_neighbor dicts for easy counting
+    # After Python 3.8 can leave off these keys(). Speedup also using G._pred
+    # nbrs = {n: G._pred[n].keys() | G._succ[n].keys() for n in G}
+    nbrs = {n: G.pred[n].keys() | G.succ[n].keys() for n in G}
+    dbl_nbrs = {n: G.pred[n].keys() & G.succ[n].keys() for n in G}
+
+    if Nnot:
+        sgl_nbrs = {n: G.pred[n].keys() ^ G.succ[n].keys() for n in not_nodeset}
+        # find number of edges not incident to nodes in nodeset
+        sgl = sum(1 for n in not_nodeset for nbr in sgl_nbrs[n] if nbr not in nodeset)
+        sgl_edges_outside = sgl // 2
+        dbl = sum(1 for n in not_nodeset for nbr in dbl_nbrs[n] if nbr not in nodeset)
+        dbl_edges_outside = dbl // 2
+
+    # Initialize the count for each triad to be zero.
+    census = dict.fromkeys(TRIAD_NAMES, 0)
+    # Main loop over nodes
+    for v in nodeset:
+        vnbrs = nbrs[v]
+        dbl_vnbrs = dbl_nbrs[v]
+        if Nnot:
+            # set up counts of edges attached to v.
+            sgl_unbrs_bdy = sgl_unbrs_out = dbl_unbrs_bdy = dbl_unbrs_out = 0
+        for u in vnbrs:
+            if m[u] <= m[v]:
+                continue
+            unbrs = nbrs[u]
+            neighbors = (vnbrs | unbrs) - {u, v}
+            # Count connected triads.
+            for w in neighbors:
+                if m[u] < m[w] or (m[v] < m[w] < m[u] and v not in nbrs[w]):
+                    code = _tricode(G, v, u, w)
+                    census[TRICODE_TO_NAME[code]] += 1
+
+            # Use a formula for dyadic triads with edge incident to v
+            if u in dbl_vnbrs:
+                census["102"] += N - len(neighbors) - 2
+            else:
+                census["012"] += N - len(neighbors) - 2
+
+            # Count edges attached to v. Subtract later to get triads with v isolated
+            # _out are (u,unbr) for unbrs outside boundary of nodeset
+            # _bdy are (u,unbr) for unbrs on boundary of nodeset (get double counted)
+            if Nnot and u not in nodeset:
+                sgl_unbrs = sgl_nbrs[u]
+                sgl_unbrs_bdy += len(sgl_unbrs & vnbrs - nodeset)
+                sgl_unbrs_out += len(sgl_unbrs - vnbrs - nodeset)
+                dbl_unbrs = dbl_nbrs[u]
+                dbl_unbrs_bdy += len(dbl_unbrs & vnbrs - nodeset)
+                dbl_unbrs_out += len(dbl_unbrs - vnbrs - nodeset)
+        # if nodeset == G.nodes, skip this b/c we will find the edge later.
+        if Nnot:
+            # Count edges outside nodeset not connected with v (v isolated triads)
+            census["012"] += sgl_edges_outside - (sgl_unbrs_out + sgl_unbrs_bdy // 2)
+            census["102"] += dbl_edges_outside - (dbl_unbrs_out + dbl_unbrs_bdy // 2)
+
+    # calculate null triads: "003"
+    # null triads = total number of possible triads - all found triads
+    total_triangles = (N * (N - 1) * (N - 2)) // 6
+    triangles_without_nodeset = (Nnot * (Nnot - 1) * (Nnot - 2)) // 6
+    total_census = total_triangles - triangles_without_nodeset
+    census["003"] = total_census - sum(census.values())
+
+    return census
+
+
+@nx._dispatchable
+def is_triad(G):
+    """Returns True if the graph G is a triad, else False.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX Graph
+
+    Returns
+    -------
+    istriad : boolean
+       Whether G is a valid triad
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
+    >>> nx.is_triad(G)
+    True
+    >>> G.add_edge(0, 1)
+    >>> nx.is_triad(G)
+    False
+    """
+    if isinstance(G, nx.Graph):
+        if G.order() == 3 and nx.is_directed(G):
+            if not any((n, n) in G.edges() for n in G.nodes()):
+                return True
+    return False
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable(returns_graph=True)
+def all_triads(G):
+    """A generator of all possible triads in G.
+
+    Parameters
+    ----------
+    G : digraph
+       A NetworkX DiGraph
+
+    Returns
+    -------
+    all_triads : generator of DiGraphs
+       Generator of triads (order-3 DiGraphs)
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 1), (3, 4), (4, 1), (4, 2)])
+    >>> for triad in nx.all_triads(G):
+    ...     print(triad.edges)
+    [(1, 2), (2, 3), (3, 1)]
+    [(1, 2), (4, 1), (4, 2)]
+    [(3, 1), (3, 4), (4, 1)]
+    [(2, 3), (3, 4), (4, 2)]
+
+    """
+    triplets = combinations(G.nodes(), 3)
+    for triplet in triplets:
+        yield G.subgraph(triplet).copy()
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def triads_by_type(G):
+    """Returns a list of all triads for each triad type in a directed graph.
+    There are exactly 16 different types of triads possible. Suppose 1, 2, 3 are three
+    nodes, they will be classified as a particular triad type if their connections
+    are as follows:
+
+    - 003: 1, 2, 3
+    - 012: 1 -> 2, 3
+    - 102: 1 <-> 2, 3
+    - 021D: 1 <- 2 -> 3
+    - 021U: 1 -> 2 <- 3
+    - 021C: 1 -> 2 -> 3
+    - 111D: 1 <-> 2 <- 3
+    - 111U: 1 <-> 2 -> 3
+    - 030T: 1 -> 2 -> 3, 1 -> 3
+    - 030C: 1 <- 2 <- 3, 1 -> 3
+    - 201: 1 <-> 2 <-> 3
+    - 120D: 1 <- 2 -> 3, 1 <-> 3
+    - 120U: 1 -> 2 <- 3, 1 <-> 3
+    - 120C: 1 -> 2 -> 3, 1 <-> 3
+    - 210: 1 -> 2 <-> 3, 1 <-> 3
+    - 300: 1 <-> 2 <-> 3, 1 <-> 3
+
+    Refer to the :doc:`example gallery </auto_examples/graph/plot_triad_types>`
+    for visual examples of the triad types.
+
+    Parameters
+    ----------
+    G : digraph
+       A NetworkX DiGraph
+
+    Returns
+    -------
+    tri_by_type : dict
+       Dictionary with triad types as keys and lists of triads as values.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 2), (1, 3), (2, 3), (3, 1), (5, 6), (5, 4), (6, 7)])
+    >>> dict = nx.triads_by_type(G)
+    >>> dict["120C"][0].edges()
+    OutEdgeView([(1, 2), (1, 3), (2, 3), (3, 1)])
+    >>> dict["012"][0].edges()
+    OutEdgeView([(1, 2)])
+
+    References
+    ----------
+    .. [1] Snijders, T. (2012). "Transitivity and triads." University of
+        Oxford.
+        https://web.archive.org/web/20170830032057/http://www.stats.ox.ac.uk/~snijders/Trans_Triads_ha.pdf
+    """
+    # num_triads = o * (o - 1) * (o - 2) // 6
+    # if num_triads > TRIAD_LIMIT: print(WARNING)
+    all_tri = all_triads(G)
+    tri_by_type = defaultdict(list)
+    for triad in all_tri:
+        name = triad_type(triad)
+        tri_by_type[name].append(triad)
+    return tri_by_type
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable
+def triad_type(G):
+    """Returns the sociological triad type for a triad.
+
+    Parameters
+    ----------
+    G : digraph
+       A NetworkX DiGraph with 3 nodes
+
+    Returns
+    -------
+    triad_type : str
+       A string identifying the triad type
+
+    Examples
+    --------
+    >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
+    >>> nx.triad_type(G)
+    '030C'
+    >>> G.add_edge(1, 3)
+    >>> nx.triad_type(G)
+    '120C'
+
+    Notes
+    -----
+    There can be 6 unique edges in a triad (order-3 DiGraph) (so 2^^6=64 unique
+    triads given 3 nodes). These 64 triads each display exactly 1 of 16
+    topologies of triads (topologies can be permuted). These topologies are
+    identified by the following notation:
+
+    {m}{a}{n}{type} (for example: 111D, 210, 102)
+
+    Here:
+
+    {m}     = number of mutual ties (takes 0, 1, 2, 3); a mutual tie is (0,1)
+              AND (1,0)
+    {a}     = number of asymmetric ties (takes 0, 1, 2, 3); an asymmetric tie
+              is (0,1) BUT NOT (1,0) or vice versa
+    {n}     = number of null ties (takes 0, 1, 2, 3); a null tie is NEITHER
+              (0,1) NOR (1,0)
+    {type}  = a letter (takes U, D, C, T) corresponding to up, down, cyclical
+              and transitive. This is only used for topologies that can have
+              more than one form (eg: 021D and 021U).
+
+    References
+    ----------
+    .. [1] Snijders, T. (2012). "Transitivity and triads." University of
+        Oxford.
+        https://web.archive.org/web/20170830032057/http://www.stats.ox.ac.uk/~snijders/Trans_Triads_ha.pdf
+    """
+    if not is_triad(G):
+        raise nx.NetworkXAlgorithmError("G is not a triad (order-3 DiGraph)")
+    num_edges = len(G.edges())
+    if num_edges == 0:
+        return "003"
+    elif num_edges == 1:
+        return "012"
+    elif num_edges == 2:
+        e1, e2 = G.edges()
+        if set(e1) == set(e2):
+            return "102"
+        elif e1[0] == e2[0]:
+            return "021D"
+        elif e1[1] == e2[1]:
+            return "021U"
+        elif e1[1] == e2[0] or e2[1] == e1[0]:
+            return "021C"
+    elif num_edges == 3:
+        for e1, e2, e3 in permutations(G.edges(), 3):
+            if set(e1) == set(e2):
+                if e3[0] in e1:
+                    return "111U"
+                # e3[1] in e1:
+                return "111D"
+            elif set(e1).symmetric_difference(set(e2)) == set(e3):
+                if {e1[0], e2[0], e3[0]} == {e1[0], e2[0], e3[0]} == set(G.nodes()):
+                    return "030C"
+                # e3 == (e1[0], e2[1]) and e2 == (e1[1], e3[1]):
+                return "030T"
+    elif num_edges == 4:
+        for e1, e2, e3, e4 in permutations(G.edges(), 4):
+            if set(e1) == set(e2):
+                # identify pair of symmetric edges (which necessarily exists)
+                if set(e3) == set(e4):
+                    return "201"
+                if {e3[0]} == {e4[0]} == set(e3).intersection(set(e4)):
+                    return "120D"
+                if {e3[1]} == {e4[1]} == set(e3).intersection(set(e4)):
+                    return "120U"
+                if e3[1] == e4[0]:
+                    return "120C"
+    elif num_edges == 5:
+        return "210"
+    elif num_edges == 6:
+        return "300"
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/vitality.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/vitality.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf4b016e78dc7429810bb48f948f40212e542eca
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/vitality.py
@@ -0,0 +1,76 @@
+"""
+Vitality measures.
+"""
+
+from functools import partial
+
+import networkx as nx
+
+__all__ = ["closeness_vitality"]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def closeness_vitality(G, node=None, weight=None, wiener_index=None):
+    """Returns the closeness vitality for nodes in the graph.
+
+    The *closeness vitality* of a node, defined in Section 3.6.2 of [1],
+    is the change in the sum of distances between all node pairs when
+    excluding that node.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A strongly-connected graph.
+
+    weight : string
+        The name of the edge attribute used as weight. This is passed
+        directly to the :func:`~networkx.wiener_index` function.
+
+    node : object
+        If specified, only the closeness vitality for this node will be
+        returned. Otherwise, a dictionary mapping each node to its
+        closeness vitality will be returned.
+
+    Other parameters
+    ----------------
+    wiener_index : number
+        If you have already computed the Wiener index of the graph
+        `G`, you can provide that value here. Otherwise, it will be
+        computed for you.
+
+    Returns
+    -------
+    dictionary or float
+        If `node` is None, this function returns a dictionary
+        with nodes as keys and closeness vitality as the
+        value. Otherwise, it returns only the closeness vitality for the
+        specified `node`.
+
+        The closeness vitality of a node may be negative infinity if
+        removing that node would disconnect the graph.
+
+    Examples
+    --------
+    >>> G = nx.cycle_graph(3)
+    >>> nx.closeness_vitality(G)
+    {0: 2.0, 1: 2.0, 2: 2.0}
+
+    See Also
+    --------
+    closeness_centrality
+
+    References
+    ----------
+    .. [1] Ulrik Brandes, Thomas Erlebach (eds.).
+           *Network Analysis: Methodological Foundations*.
+           Springer, 2005.
+           <http://books.google.com/books?id=TTNhSm7HYrIC>
+
+    """
+    if wiener_index is None:
+        wiener_index = nx.wiener_index(G, weight=weight)
+    if node is not None:
+        after = nx.wiener_index(G.subgraph(set(G) - {node}), weight=weight)
+        return wiener_index - after
+    vitality = partial(closeness_vitality, G, weight=weight, wiener_index=wiener_index)
+    return {v: vitality(node=v) for v in G}
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/voronoi.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/voronoi.py
new file mode 100644
index 0000000000000000000000000000000000000000..609a68deff89620e0e022020c33863107decced4
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/voronoi.py
@@ -0,0 +1,86 @@
+"""Functions for computing the Voronoi cells of a graph."""
+
+import networkx as nx
+from networkx.utils import groups
+
+__all__ = ["voronoi_cells"]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def voronoi_cells(G, center_nodes, weight="weight"):
+    """Returns the Voronoi cells centered at `center_nodes` with respect
+    to the shortest-path distance metric.
+
+    If $C$ is a set of nodes in the graph and $c$ is an element of $C$,
+    the *Voronoi cell* centered at a node $c$ is the set of all nodes
+    $v$ that are closer to $c$ than to any other center node in $C$ with
+    respect to the shortest-path distance metric. [1]_
+
+    For directed graphs, this will compute the "outward" Voronoi cells,
+    as defined in [1]_, in which distance is measured from the center
+    nodes to the target node. For the "inward" Voronoi cells, use the
+    :meth:`DiGraph.reverse` method to reverse the orientation of the
+    edges before invoking this function on the directed graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    center_nodes : set
+        A nonempty set of nodes in the graph `G` that represent the
+        center of the Voronoi cells.
+
+    weight : string or function
+        The edge attribute (or an arbitrary function) representing the
+        weight of an edge. This keyword argument is as described in the
+        documentation for :func:`~networkx.multi_source_dijkstra_path`,
+        for example.
+
+    Returns
+    -------
+    dictionary
+        A mapping from center node to set of all nodes in the graph
+        closer to that center node than to any other center node. The
+        keys of the dictionary are the element of `center_nodes`, and
+        the values of the dictionary form a partition of the nodes of
+        `G`.
+
+    Examples
+    --------
+    To get only the partition of the graph induced by the Voronoi cells,
+    take the collection of all values in the returned dictionary::
+
+        >>> G = nx.path_graph(6)
+        >>> center_nodes = {0, 3}
+        >>> cells = nx.voronoi_cells(G, center_nodes)
+        >>> partition = set(map(frozenset, cells.values()))
+        >>> sorted(map(sorted, partition))
+        [[0, 1], [2, 3, 4, 5]]
+
+    Raises
+    ------
+    ValueError
+        If `center_nodes` is empty.
+
+    References
+    ----------
+    .. [1] Erwig, Martin. (2000),"The graph Voronoi diagram with applications."
+        *Networks*, 36: 156--163.
+        https://doi.org/10.1002/1097-0037(200010)36:3<156::AID-NET2>3.0.CO;2-L
+
+    """
+    # Determine the shortest paths from any one of the center nodes to
+    # every node in the graph.
+    #
+    # This raises `ValueError` if `center_nodes` is an empty set.
+    paths = nx.multi_source_dijkstra_path(G, center_nodes, weight=weight)
+    # Determine the center node from which the shortest path originates.
+    nearest = {v: p[0] for v, p in paths.items()}
+    # Get the mapping from center node to all nodes closer to it than to
+    # any other center node.
+    cells = groups(nearest)
+    # We collect all unreachable nodes under a special key, if there are any.
+    unreachable = set(G) - set(nearest)
+    if unreachable:
+        cells["unreachable"] = unreachable
+    return cells
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/walks.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/walks.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ef9dac121805ef2c4e4538a97f275a05dff92cb
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/walks.py
@@ -0,0 +1,79 @@
+"""Function for computing walks in a graph."""
+
+import networkx as nx
+
+__all__ = ["number_of_walks"]
+
+
+@nx._dispatchable
+def number_of_walks(G, walk_length):
+    """Returns the number of walks connecting each pair of nodes in `G`
+
+    A *walk* is a sequence of nodes in which each adjacent pair of nodes
+    in the sequence is adjacent in the graph. A walk can repeat the same
+    edge and go in the opposite direction just as people can walk on a
+    set of paths, but standing still is not counted as part of the walk.
+
+    This function only counts the walks with `walk_length` edges. Note that
+    the number of nodes in the walk sequence is one more than `walk_length`.
+    The number of walks can grow very quickly on a larger graph
+    and with a larger walk length.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    walk_length : int
+        A nonnegative integer representing the length of a walk.
+
+    Returns
+    -------
+    dict
+        A dictionary of dictionaries in which outer keys are source
+        nodes, inner keys are target nodes, and inner values are the
+        number of walks of length `walk_length` connecting those nodes.
+
+    Raises
+    ------
+    ValueError
+        If `walk_length` is negative
+
+    Examples
+    --------
+
+    >>> G = nx.Graph([(0, 1), (1, 2)])
+    >>> walks = nx.number_of_walks(G, 2)
+    >>> walks
+    {0: {0: 1, 1: 0, 2: 1}, 1: {0: 0, 1: 2, 2: 0}, 2: {0: 1, 1: 0, 2: 1}}
+    >>> total_walks = sum(sum(tgts.values()) for _, tgts in walks.items())
+
+    You can also get the number of walks from a specific source node using the
+    returned dictionary. For example, number of walks of length 1 from node 0
+    can be found as follows:
+
+    >>> walks = nx.number_of_walks(G, 1)
+    >>> walks[0]
+    {0: 0, 1: 1, 2: 0}
+    >>> sum(walks[0].values())  # walks from 0 of length 1
+    1
+
+    Similarly, a target node can also be specified:
+
+    >>> walks[0][1]
+    1
+
+    """
+    import numpy as np
+
+    if walk_length < 0:
+        raise ValueError(f"`walk_length` cannot be negative: {walk_length}")
+
+    A = nx.adjacency_matrix(G, weight=None)
+    # TODO: Use matrix_power from scipy.sparse when available
+    # power = sp.sparse.linalg.matrix_power(A, walk_length)
+    power = np.linalg.matrix_power(A.toarray(), walk_length)
+    result = {
+        u: {v: power.item(u_idx, v_idx) for v_idx, v in enumerate(G)}
+        for u_idx, u in enumerate(G)
+    }
+    return result
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/wiener.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/wiener.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac3abe4ac12b267c1d5403b2653e6ed0b7e489c3
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/algorithms/wiener.py
@@ -0,0 +1,226 @@
+"""Functions related to the Wiener Index of a graph.
+
+The Wiener Index is a topological measure of a graph
+related to the distance between nodes and their degree.
+The Schultz Index and Gutman Index are similar measures.
+They are used categorize molecules via the network of
+atoms connected by chemical bonds. The indices are
+correlated with functional aspects of the molecules.
+
+References
+----------
+.. [1] `Wikipedia: Wiener Index <https://en.wikipedia.org/wiki/Wiener_index>`_
+.. [2] M.V. Diudeaa and I. Gutman, Wiener-Type Topological Indices,
+       Croatica Chemica Acta, 71 (1998), 21-51.
+       https://hrcak.srce.hr/132323
+"""
+
+import itertools as it
+
+import networkx as nx
+
+__all__ = ["wiener_index", "schultz_index", "gutman_index"]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def wiener_index(G, weight=None):
+    """Returns the Wiener index of the given graph.
+
+    The *Wiener index* of a graph is the sum of the shortest-path
+    (weighted) distances between each pair of reachable nodes.
+    For pairs of nodes in undirected graphs, only one orientation
+    of the pair is counted.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight : string or None, optional (default: None)
+        If None, every edge has weight 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        The edge weights are used to computing shortest-path distances.
+
+    Returns
+    -------
+    number
+        The Wiener index of the graph `G`.
+
+    Raises
+    ------
+    NetworkXError
+        If the graph `G` is not connected.
+
+    Notes
+    -----
+    If a pair of nodes is not reachable, the distance is assumed to be
+    infinity. This means that for graphs that are not
+    strongly-connected, this function returns ``inf``.
+
+    The Wiener index is not usually defined for directed graphs, however
+    this function uses the natural generalization of the Wiener index to
+    directed graphs.
+
+    Examples
+    --------
+    The Wiener index of the (unweighted) complete graph on *n* nodes
+    equals the number of pairs of the *n* nodes, since each pair of
+    nodes is at distance one::
+
+        >>> n = 10
+        >>> G = nx.complete_graph(n)
+        >>> nx.wiener_index(G) == n * (n - 1) / 2
+        True
+
+    Graphs that are not strongly-connected have infinite Wiener index::
+
+        >>> G = nx.empty_graph(2)
+        >>> nx.wiener_index(G)
+        inf
+
+    References
+    ----------
+    .. [1] `Wikipedia: Wiener Index <https://en.wikipedia.org/wiki/Wiener_index>`_
+    """
+    connected = nx.is_strongly_connected(G) if G.is_directed() else nx.is_connected(G)
+    if not connected:
+        return float("inf")
+
+    spl = nx.shortest_path_length(G, weight=weight)
+    total = sum(it.chain.from_iterable(nbrs.values() for node, nbrs in spl))
+    # Need to account for double counting pairs of nodes in undirected graphs.
+    return total if G.is_directed() else total / 2
+
+
+@nx.utils.not_implemented_for("directed")
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable(edge_attrs="weight")
+def schultz_index(G, weight=None):
+    r"""Returns the Schultz Index (of the first kind) of `G`
+
+    The *Schultz Index* [3]_ of a graph is the sum over all node pairs of
+    distances times the sum of degrees. Consider an undirected graph `G`.
+    For each node pair ``(u, v)`` compute ``dist(u, v) * (deg(u) + deg(v)``
+    where ``dist`` is the shortest path length between two nodes and ``deg``
+    is the degree of a node.
+
+    The Schultz Index is the sum of these quantities over all (unordered)
+    pairs of nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The undirected graph of interest.
+    weight : string or None, optional (default: None)
+        If None, every edge has weight 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        The edge weights are used to computing shortest-path distances.
+
+    Returns
+    -------
+    number
+        The first kind of Schultz Index of the graph `G`.
+
+    Examples
+    --------
+    The Schultz Index of the (unweighted) complete graph on *n* nodes
+    equals the number of pairs of the *n* nodes times ``2 * (n - 1)``,
+    since each pair of nodes is at distance one and the sum of degree
+    of two nodes is ``2 * (n - 1)``.
+
+    >>> n = 10
+    >>> G = nx.complete_graph(n)
+    >>> nx.schultz_index(G) == (n * (n - 1) / 2) * (2 * (n - 1))
+    True
+
+    Graph that is disconnected
+
+    >>> nx.schultz_index(nx.empty_graph(2))
+    inf
+
+    References
+    ----------
+    .. [1] I. Gutman, Selected properties of the Schultz molecular topological index,
+           J. Chem. Inf. Comput. Sci. 34 (1994), 1087–1089.
+           https://doi.org/10.1021/ci00021a009
+    .. [2] M.V. Diudeaa and I. Gutman, Wiener-Type Topological Indices,
+           Croatica Chemica Acta, 71 (1998), 21-51.
+           https://hrcak.srce.hr/132323
+    .. [3] H. P. Schultz, Topological organic chemistry. 1.
+           Graph theory and topological indices of alkanes,i
+           J. Chem. Inf. Comput. Sci. 29 (1989), 239–257.
+
+    """
+    if not nx.is_connected(G):
+        return float("inf")
+
+    spl = nx.shortest_path_length(G, weight=weight)
+    d = dict(G.degree, weight=weight)
+    return sum(dist * (d[u] + d[v]) for u, info in spl for v, dist in info.items()) / 2
+
+
+@nx.utils.not_implemented_for("directed")
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable(edge_attrs="weight")
+def gutman_index(G, weight=None):
+    r"""Returns the Gutman Index for the graph `G`.
+
+    The *Gutman Index* measures the topology of networks, especially for molecule
+    networks of atoms connected by bonds [1]_. It is also called the Schultz Index
+    of the second kind [2]_.
+
+    Consider an undirected graph `G` with node set ``V``.
+    The Gutman Index of a graph is the sum over all (unordered) pairs of nodes
+    of nodes ``(u, v)``, with distance ``dist(u, v)`` and degrees ``deg(u)``
+    and ``deg(v)``, of ``dist(u, v) * deg(u) * deg(v)``
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    weight : string or None, optional (default: None)
+        If None, every edge has weight 1.
+        If a string, use this edge attribute as the edge weight.
+        Any edge attribute not present defaults to 1.
+        The edge weights are used to computing shortest-path distances.
+
+    Returns
+    -------
+    number
+        The Gutman Index of the graph `G`.
+
+    Examples
+    --------
+    The Gutman Index of the (unweighted) complete graph on *n* nodes
+    equals the number of pairs of the *n* nodes times ``(n - 1) * (n - 1)``,
+    since each pair of nodes is at distance one and the product of degree of two
+    vertices is ``(n - 1) * (n - 1)``.
+
+    >>> n = 10
+    >>> G = nx.complete_graph(n)
+    >>> nx.gutman_index(G) == (n * (n - 1) / 2) * ((n - 1) * (n - 1))
+    True
+
+    Graphs that are disconnected
+
+    >>> G = nx.empty_graph(2)
+    >>> nx.gutman_index(G)
+    inf
+
+    References
+    ----------
+    .. [1] M.V. Diudeaa and I. Gutman, Wiener-Type Topological Indices,
+           Croatica Chemica Acta, 71 (1998), 21-51.
+           https://hrcak.srce.hr/132323
+    .. [2] I. Gutman, Selected properties of the Schultz molecular topological index,
+           J. Chem. Inf. Comput. Sci. 34 (1994), 1087–1089.
+           https://doi.org/10.1021/ci00021a009
+
+    """
+    if not nx.is_connected(G):
+        return float("inf")
+
+    spl = nx.shortest_path_length(G, weight=weight)
+    d = dict(G.degree, weight=weight)
+    return sum(dist * d[u] * d[v] for u, vinfo in spl for v, dist in vinfo.items()) / 2
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/__init__.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..721fa8b4767233bc2b624f6b2ce4d10533a4d66c
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/__init__.py
@@ -0,0 +1,13 @@
+from .graph import Graph
+from .digraph import DiGraph
+from .multigraph import MultiGraph
+from .multidigraph import MultiDiGraph
+
+from .function import *
+from .graphviews import subgraph_view, reverse_view
+
+from networkx.classes import filters
+
+from networkx.classes import coreviews
+from networkx.classes import graphviews
+from networkx.classes import reportviews
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/coreviews.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/coreviews.py
new file mode 100644
index 0000000000000000000000000000000000000000..4769ffa71ab823c154e6f7b990f0cb07299090a6
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/coreviews.py
@@ -0,0 +1,435 @@
+"""Views of core data structures such as nested Mappings (e.g. dict-of-dicts).
+These ``Views`` often restrict element access, with either the entire view or
+layers of nested mappings being read-only.
+"""
+
+from collections.abc import Mapping
+
+__all__ = [
+    "AtlasView",
+    "AdjacencyView",
+    "MultiAdjacencyView",
+    "UnionAtlas",
+    "UnionAdjacency",
+    "UnionMultiInner",
+    "UnionMultiAdjacency",
+    "FilterAtlas",
+    "FilterAdjacency",
+    "FilterMultiInner",
+    "FilterMultiAdjacency",
+]
+
+
+class AtlasView(Mapping):
+    """An AtlasView is a Read-only Mapping of Mappings.
+
+    It is a View into a dict-of-dict data structure.
+    The inner level of dict is read-write. But the
+    outer level is read-only.
+
+    See Also
+    ========
+    AdjacencyView: View into dict-of-dict-of-dict
+    MultiAdjacencyView: View into dict-of-dict-of-dict-of-dict
+    """
+
+    __slots__ = ("_atlas",)
+
+    def __getstate__(self):
+        return {"_atlas": self._atlas}
+
+    def __setstate__(self, state):
+        self._atlas = state["_atlas"]
+
+    def __init__(self, d):
+        self._atlas = d
+
+    def __len__(self):
+        return len(self._atlas)
+
+    def __iter__(self):
+        return iter(self._atlas)
+
+    def __getitem__(self, key):
+        return self._atlas[key]
+
+    def copy(self):
+        return {n: self[n].copy() for n in self._atlas}
+
+    def __str__(self):
+        return str(self._atlas)  # {nbr: self[nbr] for nbr in self})
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self._atlas!r})"
+
+
+class AdjacencyView(AtlasView):
+    """An AdjacencyView is a Read-only Map of Maps of Maps.
+
+    It is a View into a dict-of-dict-of-dict data structure.
+    The inner level of dict is read-write. But the
+    outer levels are read-only.
+
+    See Also
+    ========
+    AtlasView: View into dict-of-dict
+    MultiAdjacencyView: View into dict-of-dict-of-dict-of-dict
+    """
+
+    __slots__ = ()  # Still uses AtlasView slots names _atlas
+
+    def __getitem__(self, name):
+        return AtlasView(self._atlas[name])
+
+    def copy(self):
+        return {n: self[n].copy() for n in self._atlas}
+
+
+class MultiAdjacencyView(AdjacencyView):
+    """An MultiAdjacencyView is a Read-only Map of Maps of Maps of Maps.
+
+    It is a View into a dict-of-dict-of-dict-of-dict data structure.
+    The inner level of dict is read-write. But the
+    outer levels are read-only.
+
+    See Also
+    ========
+    AtlasView: View into dict-of-dict
+    AdjacencyView: View into dict-of-dict-of-dict
+    """
+
+    __slots__ = ()  # Still uses AtlasView slots names _atlas
+
+    def __getitem__(self, name):
+        return AdjacencyView(self._atlas[name])
+
+    def copy(self):
+        return {n: self[n].copy() for n in self._atlas}
+
+
+class UnionAtlas(Mapping):
+    """A read-only union of two atlases (dict-of-dict).
+
+    The two dict-of-dicts represent the inner dict of
+    an Adjacency:  `G.succ[node]` and `G.pred[node]`.
+    The inner level of dict of both hold attribute key:value
+    pairs and is read-write. But the outer level is read-only.
+
+    See Also
+    ========
+    UnionAdjacency: View into dict-of-dict-of-dict
+    UnionMultiAdjacency: View into dict-of-dict-of-dict-of-dict
+    """
+
+    __slots__ = ("_succ", "_pred")
+
+    def __getstate__(self):
+        return {"_succ": self._succ, "_pred": self._pred}
+
+    def __setstate__(self, state):
+        self._succ = state["_succ"]
+        self._pred = state["_pred"]
+
+    def __init__(self, succ, pred):
+        self._succ = succ
+        self._pred = pred
+
+    def __len__(self):
+        return len(self._succ.keys() | self._pred.keys())
+
+    def __iter__(self):
+        return iter(set(self._succ.keys()) | set(self._pred.keys()))
+
+    def __getitem__(self, key):
+        try:
+            return self._succ[key]
+        except KeyError:
+            return self._pred[key]
+
+    def copy(self):
+        result = {nbr: dd.copy() for nbr, dd in self._succ.items()}
+        for nbr, dd in self._pred.items():
+            if nbr in result:
+                result[nbr].update(dd)
+            else:
+                result[nbr] = dd.copy()
+        return result
+
+    def __str__(self):
+        return str({nbr: self[nbr] for nbr in self})
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self._succ!r}, {self._pred!r})"
+
+
+class UnionAdjacency(Mapping):
+    """A read-only union of dict Adjacencies as a Map of Maps of Maps.
+
+    The two input dict-of-dict-of-dicts represent the union of
+    `G.succ` and `G.pred`. Return values are UnionAtlas
+    The inner level of dict is read-write. But the
+    middle and outer levels are read-only.
+
+    succ : a dict-of-dict-of-dict {node: nbrdict}
+    pred : a dict-of-dict-of-dict {node: nbrdict}
+    The keys for the two dicts should be the same
+
+    See Also
+    ========
+    UnionAtlas: View into dict-of-dict
+    UnionMultiAdjacency: View into dict-of-dict-of-dict-of-dict
+    """
+
+    __slots__ = ("_succ", "_pred")
+
+    def __getstate__(self):
+        return {"_succ": self._succ, "_pred": self._pred}
+
+    def __setstate__(self, state):
+        self._succ = state["_succ"]
+        self._pred = state["_pred"]
+
+    def __init__(self, succ, pred):
+        # keys must be the same for two input dicts
+        assert len(set(succ.keys()) ^ set(pred.keys())) == 0
+        self._succ = succ
+        self._pred = pred
+
+    def __len__(self):
+        return len(self._succ)  # length of each dict should be the same
+
+    def __iter__(self):
+        return iter(self._succ)
+
+    def __getitem__(self, nbr):
+        return UnionAtlas(self._succ[nbr], self._pred[nbr])
+
+    def copy(self):
+        return {n: self[n].copy() for n in self._succ}
+
+    def __str__(self):
+        return str({nbr: self[nbr] for nbr in self})
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self._succ!r}, {self._pred!r})"
+
+
+class UnionMultiInner(UnionAtlas):
+    """A read-only union of two inner dicts of MultiAdjacencies.
+
+    The two input dict-of-dict-of-dicts represent the union of
+    `G.succ[node]` and `G.pred[node]` for MultiDiGraphs.
+    Return values are UnionAtlas.
+    The inner level of dict is read-write. But the outer levels are read-only.
+
+    See Also
+    ========
+    UnionAtlas: View into dict-of-dict
+    UnionAdjacency:  View into dict-of-dict-of-dict
+    UnionMultiAdjacency:  View into dict-of-dict-of-dict-of-dict
+    """
+
+    __slots__ = ()  # Still uses UnionAtlas slots names _succ, _pred
+
+    def __getitem__(self, node):
+        in_succ = node in self._succ
+        in_pred = node in self._pred
+        if in_succ:
+            if in_pred:
+                return UnionAtlas(self._succ[node], self._pred[node])
+            return UnionAtlas(self._succ[node], {})
+        return UnionAtlas({}, self._pred[node])
+
+    def copy(self):
+        nodes = set(self._succ.keys()) | set(self._pred.keys())
+        return {n: self[n].copy() for n in nodes}
+
+
+class UnionMultiAdjacency(UnionAdjacency):
+    """A read-only union of two dict MultiAdjacencies.
+
+    The two input dict-of-dict-of-dict-of-dicts represent the union of
+    `G.succ` and `G.pred` for MultiDiGraphs. Return values are UnionAdjacency.
+    The inner level of dict is read-write. But the outer levels are read-only.
+
+    See Also
+    ========
+    UnionAtlas:  View into dict-of-dict
+    UnionMultiInner:  View into dict-of-dict-of-dict
+    """
+
+    __slots__ = ()  # Still uses UnionAdjacency slots names _succ, _pred
+
+    def __getitem__(self, node):
+        return UnionMultiInner(self._succ[node], self._pred[node])
+
+
+class FilterAtlas(Mapping):  # nodedict, nbrdict, keydict
+    """A read-only Mapping of Mappings with filtering criteria for nodes.
+
+    It is a view into a dict-of-dict data structure, and it selects only
+    nodes that meet the criteria defined by ``NODE_OK``.
+
+    See Also
+    ========
+    FilterAdjacency
+    FilterMultiInner
+    FilterMultiAdjacency
+    """
+
+    def __init__(self, d, NODE_OK):
+        self._atlas = d
+        self.NODE_OK = NODE_OK
+
+    def __len__(self):
+        # check whether NODE_OK stores the number of nodes as `length`
+        # or the nodes themselves as a set `nodes`. If not, count the nodes.
+        if hasattr(self.NODE_OK, "length"):
+            return self.NODE_OK.length
+        if hasattr(self.NODE_OK, "nodes"):
+            return len(self.NODE_OK.nodes & self._atlas.keys())
+        return sum(1 for n in self._atlas if self.NODE_OK(n))
+
+    def __iter__(self):
+        try:  # check that NODE_OK has attr 'nodes'
+            node_ok_shorter = 2 * len(self.NODE_OK.nodes) < len(self._atlas)
+        except AttributeError:
+            node_ok_shorter = False
+        if node_ok_shorter:
+            return (n for n in self.NODE_OK.nodes if n in self._atlas)
+        return (n for n in self._atlas if self.NODE_OK(n))
+
+    def __getitem__(self, key):
+        if key in self._atlas and self.NODE_OK(key):
+            return self._atlas[key]
+        raise KeyError(f"Key {key} not found")
+
+    def __str__(self):
+        return str({nbr: self[nbr] for nbr in self})
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self._atlas!r}, {self.NODE_OK!r})"
+
+
+class FilterAdjacency(Mapping):  # edgedict
+    """A read-only Mapping of Mappings with filtering criteria for nodes and edges.
+
+    It is a view into a dict-of-dict-of-dict data structure, and it selects nodes
+    and edges that satisfy specific criteria defined by ``NODE_OK`` and ``EDGE_OK``,
+    respectively.
+
+    See Also
+    ========
+    FilterAtlas
+    FilterMultiInner
+    FilterMultiAdjacency
+    """
+
+    def __init__(self, d, NODE_OK, EDGE_OK):
+        self._atlas = d
+        self.NODE_OK = NODE_OK
+        self.EDGE_OK = EDGE_OK
+
+    def __len__(self):
+        # check whether NODE_OK stores the number of nodes as `length`
+        # or the nodes themselves as a set `nodes`. If not, count the nodes.
+        if hasattr(self.NODE_OK, "length"):
+            return self.NODE_OK.length
+        if hasattr(self.NODE_OK, "nodes"):
+            return len(self.NODE_OK.nodes & self._atlas.keys())
+        return sum(1 for n in self._atlas if self.NODE_OK(n))
+
+    def __iter__(self):
+        try:  # check that NODE_OK has attr 'nodes'
+            node_ok_shorter = 2 * len(self.NODE_OK.nodes) < len(self._atlas)
+        except AttributeError:
+            node_ok_shorter = False
+        if node_ok_shorter:
+            return (n for n in self.NODE_OK.nodes if n in self._atlas)
+        return (n for n in self._atlas if self.NODE_OK(n))
+
+    def __getitem__(self, node):
+        if node in self._atlas and self.NODE_OK(node):
+
+            def new_node_ok(nbr):
+                return self.NODE_OK(nbr) and self.EDGE_OK(node, nbr)
+
+            return FilterAtlas(self._atlas[node], new_node_ok)
+        raise KeyError(f"Key {node} not found")
+
+    def __str__(self):
+        return str({nbr: self[nbr] for nbr in self})
+
+    def __repr__(self):
+        name = self.__class__.__name__
+        return f"{name}({self._atlas!r}, {self.NODE_OK!r}, {self.EDGE_OK!r})"
+
+
+class FilterMultiInner(FilterAdjacency):  # muliedge_seconddict
+    """A read-only Mapping of Mappings with filtering criteria for nodes and edges.
+
+    It is a view into a dict-of-dict-of-dict-of-dict data structure, and it selects nodes
+    and edges that meet specific criteria defined by ``NODE_OK`` and ``EDGE_OK``.
+
+    See Also
+    ========
+    FilterAtlas
+    FilterAdjacency
+    FilterMultiAdjacency
+    """
+
+    def __iter__(self):
+        try:  # check that NODE_OK has attr 'nodes'
+            node_ok_shorter = 2 * len(self.NODE_OK.nodes) < len(self._atlas)
+        except AttributeError:
+            node_ok_shorter = False
+        if node_ok_shorter:
+            my_nodes = (n for n in self.NODE_OK.nodes if n in self._atlas)
+        else:
+            my_nodes = (n for n in self._atlas if self.NODE_OK(n))
+        for n in my_nodes:
+            some_keys_ok = False
+            for key in self._atlas[n]:
+                if self.EDGE_OK(n, key):
+                    some_keys_ok = True
+                    break
+            if some_keys_ok is True:
+                yield n
+
+    def __getitem__(self, nbr):
+        if (
+            nbr in self._atlas
+            and self.NODE_OK(nbr)
+            and any(self.EDGE_OK(nbr, key) for key in self._atlas[nbr])
+        ):
+
+            def new_node_ok(key):
+                return self.EDGE_OK(nbr, key)
+
+            return FilterAtlas(self._atlas[nbr], new_node_ok)
+        raise KeyError(f"Key {nbr} not found")
+
+
+class FilterMultiAdjacency(FilterAdjacency):  # multiedgedict
+    """A read-only Mapping of Mappings with filtering criteria
+    for nodes and edges.
+
+    It is a view into a dict-of-dict-of-dict-of-dict data structure,
+    and it selects nodes and edges that satisfy specific criteria
+    defined by ``NODE_OK`` and ``EDGE_OK``, respectively.
+
+    See Also
+    ========
+    FilterAtlas
+    FilterAdjacency
+    FilterMultiInner
+    """
+
+    def __getitem__(self, node):
+        if node in self._atlas and self.NODE_OK(node):
+
+            def edge_ok(nbr, key):
+                return self.NODE_OK(nbr) and self.EDGE_OK(node, nbr, key)
+
+            return FilterMultiInner(self._atlas[node], self.NODE_OK, edge_ok)
+        raise KeyError(f"Key {node} not found")
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/digraph.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/digraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ba56dea101c9f0caf4c2f9a2440456794dbfcc8
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/digraph.py
@@ -0,0 +1,1352 @@
+"""Base class for directed graphs."""
+
+from copy import deepcopy
+from functools import cached_property
+
+import networkx as nx
+from networkx import convert
+from networkx.classes.coreviews import AdjacencyView
+from networkx.classes.graph import Graph
+from networkx.classes.reportviews import (
+    DiDegreeView,
+    InDegreeView,
+    InEdgeView,
+    OutDegreeView,
+    OutEdgeView,
+)
+from networkx.exception import NetworkXError
+
+__all__ = ["DiGraph"]
+
+
+class _CachedPropertyResetterAdjAndSucc:
+    """Data Descriptor class that syncs and resets cached properties adj and succ
+
+    The cached properties `adj` and `succ` are reset whenever `_adj` or `_succ`
+    are set to new objects. In addition, the attributes `_succ` and `_adj`
+    are synced so these two names point to the same object.
+
+    Warning: most of the time, when ``G._adj`` is set, ``G._pred`` should also
+    be set to maintain a valid data structure. They share datadicts.
+
+    This object sits on a class and ensures that any instance of that
+    class clears its cached properties "succ" and "adj" whenever the
+    underlying instance attributes "_succ" or "_adj" are set to a new object.
+    It only affects the set process of the obj._adj and obj._succ attribute.
+    All get/del operations act as they normally would.
+
+    For info on Data Descriptors see: https://docs.python.org/3/howto/descriptor.html
+    """
+
+    def __set__(self, obj, value):
+        od = obj.__dict__
+        od["_adj"] = value
+        od["_succ"] = value
+        # reset cached properties
+        props = [
+            "adj",
+            "succ",
+            "edges",
+            "out_edges",
+            "degree",
+            "out_degree",
+            "in_degree",
+        ]
+        for prop in props:
+            if prop in od:
+                del od[prop]
+
+
+class _CachedPropertyResetterPred:
+    """Data Descriptor class for _pred that resets ``pred`` cached_property when needed
+
+    This assumes that the ``cached_property`` ``G.pred`` should be reset whenever
+    ``G._pred`` is set to a new value.
+
+    Warning: most of the time, when ``G._pred`` is set, ``G._adj`` should also
+    be set to maintain a valid data structure. They share datadicts.
+
+    This object sits on a class and ensures that any instance of that
+    class clears its cached property "pred" whenever the underlying
+    instance attribute "_pred" is set to a new object. It only affects
+    the set process of the obj._pred attribute. All get/del operations
+    act as they normally would.
+
+    For info on Data Descriptors see: https://docs.python.org/3/howto/descriptor.html
+    """
+
+    def __set__(self, obj, value):
+        od = obj.__dict__
+        od["_pred"] = value
+        # reset cached properties
+        props = ["pred", "in_edges", "degree", "out_degree", "in_degree"]
+        for prop in props:
+            if prop in od:
+                del od[prop]
+
+
+class DiGraph(Graph):
+    """
+    Base class for directed graphs.
+
+    A DiGraph stores nodes and edges with optional data, or attributes.
+
+    DiGraphs hold directed edges.  Self loops are allowed but multiple
+    (parallel) edges are not.
+
+    Nodes can be arbitrary (hashable) Python objects with optional
+    key/value attributes. By convention `None` is not used as a node.
+
+    Edges are represented as links between nodes with optional
+    key/value attributes.
+
+    Parameters
+    ----------
+    incoming_graph_data : input graph (optional, default: None)
+        Data to initialize graph. If None (default) an empty
+        graph is created.  The data can be any format that is supported
+        by the to_networkx_graph() function, currently including edge list,
+        dict of dicts, dict of lists, NetworkX graph, 2D NumPy array, SciPy
+        sparse matrix, or PyGraphviz graph.
+
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to graph as key=value pairs.
+
+    See Also
+    --------
+    Graph
+    MultiGraph
+    MultiDiGraph
+
+    Examples
+    --------
+    Create an empty graph structure (a "null graph") with no nodes and
+    no edges.
+
+    >>> G = nx.DiGraph()
+
+    G can be grown in several ways.
+
+    **Nodes:**
+
+    Add one node at a time:
+
+    >>> G.add_node(1)
+
+    Add the nodes from any container (a list, dict, set or
+    even the lines from a file or the nodes from another graph).
+
+    >>> G.add_nodes_from([2, 3])
+    >>> G.add_nodes_from(range(100, 110))
+    >>> H = nx.path_graph(10)
+    >>> G.add_nodes_from(H)
+
+    In addition to strings and integers any hashable Python object
+    (except None) can represent a node, e.g. a customized node object,
+    or even another Graph.
+
+    >>> G.add_node(H)
+
+    **Edges:**
+
+    G can also be grown by adding edges.
+
+    Add one edge,
+
+    >>> G.add_edge(1, 2)
+
+    a list of edges,
+
+    >>> G.add_edges_from([(1, 2), (1, 3)])
+
+    or a collection of edges,
+
+    >>> G.add_edges_from(H.edges)
+
+    If some edges connect nodes not yet in the graph, the nodes
+    are added automatically.  There are no errors when adding
+    nodes or edges that already exist.
+
+    **Attributes:**
+
+    Each graph, node, and edge can hold key/value attribute pairs
+    in an associated attribute dictionary (the keys must be hashable).
+    By default these are empty, but can be added or changed using
+    add_edge, add_node or direct manipulation of the attribute
+    dictionaries named graph, node and edge respectively.
+
+    >>> G = nx.DiGraph(day="Friday")
+    >>> G.graph
+    {'day': 'Friday'}
+
+    Add node attributes using add_node(), add_nodes_from() or G.nodes
+
+    >>> G.add_node(1, time="5pm")
+    >>> G.add_nodes_from([3], time="2pm")
+    >>> G.nodes[1]
+    {'time': '5pm'}
+    >>> G.nodes[1]["room"] = 714
+    >>> del G.nodes[1]["room"]  # remove attribute
+    >>> list(G.nodes(data=True))
+    [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
+
+    Add edge attributes using add_edge(), add_edges_from(), subscript
+    notation, or G.edges.
+
+    >>> G.add_edge(1, 2, weight=4.7)
+    >>> G.add_edges_from([(3, 4), (4, 5)], color="red")
+    >>> G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
+    >>> G[1][2]["weight"] = 4.7
+    >>> G.edges[1, 2]["weight"] = 4
+
+    Warning: we protect the graph data structure by making `G.edges[1, 2]` a
+    read-only dict-like structure. However, you can assign to attributes
+    in e.g. `G.edges[1, 2]`. Thus, use 2 sets of brackets to add/change
+    data attributes: `G.edges[1, 2]['weight'] = 4`
+    (For multigraphs: `MG.edges[u, v, key][name] = value`).
+
+    **Shortcuts:**
+
+    Many common graph features allow python syntax to speed reporting.
+
+    >>> 1 in G  # check if node in graph
+    True
+    >>> [n for n in G if n < 3]  # iterate through nodes
+    [1, 2]
+    >>> len(G)  # number of nodes in graph
+    5
+
+    Often the best way to traverse all edges of a graph is via the neighbors.
+    The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`
+
+    >>> for n, nbrsdict in G.adjacency():
+    ...     for nbr, eattr in nbrsdict.items():
+    ...         if "weight" in eattr:
+    ...             # Do something useful with the edges
+    ...             pass
+
+    But the edges reporting object is often more convenient:
+
+    >>> for u, v, weight in G.edges(data="weight"):
+    ...     if weight is not None:
+    ...         # Do something useful with the edges
+    ...         pass
+
+    **Reporting:**
+
+    Simple graph information is obtained using object-attributes and methods.
+    Reporting usually provides views instead of containers to reduce memory
+    usage. The views update as the graph is updated similarly to dict-views.
+    The objects `nodes`, `edges` and `adj` provide access to data attributes
+    via lookup (e.g. `nodes[n]`, `edges[u, v]`, `adj[u][v]`) and iteration
+    (e.g. `nodes.items()`, `nodes.data('color')`,
+    `nodes.data('color', default='blue')` and similarly for `edges`)
+    Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
+
+    For details on these and other miscellaneous methods, see below.
+
+    **Subclasses (Advanced):**
+
+    The Graph class uses a dict-of-dict-of-dict data structure.
+    The outer dict (node_dict) holds adjacency information keyed by node.
+    The next dict (adjlist_dict) represents the adjacency information and holds
+    edge data keyed by neighbor.  The inner dict (edge_attr_dict) represents
+    the edge data and holds edge attribute values keyed by attribute names.
+
+    Each of these three dicts can be replaced in a subclass by a user defined
+    dict-like object. In general, the dict-like features should be
+    maintained but extra features can be added. To replace one of the
+    dicts create a new graph class by changing the class(!) variable
+    holding the factory for that dict-like structure. The variable names are
+    node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,
+    adjlist_outer_dict_factory, edge_attr_dict_factory and graph_attr_dict_factory.
+
+    node_dict_factory : function, (default: dict)
+        Factory function to be used to create the dict containing node
+        attributes, keyed by node id.
+        It should require no arguments and return a dict-like object
+
+    node_attr_dict_factory: function, (default: dict)
+        Factory function to be used to create the node attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object
+
+    adjlist_outer_dict_factory : function, (default: dict)
+        Factory function to be used to create the outer-most dict
+        in the data structure that holds adjacency info keyed by node.
+        It should require no arguments and return a dict-like object.
+
+    adjlist_inner_dict_factory : function, optional (default: dict)
+        Factory function to be used to create the adjacency list
+        dict which holds edge data keyed by neighbor.
+        It should require no arguments and return a dict-like object
+
+    edge_attr_dict_factory : function, optional (default: dict)
+        Factory function to be used to create the edge attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    graph_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the graph attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    Typically, if your extension doesn't impact the data structure all
+    methods will inherited without issue except: `to_directed/to_undirected`.
+    By default these methods create a DiGraph/Graph class and you probably
+    want them to create your extension of a DiGraph/Graph. To facilitate
+    this we define two class variables that you can set in your subclass.
+
+    to_directed_class : callable, (default: DiGraph or MultiDiGraph)
+        Class to create a new graph structure in the `to_directed` method.
+        If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
+
+    to_undirected_class : callable, (default: Graph or MultiGraph)
+        Class to create a new graph structure in the `to_undirected` method.
+        If `None`, a NetworkX class (Graph or MultiGraph) is used.
+
+    **Subclassing Example**
+
+    Create a low memory graph class that effectively disallows edge
+    attributes by using a single attribute dict for all edges.
+    This reduces the memory used, but you lose edge attributes.
+
+    >>> class ThinGraph(nx.Graph):
+    ...     all_edge_dict = {"weight": 1}
+    ...
+    ...     def single_edge_dict(self):
+    ...         return self.all_edge_dict
+    ...
+    ...     edge_attr_dict_factory = single_edge_dict
+    >>> G = ThinGraph()
+    >>> G.add_edge(2, 1)
+    >>> G[2][1]
+    {'weight': 1}
+    >>> G.add_edge(2, 2)
+    >>> G[2][1] is G[2][2]
+    True
+    """
+
+    _adj = _CachedPropertyResetterAdjAndSucc()  # type: ignore[assignment]
+    _succ = _adj  # type: ignore[has-type]
+    _pred = _CachedPropertyResetterPred()
+
+    def __init__(self, incoming_graph_data=None, **attr):
+        """Initialize a graph with edges, name, or graph attributes.
+
+        Parameters
+        ----------
+        incoming_graph_data : input graph (optional, default: None)
+            Data to initialize graph.  If None (default) an empty
+            graph is created.  The data can be an edge list, or any
+            NetworkX graph object.  If the corresponding optional Python
+            packages are installed the data can also be a 2D NumPy array, a
+            SciPy sparse array, or a PyGraphviz graph.
+
+        attr : keyword arguments, optional (default= no attributes)
+            Attributes to add to graph as key=value pairs.
+
+        See Also
+        --------
+        convert
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G = nx.Graph(name="my graph")
+        >>> e = [(1, 2), (2, 3), (3, 4)]  # list of edges
+        >>> G = nx.Graph(e)
+
+        Arbitrary graph attribute pairs (key=value) may be assigned
+
+        >>> G = nx.Graph(e, day="Friday")
+        >>> G.graph
+        {'day': 'Friday'}
+
+        """
+        self.graph = self.graph_attr_dict_factory()  # dictionary for graph attributes
+        self._node = self.node_dict_factory()  # dictionary for node attr
+        # We store two adjacency lists:
+        # the predecessors of node n are stored in the dict self._pred
+        # the successors of node n are stored in the dict self._succ=self._adj
+        self._adj = self.adjlist_outer_dict_factory()  # empty adjacency dict successor
+        self._pred = self.adjlist_outer_dict_factory()  # predecessor
+        # Note: self._succ = self._adj  # successor
+
+        self.__networkx_cache__ = {}
+        # attempt to load graph with data
+        if incoming_graph_data is not None:
+            convert.to_networkx_graph(incoming_graph_data, create_using=self)
+        # load graph attributes (must be after convert)
+        self.graph.update(attr)
+
+    @cached_property
+    def adj(self):
+        """Graph adjacency object holding the neighbors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edge-data-dict.  So `G.adj[3][2]['color'] = 'blue'` sets
+        the color of the edge `(3, 2)` to `"blue"`.
+
+        Iterating over G.adj behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.adj[n].items():`.
+
+        The neighbor information is also provided by subscripting the graph.
+        So `for nbr, foovalue in G[node].data('foo', default=1):` works.
+
+        For directed graphs, `G.adj` holds outgoing (successor) info.
+        """
+        return AdjacencyView(self._succ)
+
+    @cached_property
+    def succ(self):
+        """Graph adjacency object holding the successors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edge-data-dict.  So `G.succ[3][2]['color'] = 'blue'` sets
+        the color of the edge `(3, 2)` to `"blue"`.
+
+        Iterating over G.succ behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.succ[n].items():`.  A data-view not provided
+        by dicts also exists: `for nbr, foovalue in G.succ[node].data('foo'):`
+        and a default can be set via a `default` argument to the `data` method.
+
+        The neighbor information is also provided by subscripting the graph.
+        So `for nbr, foovalue in G[node].data('foo', default=1):` works.
+
+        For directed graphs, `G.adj` is identical to `G.succ`.
+        """
+        return AdjacencyView(self._succ)
+
+    @cached_property
+    def pred(self):
+        """Graph adjacency object holding the predecessors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edge-data-dict.  So `G.pred[2][3]['color'] = 'blue'` sets
+        the color of the edge `(3, 2)` to `"blue"`.
+
+        Iterating over G.pred behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.pred[n].items():`.  A data-view not provided
+        by dicts also exists: `for nbr, foovalue in G.pred[node].data('foo'):`
+        A default can be set via a `default` argument to the `data` method.
+        """
+        return AdjacencyView(self._pred)
+
+    def add_node(self, node_for_adding, **attr):
+        """Add a single node `node_for_adding` and update node attributes.
+
+        Parameters
+        ----------
+        node_for_adding : node
+            A node can be any hashable Python object except None.
+        attr : keyword arguments, optional
+            Set or change node attributes using key=value.
+
+        See Also
+        --------
+        add_nodes_from
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_node(1)
+        >>> G.add_node("Hello")
+        >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
+        >>> G.add_node(K3)
+        >>> G.number_of_nodes()
+        3
+
+        Use keywords set/change node attributes:
+
+        >>> G.add_node(1, size=10)
+        >>> G.add_node(3, weight=0.4, UTM=("13S", 382871, 3972649))
+
+        Notes
+        -----
+        A hashable object is one that can be used as a key in a Python
+        dictionary. This includes strings, numbers, tuples of strings
+        and numbers, etc.
+
+        On many platforms hashable items also include mutables such as
+        NetworkX Graphs, though one should be careful that the hash
+        doesn't change on mutables.
+        """
+        if node_for_adding not in self._succ:
+            if node_for_adding is None:
+                raise ValueError("None cannot be a node")
+            self._succ[node_for_adding] = self.adjlist_inner_dict_factory()
+            self._pred[node_for_adding] = self.adjlist_inner_dict_factory()
+            attr_dict = self._node[node_for_adding] = self.node_attr_dict_factory()
+            attr_dict.update(attr)
+        else:  # update attr even if node already exists
+            self._node[node_for_adding].update(attr)
+        nx._clear_cache(self)
+
+    def add_nodes_from(self, nodes_for_adding, **attr):
+        """Add multiple nodes.
+
+        Parameters
+        ----------
+        nodes_for_adding : iterable container
+            A container of nodes (list, dict, set, etc.).
+            OR
+            A container of (node, attribute dict) tuples.
+            Node attributes are updated using the attribute dict.
+        attr : keyword arguments, optional (default= no attributes)
+            Update attributes for all nodes in nodes.
+            Node attributes specified in nodes as a tuple take
+            precedence over attributes specified via keyword arguments.
+
+        See Also
+        --------
+        add_node
+
+        Notes
+        -----
+        When adding nodes from an iterator over the graph you are changing,
+        a `RuntimeError` can be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_nodes)`, and pass this
+        object to `G.add_nodes_from`.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_nodes_from("Hello")
+        >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
+        >>> G.add_nodes_from(K3)
+        >>> sorted(G.nodes(), key=str)
+        [0, 1, 2, 'H', 'e', 'l', 'o']
+
+        Use keywords to update specific node attributes for every node.
+
+        >>> G.add_nodes_from([1, 2], size=10)
+        >>> G.add_nodes_from([3, 4], weight=0.4)
+
+        Use (node, attrdict) tuples to update attributes for specific nodes.
+
+        >>> G.add_nodes_from([(1, dict(size=11)), (2, {"color": "blue"})])
+        >>> G.nodes[1]["size"]
+        11
+        >>> H = nx.Graph()
+        >>> H.add_nodes_from(G.nodes(data=True))
+        >>> H.nodes[1]["size"]
+        11
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.DiGraph([(0, 1), (1, 2), (3, 4)])
+        >>> # wrong way - will raise RuntimeError
+        >>> # G.add_nodes_from(n + 1 for n in G.nodes)
+        >>> # correct way
+        >>> G.add_nodes_from(list(n + 1 for n in G.nodes))
+        """
+        for n in nodes_for_adding:
+            try:
+                newnode = n not in self._node
+                newdict = attr
+            except TypeError:
+                n, ndict = n
+                newnode = n not in self._node
+                newdict = attr.copy()
+                newdict.update(ndict)
+            if newnode:
+                if n is None:
+                    raise ValueError("None cannot be a node")
+                self._succ[n] = self.adjlist_inner_dict_factory()
+                self._pred[n] = self.adjlist_inner_dict_factory()
+                self._node[n] = self.node_attr_dict_factory()
+            self._node[n].update(newdict)
+        nx._clear_cache(self)
+
+    def remove_node(self, n):
+        """Remove node n.
+
+        Removes the node n and all adjacent edges.
+        Attempting to remove a nonexistent node will raise an exception.
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph
+
+        Raises
+        ------
+        NetworkXError
+           If n is not in the graph.
+
+        See Also
+        --------
+        remove_nodes_from
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> list(G.edges)
+        [(0, 1), (1, 2)]
+        >>> G.remove_node(1)
+        >>> list(G.edges)
+        []
+
+        """
+        try:
+            nbrs = self._succ[n]
+            del self._node[n]
+        except KeyError as err:  # NetworkXError if n not in self
+            raise NetworkXError(f"The node {n} is not in the digraph.") from err
+        for u in nbrs:
+            del self._pred[u][n]  # remove all edges n-u in digraph
+        del self._succ[n]  # remove node from succ
+        for u in self._pred[n]:
+            del self._succ[u][n]  # remove all edges n-u in digraph
+        del self._pred[n]  # remove node from pred
+        nx._clear_cache(self)
+
+    def remove_nodes_from(self, nodes):
+        """Remove multiple nodes.
+
+        Parameters
+        ----------
+        nodes : iterable container
+            A container of nodes (list, dict, set, etc.).  If a node
+            in the container is not in the graph it is silently ignored.
+
+        See Also
+        --------
+        remove_node
+
+        Notes
+        -----
+        When removing nodes from an iterator over the graph you are changing,
+        a `RuntimeError` will be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_nodes)`, and pass this
+        object to `G.remove_nodes_from`.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> e = list(G.nodes)
+        >>> e
+        [0, 1, 2]
+        >>> G.remove_nodes_from(e)
+        >>> list(G.nodes)
+        []
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.DiGraph([(0, 1), (1, 2), (3, 4)])
+        >>> # this command will fail, as the graph's dict is modified during iteration
+        >>> # G.remove_nodes_from(n for n in G.nodes if n < 2)
+        >>> # this command will work, since the dictionary underlying graph is not modified
+        >>> G.remove_nodes_from(list(n for n in G.nodes if n < 2))
+        """
+        for n in nodes:
+            try:
+                succs = self._succ[n]
+                del self._node[n]
+                for u in succs:
+                    del self._pred[u][n]  # remove all edges n-u in digraph
+                del self._succ[n]  # now remove node
+                for u in self._pred[n]:
+                    del self._succ[u][n]  # remove all edges n-u in digraph
+                del self._pred[n]  # now remove node
+            except KeyError:
+                pass  # silent failure on remove
+        nx._clear_cache(self)
+
+    def add_edge(self, u_of_edge, v_of_edge, **attr):
+        """Add an edge between u and v.
+
+        The nodes u and v will be automatically added if they are
+        not already in the graph.
+
+        Edge attributes can be specified with keywords or by directly
+        accessing the edge's attribute dictionary. See examples below.
+
+        Parameters
+        ----------
+        u_of_edge, v_of_edge : nodes
+            Nodes can be, for example, strings or numbers.
+            Nodes must be hashable (and not None) Python objects.
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        See Also
+        --------
+        add_edges_from : add a collection of edges
+
+        Notes
+        -----
+        Adding an edge that already exists updates the edge data.
+
+        Many NetworkX algorithms designed for weighted graphs use
+        an edge attribute (by default `weight`) to hold a numerical value.
+
+        Examples
+        --------
+        The following all add the edge e=(1, 2) to graph G:
+
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> e = (1, 2)
+        >>> G.add_edge(1, 2)  # explicit two-node form
+        >>> G.add_edge(*e)  # single edge as tuple of two nodes
+        >>> G.add_edges_from([(1, 2)])  # add edges from iterable container
+
+        Associate data to edges using keywords:
+
+        >>> G.add_edge(1, 2, weight=3)
+        >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
+
+        For non-string attribute keys, use subscript notation.
+
+        >>> G.add_edge(1, 2)
+        >>> G[1][2].update({0: 5})
+        >>> G.edges[1, 2].update({0: 5})
+        """
+        u, v = u_of_edge, v_of_edge
+        # add nodes
+        if u not in self._succ:
+            if u is None:
+                raise ValueError("None cannot be a node")
+            self._succ[u] = self.adjlist_inner_dict_factory()
+            self._pred[u] = self.adjlist_inner_dict_factory()
+            self._node[u] = self.node_attr_dict_factory()
+        if v not in self._succ:
+            if v is None:
+                raise ValueError("None cannot be a node")
+            self._succ[v] = self.adjlist_inner_dict_factory()
+            self._pred[v] = self.adjlist_inner_dict_factory()
+            self._node[v] = self.node_attr_dict_factory()
+        # add the edge
+        datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
+        datadict.update(attr)
+        self._succ[u][v] = datadict
+        self._pred[v][u] = datadict
+        nx._clear_cache(self)
+
+    def add_edges_from(self, ebunch_to_add, **attr):
+        """Add all the edges in ebunch_to_add.
+
+        Parameters
+        ----------
+        ebunch_to_add : container of edges
+            Each edge given in the container will be added to the
+            graph. The edges must be given as 2-tuples (u, v) or
+            3-tuples (u, v, d) where d is a dictionary containing edge data.
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        See Also
+        --------
+        add_edge : add a single edge
+        add_weighted_edges_from : convenient way to add weighted edges
+
+        Notes
+        -----
+        Adding the same edge twice has no effect but any edge data
+        will be updated when each duplicate edge is added.
+
+        Edge attributes specified in an ebunch take precedence over
+        attributes specified via keyword arguments.
+
+        When adding edges from an iterator over the graph you are changing,
+        a `RuntimeError` can be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_edges)`, and pass this
+        object to `G.add_edges_from`.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_edges_from([(0, 1), (1, 2)])  # using a list of edge tuples
+        >>> e = zip(range(0, 3), range(1, 4))
+        >>> G.add_edges_from(e)  # Add the path graph 0-1-2-3
+
+        Associate data to edges
+
+        >>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
+        >>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.DiGraph([(1, 2), (2, 3), (3, 4)])
+        >>> # Grow graph by one new node, adding edges to all existing nodes.
+        >>> # wrong way - will raise RuntimeError
+        >>> # G.add_edges_from(((5, n) for n in G.nodes))
+        >>> # right way - note that there will be no self-edge for node 5
+        >>> G.add_edges_from(list((5, n) for n in G.nodes))
+        """
+        for e in ebunch_to_add:
+            ne = len(e)
+            if ne == 3:
+                u, v, dd = e
+            elif ne == 2:
+                u, v = e
+                dd = {}
+            else:
+                raise NetworkXError(f"Edge tuple {e} must be a 2-tuple or 3-tuple.")
+            if u not in self._succ:
+                if u is None:
+                    raise ValueError("None cannot be a node")
+                self._succ[u] = self.adjlist_inner_dict_factory()
+                self._pred[u] = self.adjlist_inner_dict_factory()
+                self._node[u] = self.node_attr_dict_factory()
+            if v not in self._succ:
+                if v is None:
+                    raise ValueError("None cannot be a node")
+                self._succ[v] = self.adjlist_inner_dict_factory()
+                self._pred[v] = self.adjlist_inner_dict_factory()
+                self._node[v] = self.node_attr_dict_factory()
+            datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
+            datadict.update(attr)
+            datadict.update(dd)
+            self._succ[u][v] = datadict
+            self._pred[v][u] = datadict
+        nx._clear_cache(self)
+
+    def remove_edge(self, u, v):
+        """Remove the edge between u and v.
+
+        Parameters
+        ----------
+        u, v : nodes
+            Remove the edge between nodes u and v.
+
+        Raises
+        ------
+        NetworkXError
+            If there is not an edge between u and v.
+
+        See Also
+        --------
+        remove_edges_from : remove a collection of edges
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, etc
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.remove_edge(0, 1)
+        >>> e = (1, 2)
+        >>> G.remove_edge(*e)  # unpacks e from an edge tuple
+        >>> e = (2, 3, {"weight": 7})  # an edge with attribute data
+        >>> G.remove_edge(*e[:2])  # select first part of edge tuple
+        """
+        try:
+            del self._succ[u][v]
+            del self._pred[v][u]
+        except KeyError as err:
+            raise NetworkXError(f"The edge {u}-{v} not in graph.") from err
+        nx._clear_cache(self)
+
+    def remove_edges_from(self, ebunch):
+        """Remove all edges specified in ebunch.
+
+        Parameters
+        ----------
+        ebunch: list or container of edge tuples
+            Each edge given in the list or container will be removed
+            from the graph. The edges can be:
+
+                - 2-tuples (u, v) edge between u and v.
+                - 3-tuples (u, v, k) where k is ignored.
+
+        See Also
+        --------
+        remove_edge : remove a single edge
+
+        Notes
+        -----
+        Will fail silently if an edge in ebunch is not in the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> ebunch = [(1, 2), (2, 3)]
+        >>> G.remove_edges_from(ebunch)
+        """
+        for e in ebunch:
+            u, v = e[:2]  # ignore edge data
+            if u in self._succ and v in self._succ[u]:
+                del self._succ[u][v]
+                del self._pred[v][u]
+        nx._clear_cache(self)
+
+    def has_successor(self, u, v):
+        """Returns True if node u has successor v.
+
+        This is true if graph has the edge u->v.
+        """
+        return u in self._succ and v in self._succ[u]
+
+    def has_predecessor(self, u, v):
+        """Returns True if node u has predecessor v.
+
+        This is true if graph has the edge u<-v.
+        """
+        return u in self._pred and v in self._pred[u]
+
+    def successors(self, n):
+        """Returns an iterator over successor nodes of n.
+
+        A successor of n is a node m such that there exists a directed
+        edge from n to m.
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph
+
+        Raises
+        ------
+        NetworkXError
+           If n is not in the graph.
+
+        See Also
+        --------
+        predecessors
+
+        Notes
+        -----
+        neighbors() and successors() are the same.
+        """
+        try:
+            return iter(self._succ[n])
+        except KeyError as err:
+            raise NetworkXError(f"The node {n} is not in the digraph.") from err
+
+    # digraph definitions
+    neighbors = successors
+
+    def predecessors(self, n):
+        """Returns an iterator over predecessor nodes of n.
+
+        A predecessor of n is a node m such that there exists a directed
+        edge from m to n.
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph
+
+        Raises
+        ------
+        NetworkXError
+           If n is not in the graph.
+
+        See Also
+        --------
+        successors
+        """
+        try:
+            return iter(self._pred[n])
+        except KeyError as err:
+            raise NetworkXError(f"The node {n} is not in the digraph.") from err
+
+    @cached_property
+    def edges(self):
+        """An OutEdgeView of the DiGraph as G.edges or G.edges().
+
+        edges(self, nbunch=None, data=False, default=None)
+
+        The OutEdgeView provides set-like operations on the edge-tuples
+        as well as edge attribute lookup. When called, it also provides
+        an EdgeDataView object which allows control of access to edge
+        attributes (but does not provide set-like operations).
+        Hence, `G.edges[u, v]['color']` provides the value of the color
+        attribute for edge `(u, v)` while
+        `for (u, v, c) in G.edges.data('color', default='red'):`
+        iterates through all the edges yielding the color attribute
+        with default `'red'` if no color attribute exists.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges from these nodes.
+        data : string or bool, optional (default=False)
+            The edge attribute returned in 3-tuple (u, v, ddict[data]).
+            If True, return edge attribute dict in 3-tuple (u, v, ddict).
+            If False, return 2-tuple (u, v).
+        default : value, optional (default=None)
+            Value used for edges that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        edges : OutEdgeView
+            A view of edge attributes, usually it iterates over (u, v)
+            or (u, v, d) tuples of edges, but can also be used for
+            attribute lookup as `edges[u, v]['foo']`.
+
+        See Also
+        --------
+        in_edges, out_edges
+
+        Notes
+        -----
+        Nodes in nbunch that are not in the graph will be (quietly) ignored.
+        For directed graphs this returns the out-edges.
+
+        Examples
+        --------
+        >>> G = nx.DiGraph()  # or MultiDiGraph, etc
+        >>> nx.add_path(G, [0, 1, 2])
+        >>> G.add_edge(2, 3, weight=5)
+        >>> [e for e in G.edges]
+        [(0, 1), (1, 2), (2, 3)]
+        >>> G.edges.data()  # default data is {} (empty dict)
+        OutEdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})])
+        >>> G.edges.data("weight", default=1)
+        OutEdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)])
+        >>> G.edges([0, 2])  # only edges originating from these nodes
+        OutEdgeDataView([(0, 1), (2, 3)])
+        >>> G.edges(0)  # only edges from node 0
+        OutEdgeDataView([(0, 1)])
+
+        """
+        return OutEdgeView(self)
+
+    # alias out_edges to edges
+    @cached_property
+    def out_edges(self):
+        return OutEdgeView(self)
+
+    out_edges.__doc__ = edges.__doc__
+
+    @cached_property
+    def in_edges(self):
+        """A view of the in edges of the graph as G.in_edges or G.in_edges().
+
+        in_edges(self, nbunch=None, data=False, default=None):
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+        data : string or bool, optional (default=False)
+            The edge attribute returned in 3-tuple (u, v, ddict[data]).
+            If True, return edge attribute dict in 3-tuple (u, v, ddict).
+            If False, return 2-tuple (u, v).
+        default : value, optional (default=None)
+            Value used for edges that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        in_edges : InEdgeView or InEdgeDataView
+            A view of edge attributes, usually it iterates over (u, v)
+            or (u, v, d) tuples of edges, but can also be used for
+            attribute lookup as `edges[u, v]['foo']`.
+
+        Examples
+        --------
+        >>> G = nx.DiGraph()
+        >>> G.add_edge(1, 2, color="blue")
+        >>> G.in_edges()
+        InEdgeView([(1, 2)])
+        >>> G.in_edges(nbunch=2)
+        InEdgeDataView([(1, 2)])
+
+        See Also
+        --------
+        edges
+        """
+        return InEdgeView(self)
+
+    @cached_property
+    def degree(self):
+        """A DegreeView for the Graph as G.degree or G.degree().
+
+        The node degree is the number of edges adjacent to the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iterator for (node, degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The name of an edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        DiDegreeView or int
+            If multiple nodes are requested (the default), returns a `DiDegreeView`
+            mapping nodes to their degree.
+            If a single node is requested, returns the degree of the node as an integer.
+
+        See Also
+        --------
+        in_degree, out_degree
+
+        Examples
+        --------
+        >>> G = nx.DiGraph()  # or MultiDiGraph
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.degree(0)  # node 0 with degree 1
+        1
+        >>> list(G.degree([0, 1, 2]))
+        [(0, 1), (1, 2), (2, 2)]
+
+        """
+        return DiDegreeView(self)
+
+    @cached_property
+    def in_degree(self):
+        """An InDegreeView for (node, in_degree) or in_degree for single node.
+
+        The node in_degree is the number of edges pointing to the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iteration over (node, in_degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The name of an edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        If a single node is requested
+        deg : int
+            In-degree of the node
+
+        OR if multiple nodes are requested
+        nd_iter : iterator
+            The iterator returns two-tuples of (node, in-degree).
+
+        See Also
+        --------
+        degree, out_degree
+
+        Examples
+        --------
+        >>> G = nx.DiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.in_degree(0)  # node 0 with degree 0
+        0
+        >>> list(G.in_degree([0, 1, 2]))
+        [(0, 0), (1, 1), (2, 1)]
+
+        """
+        return InDegreeView(self)
+
+    @cached_property
+    def out_degree(self):
+        """An OutDegreeView for (node, out_degree)
+
+        The node out_degree is the number of edges pointing out of the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iterator over (node, out_degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The name of an edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        If a single node is requested
+        deg : int
+            Out-degree of the node
+
+        OR if multiple nodes are requested
+        nd_iter : iterator
+            The iterator returns two-tuples of (node, out-degree).
+
+        See Also
+        --------
+        degree, in_degree
+
+        Examples
+        --------
+        >>> G = nx.DiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.out_degree(0)  # node 0 with degree 1
+        1
+        >>> list(G.out_degree([0, 1, 2]))
+        [(0, 1), (1, 1), (2, 1)]
+
+        """
+        return OutDegreeView(self)
+
+    def clear(self):
+        """Remove all nodes and edges from the graph.
+
+        This also removes the name, and all graph, node, and edge attributes.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.clear()
+        >>> list(G.nodes)
+        []
+        >>> list(G.edges)
+        []
+
+        """
+        self._succ.clear()
+        self._pred.clear()
+        self._node.clear()
+        self.graph.clear()
+        nx._clear_cache(self)
+
+    def clear_edges(self):
+        """Remove all edges from the graph without altering nodes.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.clear_edges()
+        >>> list(G.nodes)
+        [0, 1, 2, 3]
+        >>> list(G.edges)
+        []
+
+        """
+        for predecessor_dict in self._pred.values():
+            predecessor_dict.clear()
+        for successor_dict in self._succ.values():
+            successor_dict.clear()
+        nx._clear_cache(self)
+
+    def is_multigraph(self):
+        """Returns True if graph is a multigraph, False otherwise."""
+        return False
+
+    def is_directed(self):
+        """Returns True if graph is directed, False otherwise."""
+        return True
+
+    def to_undirected(self, reciprocal=False, as_view=False):
+        """Returns an undirected representation of the digraph.
+
+        Parameters
+        ----------
+        reciprocal : bool (optional)
+          If True only keep edges that appear in both directions
+          in the original digraph.
+        as_view : bool (optional, default=False)
+          If True return an undirected view of the original directed graph.
+
+        Returns
+        -------
+        G : Graph
+            An undirected graph with the same name and nodes and
+            with edge (u, v, data) if either (u, v, data) or (v, u, data)
+            is in the digraph.  If both edges exist in digraph and
+            their edge data is different, only one edge is created
+            with an arbitrary choice of which edge data to use.
+            You must check and correct for this manually if desired.
+
+        See Also
+        --------
+        Graph, copy, add_edge, add_edges_from
+
+        Notes
+        -----
+        If edges in both directions (u, v) and (v, u) exist in the
+        graph, attributes for the new undirected edge will be a combination of
+        the attributes of the directed edges.  The edge data is updated
+        in the (arbitrary) order that the edges are encountered.  For
+        more customized control of the edge attributes use add_edge().
+
+        This returns a "deepcopy" of the edge, node, and
+        graph attributes which attempts to completely copy
+        all of the data and references.
+
+        This is in contrast to the similar G=DiGraph(D) which returns a
+        shallow copy of the data.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Warning: If you have subclassed DiGraph to use dict-like objects
+        in the data structure, those changes do not transfer to the
+        Graph created by this method.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(2)  # or MultiGraph, etc
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1), (1, 0)]
+        >>> G2 = H.to_undirected()
+        >>> list(G2.edges)
+        [(0, 1)]
+        """
+        graph_class = self.to_undirected_class()
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self, graph_class)
+        # deepcopy when not a view
+        G = graph_class()
+        G.graph.update(deepcopy(self.graph))
+        G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+        if reciprocal is True:
+            G.add_edges_from(
+                (u, v, deepcopy(d))
+                for u, nbrs in self._adj.items()
+                for v, d in nbrs.items()
+                if v in self._pred[u]
+            )
+        else:
+            G.add_edges_from(
+                (u, v, deepcopy(d))
+                for u, nbrs in self._adj.items()
+                for v, d in nbrs.items()
+            )
+        return G
+
+    def reverse(self, copy=True):
+        """Returns the reverse of the graph.
+
+        The reverse is a graph with the same nodes and edges
+        but with the directions of the edges reversed.
+
+        Parameters
+        ----------
+        copy : bool optional (default=True)
+            If True, return a new DiGraph holding the reversed edges.
+            If False, the reverse graph is created using a view of
+            the original graph.
+        """
+        if copy:
+            H = self.__class__()
+            H.graph.update(deepcopy(self.graph))
+            H.add_nodes_from((n, deepcopy(d)) for n, d in self.nodes.items())
+            H.add_edges_from((v, u, deepcopy(d)) for u, v, d in self.edges(data=True))
+            return H
+        return nx.reverse_view(self)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/filters.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/filters.py
new file mode 100644
index 0000000000000000000000000000000000000000..e989e22bb6d7e79b6eab34103edd263d82694fd4
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/filters.py
@@ -0,0 +1,95 @@
+"""Filter factories to hide or show sets of nodes and edges.
+
+These filters return the function used when creating `SubGraph`.
+"""
+
+__all__ = [
+    "no_filter",
+    "hide_nodes",
+    "hide_edges",
+    "hide_multiedges",
+    "hide_diedges",
+    "hide_multidiedges",
+    "show_nodes",
+    "show_edges",
+    "show_multiedges",
+    "show_diedges",
+    "show_multidiedges",
+]
+
+
+def no_filter(*items):
+    """Returns a filter function that always evaluates to True."""
+    return True
+
+
+def hide_nodes(nodes):
+    """Returns a filter function that hides specific nodes."""
+    nodes = set(nodes)
+    return lambda node: node not in nodes
+
+
+def hide_diedges(edges):
+    """Returns a filter function that hides specific directed edges."""
+    edges = {(u, v) for u, v in edges}
+    return lambda u, v: (u, v) not in edges
+
+
+def hide_edges(edges):
+    """Returns a filter function that hides specific undirected edges."""
+    alledges = set(edges) | {(v, u) for (u, v) in edges}
+    return lambda u, v: (u, v) not in alledges
+
+
+def hide_multidiedges(edges):
+    """Returns a filter function that hides specific multi-directed edges."""
+    edges = {(u, v, k) for u, v, k in edges}
+    return lambda u, v, k: (u, v, k) not in edges
+
+
+def hide_multiedges(edges):
+    """Returns a filter function that hides specific multi-undirected edges."""
+    alledges = set(edges) | {(v, u, k) for (u, v, k) in edges}
+    return lambda u, v, k: (u, v, k) not in alledges
+
+
+# write show_nodes as a class to make SubGraph pickleable
+class show_nodes:
+    """Filter class to show specific nodes.
+
+    Attach the set of nodes as an attribute to speed up this commonly used filter
+
+    Note that another allowed attribute for filters is to store the number of nodes
+    on the filter as attribute `length` (used in `__len__`). It is a user
+    responsibility to ensure this attribute is accurate if present.
+    """
+
+    def __init__(self, nodes):
+        self.nodes = set(nodes)
+
+    def __call__(self, node):
+        return node in self.nodes
+
+
+def show_diedges(edges):
+    """Returns a filter function that shows specific directed edges."""
+    edges = {(u, v) for u, v in edges}
+    return lambda u, v: (u, v) in edges
+
+
+def show_edges(edges):
+    """Returns a filter function that shows specific undirected edges."""
+    alledges = set(edges) | {(v, u) for (u, v) in edges}
+    return lambda u, v: (u, v) in alledges
+
+
+def show_multidiedges(edges):
+    """Returns a filter function that shows specific multi-directed edges."""
+    edges = {(u, v, k) for u, v, k in edges}
+    return lambda u, v, k: (u, v, k) in edges
+
+
+def show_multiedges(edges):
+    """Returns a filter function that shows specific multi-undirected edges."""
+    alledges = set(edges) | {(v, u, k) for (u, v, k) in edges}
+    return lambda u, v, k: (u, v, k) in alledges
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/function.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/function.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cf72780df99db901f1621e61bfd0c302d6e9a5e
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/function.py
@@ -0,0 +1,1416 @@
+"""Functional interface to graph methods and assorted utilities."""
+
+from collections import Counter
+from itertools import chain
+
+import networkx as nx
+from networkx.utils import not_implemented_for, pairwise
+
+__all__ = [
+    "nodes",
+    "edges",
+    "degree",
+    "degree_histogram",
+    "neighbors",
+    "number_of_nodes",
+    "number_of_edges",
+    "density",
+    "is_directed",
+    "freeze",
+    "is_frozen",
+    "subgraph",
+    "induced_subgraph",
+    "edge_subgraph",
+    "restricted_view",
+    "to_directed",
+    "to_undirected",
+    "add_star",
+    "add_path",
+    "add_cycle",
+    "create_empty_copy",
+    "set_node_attributes",
+    "get_node_attributes",
+    "remove_node_attributes",
+    "set_edge_attributes",
+    "get_edge_attributes",
+    "remove_edge_attributes",
+    "all_neighbors",
+    "non_neighbors",
+    "non_edges",
+    "common_neighbors",
+    "is_weighted",
+    "is_negatively_weighted",
+    "is_empty",
+    "selfloop_edges",
+    "nodes_with_selfloops",
+    "number_of_selfloops",
+    "path_weight",
+    "is_path",
+]
+
+
+def nodes(G):
+    """Returns a NodeView over the graph nodes.
+
+    This function wraps the :func:`G.nodes <networkx.Graph.nodes>` property.
+    """
+    return G.nodes()
+
+
+def edges(G, nbunch=None):
+    """Returns an edge view of edges incident to nodes in nbunch.
+
+    Return all edges if nbunch is unspecified or nbunch=None.
+
+    For digraphs, edges=out_edges
+
+    This function wraps the :func:`G.edges <networkx.Graph.edges>` property.
+    """
+    return G.edges(nbunch)
+
+
+def degree(G, nbunch=None, weight=None):
+    """Returns a degree view of single node or of nbunch of nodes.
+    If nbunch is omitted, then return degrees of *all* nodes.
+
+    This function wraps the :func:`G.degree <networkx.Graph.degree>` property.
+    """
+    return G.degree(nbunch, weight)
+
+
+def neighbors(G, n):
+    """Returns an iterator over all neighbors of node n.
+
+    This function wraps the :func:`G.neighbors <networkx.Graph.neighbors>` function.
+    """
+    return G.neighbors(n)
+
+
+def number_of_nodes(G):
+    """Returns the number of nodes in the graph.
+
+    This function wraps the :func:`G.number_of_nodes <networkx.Graph.number_of_nodes>` function.
+    """
+    return G.number_of_nodes()
+
+
+def number_of_edges(G):
+    """Returns the number of edges in the graph.
+
+    This function wraps the :func:`G.number_of_edges <networkx.Graph.number_of_edges>` function.
+    """
+    return G.number_of_edges()
+
+
+def density(G):
+    r"""Returns the density of a graph.
+
+    The density for undirected graphs is
+
+    .. math::
+
+       d = \frac{2m}{n(n-1)},
+
+    and for directed graphs is
+
+    .. math::
+
+       d = \frac{m}{n(n-1)},
+
+    where `n` is the number of nodes and `m`  is the number of edges in `G`.
+
+    Notes
+    -----
+    The density is 0 for a graph without edges and 1 for a complete graph.
+    The density of multigraphs can be higher than 1.
+
+    Self loops are counted in the total number of edges so graphs with self
+    loops can have density higher than 1.
+    """
+    n = number_of_nodes(G)
+    m = number_of_edges(G)
+    if m == 0 or n <= 1:
+        return 0
+    d = m / (n * (n - 1))
+    if not G.is_directed():
+        d *= 2
+    return d
+
+
+def degree_histogram(G):
+    """Returns a list of the frequency of each degree value.
+
+    Parameters
+    ----------
+    G : Networkx graph
+       A graph
+
+    Returns
+    -------
+    hist : list
+       A list of frequencies of degrees.
+       The degree values are the index in the list.
+
+    Notes
+    -----
+    Note: the bins are width one, hence len(list) can be large
+    (Order(number_of_edges))
+    """
+    counts = Counter(d for n, d in G.degree())
+    return [counts.get(i, 0) for i in range(max(counts) + 1 if counts else 0)]
+
+
+def is_directed(G):
+    """Return True if graph is directed."""
+    return G.is_directed()
+
+
+def frozen(*args, **kwargs):
+    """Dummy method for raising errors when trying to modify frozen graphs"""
+    raise nx.NetworkXError("Frozen graph can't be modified")
+
+
+def freeze(G):
+    """Modify graph to prevent further change by adding or removing
+    nodes or edges.
+
+    Node and edge data can still be modified.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> G = nx.freeze(G)
+    >>> try:
+    ...     G.add_edge(4, 5)
+    ... except nx.NetworkXError as err:
+    ...     print(str(err))
+    Frozen graph can't be modified
+
+    Notes
+    -----
+    To "unfreeze" a graph you must make a copy by creating a new graph object:
+
+    >>> graph = nx.path_graph(4)
+    >>> frozen_graph = nx.freeze(graph)
+    >>> unfrozen_graph = nx.Graph(frozen_graph)
+    >>> nx.is_frozen(unfrozen_graph)
+    False
+
+    See Also
+    --------
+    is_frozen
+    """
+    G.add_node = frozen
+    G.add_nodes_from = frozen
+    G.remove_node = frozen
+    G.remove_nodes_from = frozen
+    G.add_edge = frozen
+    G.add_edges_from = frozen
+    G.add_weighted_edges_from = frozen
+    G.remove_edge = frozen
+    G.remove_edges_from = frozen
+    G.clear = frozen
+    G.clear_edges = frozen
+    G.frozen = True
+    return G
+
+
+def is_frozen(G):
+    """Returns True if graph is frozen.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    See Also
+    --------
+    freeze
+    """
+    try:
+        return G.frozen
+    except AttributeError:
+        return False
+
+
+def add_star(G_to_add_to, nodes_for_star, **attr):
+    """Add a star to Graph G_to_add_to.
+
+    The first node in `nodes_for_star` is the middle of the star.
+    It is connected to all other nodes.
+
+    Parameters
+    ----------
+    G_to_add_to : graph
+        A NetworkX graph
+    nodes_for_star : iterable container
+        A container of nodes.
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to every edge in star.
+
+    See Also
+    --------
+    add_path, add_cycle
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_star(G, [0, 1, 2, 3])
+    >>> nx.add_star(G, [10, 11, 12], weight=2)
+    """
+    nlist = iter(nodes_for_star)
+    try:
+        v = next(nlist)
+    except StopIteration:
+        return
+    G_to_add_to.add_node(v)
+    edges = ((v, n) for n in nlist)
+    G_to_add_to.add_edges_from(edges, **attr)
+
+
+def add_path(G_to_add_to, nodes_for_path, **attr):
+    """Add a path to the Graph G_to_add_to.
+
+    Parameters
+    ----------
+    G_to_add_to : graph
+        A NetworkX graph
+    nodes_for_path : iterable container
+        A container of nodes.  A path will be constructed from
+        the nodes (in order) and added to the graph.
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to every edge in path.
+
+    See Also
+    --------
+    add_star, add_cycle
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_path(G, [0, 1, 2, 3])
+    >>> nx.add_path(G, [10, 11, 12], weight=7)
+    """
+    nlist = iter(nodes_for_path)
+    try:
+        first_node = next(nlist)
+    except StopIteration:
+        return
+    G_to_add_to.add_node(first_node)
+    G_to_add_to.add_edges_from(pairwise(chain((first_node,), nlist)), **attr)
+
+
+def add_cycle(G_to_add_to, nodes_for_cycle, **attr):
+    """Add a cycle to the Graph G_to_add_to.
+
+    Parameters
+    ----------
+    G_to_add_to : graph
+        A NetworkX graph
+    nodes_for_cycle: iterable container
+        A container of nodes.  A cycle will be constructed from
+        the nodes (in order) and added to the graph.
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to every edge in cycle.
+
+    See Also
+    --------
+    add_path, add_star
+
+    Examples
+    --------
+    >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+    >>> nx.add_cycle(G, [0, 1, 2, 3])
+    >>> nx.add_cycle(G, [10, 11, 12], weight=7)
+    """
+    nlist = iter(nodes_for_cycle)
+    try:
+        first_node = next(nlist)
+    except StopIteration:
+        return
+    G_to_add_to.add_node(first_node)
+    G_to_add_to.add_edges_from(
+        pairwise(chain((first_node,), nlist), cyclic=True), **attr
+    )
+
+
+def subgraph(G, nbunch):
+    """Returns the subgraph induced on nodes in nbunch.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nbunch : list, iterable
+       A container of nodes that will be iterated through once (thus
+       it should be an iterator or be iterable).  Each element of the
+       container should be a valid node type: any hashable type except
+       None.  If nbunch is None, return all edges data in the graph.
+       Nodes in nbunch that are not in the graph will be (quietly)
+       ignored.
+
+    Notes
+    -----
+    subgraph(G) calls G.subgraph()
+    """
+    return G.subgraph(nbunch)
+
+
+def induced_subgraph(G, nbunch):
+    """Returns a SubGraph view of `G` showing only nodes in nbunch.
+
+    The induced subgraph of a graph on a set of nodes N is the
+    graph with nodes N and edges from G which have both ends in N.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    nbunch : node, container of nodes or None (for all nodes)
+
+    Returns
+    -------
+    subgraph : SubGraph View
+        A read-only view of the subgraph in `G` induced by the nodes.
+        Changes to the graph `G` will be reflected in the view.
+
+    Notes
+    -----
+    To create a mutable subgraph with its own copies of nodes
+    edges and attributes use `subgraph.copy()` or `Graph(subgraph)`
+
+    For an inplace reduction of a graph to a subgraph you can remove nodes:
+    `G.remove_nodes_from(n in G if n not in set(nbunch))`
+
+    If you are going to compute subgraphs of your subgraphs you could
+    end up with a chain of views that can be very slow once the chain
+    has about 15 views in it. If they are all induced subgraphs, you
+    can short-cut the chain by making them all subgraphs of the original
+    graph. The graph class method `G.subgraph` does this when `G` is
+    a subgraph. In contrast, this function allows you to choose to build
+    chains or not, as you wish. The returned subgraph is a view on `G`.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+    >>> H = nx.induced_subgraph(G, [0, 1, 3])
+    >>> list(H.edges)
+    [(0, 1)]
+    >>> list(H.nodes)
+    [0, 1, 3]
+    """
+    induced_nodes = nx.filters.show_nodes(G.nbunch_iter(nbunch))
+    return nx.subgraph_view(G, filter_node=induced_nodes)
+
+
+def edge_subgraph(G, edges):
+    """Returns a view of the subgraph induced by the specified edges.
+
+    The induced subgraph contains each edge in `edges` and each
+    node incident to any of those edges.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    edges : iterable
+        An iterable of edges. Edges not present in `G` are ignored.
+
+    Returns
+    -------
+    subgraph : SubGraph View
+        A read-only edge-induced subgraph of `G`.
+        Changes to `G` are reflected in the view.
+
+    Notes
+    -----
+    To create a mutable subgraph with its own copies of nodes
+    edges and attributes use `subgraph.copy()` or `Graph(subgraph)`
+
+    If you create a subgraph of a subgraph recursively you can end up
+    with a chain of subgraphs that becomes very slow with about 15
+    nested subgraph views. Luckily the edge_subgraph filter nests
+    nicely so you can use the original graph as G in this function
+    to avoid chains. We do not rule out chains programmatically so
+    that odd cases like an `edge_subgraph` of a `restricted_view`
+    can be created.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> H = G.edge_subgraph([(0, 1), (3, 4)])
+    >>> list(H.nodes)
+    [0, 1, 3, 4]
+    >>> list(H.edges)
+    [(0, 1), (3, 4)]
+    """
+    nxf = nx.filters
+    edges = set(edges)
+    nodes = set()
+    for e in edges:
+        nodes.update(e[:2])
+    induced_nodes = nxf.show_nodes(nodes)
+    if G.is_multigraph():
+        if G.is_directed():
+            induced_edges = nxf.show_multidiedges(edges)
+        else:
+            induced_edges = nxf.show_multiedges(edges)
+    else:
+        if G.is_directed():
+            induced_edges = nxf.show_diedges(edges)
+        else:
+            induced_edges = nxf.show_edges(edges)
+    return nx.subgraph_view(G, filter_node=induced_nodes, filter_edge=induced_edges)
+
+
+def restricted_view(G, nodes, edges):
+    """Returns a view of `G` with hidden nodes and edges.
+
+    The resulting subgraph filters out node `nodes` and edges `edges`.
+    Filtered out nodes also filter out any of their edges.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    nodes : iterable
+        An iterable of nodes. Nodes not present in `G` are ignored.
+    edges : iterable
+        An iterable of edges. Edges not present in `G` are ignored.
+
+    Returns
+    -------
+    subgraph : SubGraph View
+        A read-only restricted view of `G` filtering out nodes and edges.
+        Changes to `G` are reflected in the view.
+
+    Notes
+    -----
+    To create a mutable subgraph with its own copies of nodes
+    edges and attributes use `subgraph.copy()` or `Graph(subgraph)`
+
+    If you create a subgraph of a subgraph recursively you may end up
+    with a chain of subgraph views. Such chains can get quite slow
+    for lengths near 15. To avoid long chains, try to make your subgraph
+    based on the original graph.  We do not rule out chains programmatically
+    so that odd cases like an `edge_subgraph` of a `restricted_view`
+    can be created.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> H = nx.restricted_view(G, [0], [(1, 2), (3, 4)])
+    >>> list(H.nodes)
+    [1, 2, 3, 4]
+    >>> list(H.edges)
+    [(2, 3)]
+    """
+    nxf = nx.filters
+    hide_nodes = nxf.hide_nodes(nodes)
+    if G.is_multigraph():
+        if G.is_directed():
+            hide_edges = nxf.hide_multidiedges(edges)
+        else:
+            hide_edges = nxf.hide_multiedges(edges)
+    else:
+        if G.is_directed():
+            hide_edges = nxf.hide_diedges(edges)
+        else:
+            hide_edges = nxf.hide_edges(edges)
+    return nx.subgraph_view(G, filter_node=hide_nodes, filter_edge=hide_edges)
+
+
+def to_directed(graph):
+    """Returns a directed view of the graph `graph`.
+
+    Identical to graph.to_directed(as_view=True)
+    Note that graph.to_directed defaults to `as_view=False`
+    while this function always provides a view.
+    """
+    return graph.to_directed(as_view=True)
+
+
+def to_undirected(graph):
+    """Returns an undirected view of the graph `graph`.
+
+    Identical to graph.to_undirected(as_view=True)
+    Note that graph.to_undirected defaults to `as_view=False`
+    while this function always provides a view.
+    """
+    return graph.to_undirected(as_view=True)
+
+
+def create_empty_copy(G, with_data=True):
+    """Returns a copy of the graph G with all of the edges removed.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    with_data :  bool (default=True)
+       Propagate Graph and Nodes data to the new graph.
+
+    See Also
+    --------
+    empty_graph
+
+    """
+    H = G.__class__()
+    H.add_nodes_from(G.nodes(data=with_data))
+    if with_data:
+        H.graph.update(G.graph)
+    return H
+
+
+@nx._dispatchable(preserve_node_attrs=True, mutates_input=True)
+def set_node_attributes(G, values, name=None):
+    """Sets node attributes from a given value or dictionary of values.
+
+    .. Warning:: The call order of arguments `values` and `name`
+        switched between v1.x & v2.x.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    values : scalar value, dict-like
+        What the node attribute should be set to.  If `values` is
+        not a dictionary, then it is treated as a single attribute value
+        that is then applied to every node in `G`.  This means that if
+        you provide a mutable object, like a list, updates to that object
+        will be reflected in the node attribute for every node.
+        The attribute name will be `name`.
+
+        If `values` is a dict or a dict of dict, it should be keyed
+        by node to either an attribute value or a dict of attribute key/value
+        pairs used to update the node's attributes.
+
+    name : string (optional, default=None)
+        Name of the node attribute to set if values is a scalar.
+
+    Examples
+    --------
+    After computing some property of the nodes of a graph, you may want
+    to assign a node attribute to store the value of that property for
+    each node::
+
+        >>> G = nx.path_graph(3)
+        >>> bb = nx.betweenness_centrality(G)
+        >>> isinstance(bb, dict)
+        True
+        >>> nx.set_node_attributes(G, bb, "betweenness")
+        >>> G.nodes[1]["betweenness"]
+        1.0
+
+    If you provide a list as the second argument, updates to the list
+    will be reflected in the node attribute for each node::
+
+        >>> G = nx.path_graph(3)
+        >>> labels = []
+        >>> nx.set_node_attributes(G, labels, "labels")
+        >>> labels.append("foo")
+        >>> G.nodes[0]["labels"]
+        ['foo']
+        >>> G.nodes[1]["labels"]
+        ['foo']
+        >>> G.nodes[2]["labels"]
+        ['foo']
+
+    If you provide a dictionary of dictionaries as the second argument,
+    the outer dictionary is assumed to be keyed by node to an inner
+    dictionary of node attributes for that node::
+
+        >>> G = nx.path_graph(3)
+        >>> attrs = {0: {"attr1": 20, "attr2": "nothing"}, 1: {"attr2": 3}}
+        >>> nx.set_node_attributes(G, attrs)
+        >>> G.nodes[0]["attr1"]
+        20
+        >>> G.nodes[0]["attr2"]
+        'nothing'
+        >>> G.nodes[1]["attr2"]
+        3
+        >>> G.nodes[2]
+        {}
+
+    Note that if the dictionary contains nodes that are not in `G`, the
+    values are silently ignored::
+
+        >>> G = nx.Graph()
+        >>> G.add_node(0)
+        >>> nx.set_node_attributes(G, {0: "red", 1: "blue"}, name="color")
+        >>> G.nodes[0]["color"]
+        'red'
+        >>> 1 in G.nodes
+        False
+
+    """
+    # Set node attributes based on type of `values`
+    if name is not None:  # `values` must not be a dict of dict
+        try:  # `values` is a dict
+            for n, v in values.items():
+                try:
+                    G.nodes[n][name] = values[n]
+                except KeyError:
+                    pass
+        except AttributeError:  # `values` is a constant
+            for n in G:
+                G.nodes[n][name] = values
+    else:  # `values` must be dict of dict
+        for n, d in values.items():
+            try:
+                G.nodes[n].update(d)
+            except KeyError:
+                pass
+    nx._clear_cache(G)
+
+
+@nx._dispatchable(node_attrs={"name": "default"})
+def get_node_attributes(G, name, default=None):
+    """Get node attributes from graph
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    name : string
+       Attribute name
+
+    default: object (default=None)
+       Default value of the node attribute if there is no value set for that
+       node in graph. If `None` then nodes without this attribute are not
+       included in the returned dict.
+
+    Returns
+    -------
+    Dictionary of attributes keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from([1, 2, 3], color="red")
+    >>> color = nx.get_node_attributes(G, "color")
+    >>> color[1]
+    'red'
+    >>> G.add_node(4)
+    >>> color = nx.get_node_attributes(G, "color", default="yellow")
+    >>> color[4]
+    'yellow'
+    """
+    if default is not None:
+        return {n: d.get(name, default) for n, d in G.nodes.items()}
+    return {n: d[name] for n, d in G.nodes.items() if name in d}
+
+
+@nx._dispatchable(preserve_node_attrs=True, mutates_input=True)
+def remove_node_attributes(G, *attr_names, nbunch=None):
+    """Remove node attributes from all nodes in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    *attr_names : List of Strings
+        The attribute names to remove from the graph.
+
+    nbunch : List of Nodes
+        Remove the node attributes only from the nodes in this list.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from([1, 2, 3], color="blue")
+    >>> nx.get_node_attributes(G, "color")
+    {1: 'blue', 2: 'blue', 3: 'blue'}
+    >>> nx.remove_node_attributes(G, "color")
+    >>> nx.get_node_attributes(G, "color")
+    {}
+    """
+
+    if nbunch is None:
+        nbunch = G.nodes()
+
+    for attr in attr_names:
+        for n, d in G.nodes(data=True):
+            if n in nbunch:
+                try:
+                    del d[attr]
+                except KeyError:
+                    pass
+
+
+@nx._dispatchable(preserve_edge_attrs=True, mutates_input=True)
+def set_edge_attributes(G, values, name=None):
+    """Sets edge attributes from a given value or dictionary of values.
+
+    .. Warning:: The call order of arguments `values` and `name`
+        switched between v1.x & v2.x.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    values : scalar value, dict-like
+        What the edge attribute should be set to.  If `values` is
+        not a dictionary, then it is treated as a single attribute value
+        that is then applied to every edge in `G`.  This means that if
+        you provide a mutable object, like a list, updates to that object
+        will be reflected in the edge attribute for each edge.  The attribute
+        name will be `name`.
+
+        If `values` is a dict or a dict of dict, it should be keyed
+        by edge tuple to either an attribute value or a dict of attribute
+        key/value pairs used to update the edge's attributes.
+        For multigraphs, the edge tuples must be of the form ``(u, v, key)``,
+        where `u` and `v` are nodes and `key` is the edge key.
+        For non-multigraphs, the keys must be tuples of the form ``(u, v)``.
+
+    name : string (optional, default=None)
+        Name of the edge attribute to set if values is a scalar.
+
+    Examples
+    --------
+    After computing some property of the edges of a graph, you may want
+    to assign a edge attribute to store the value of that property for
+    each edge::
+
+        >>> G = nx.path_graph(3)
+        >>> bb = nx.edge_betweenness_centrality(G, normalized=False)
+        >>> nx.set_edge_attributes(G, bb, "betweenness")
+        >>> G.edges[1, 2]["betweenness"]
+        2.0
+
+    If you provide a list as the second argument, updates to the list
+    will be reflected in the edge attribute for each edge::
+
+        >>> labels = []
+        >>> nx.set_edge_attributes(G, labels, "labels")
+        >>> labels.append("foo")
+        >>> G.edges[0, 1]["labels"]
+        ['foo']
+        >>> G.edges[1, 2]["labels"]
+        ['foo']
+
+    If you provide a dictionary of dictionaries as the second argument,
+    the entire dictionary will be used to update edge attributes::
+
+        >>> G = nx.path_graph(3)
+        >>> attrs = {(0, 1): {"attr1": 20, "attr2": "nothing"}, (1, 2): {"attr2": 3}}
+        >>> nx.set_edge_attributes(G, attrs)
+        >>> G[0][1]["attr1"]
+        20
+        >>> G[0][1]["attr2"]
+        'nothing'
+        >>> G[1][2]["attr2"]
+        3
+
+    The attributes of one Graph can be used to set those of another.
+
+        >>> H = nx.path_graph(3)
+        >>> nx.set_edge_attributes(H, G.edges)
+
+    Note that if the dict contains edges that are not in `G`, they are
+    silently ignored::
+
+        >>> G = nx.Graph([(0, 1)])
+        >>> nx.set_edge_attributes(G, {(1, 2): {"weight": 2.0}})
+        >>> (1, 2) in G.edges()
+        False
+
+    For multigraphs, the `values` dict is expected to be keyed by 3-tuples
+    including the edge key::
+
+        >>> MG = nx.MultiGraph()
+        >>> edges = [(0, 1), (0, 1)]
+        >>> MG.add_edges_from(edges)  # Returns list of edge keys
+        [0, 1]
+        >>> attributes = {(0, 1, 0): {"cost": 21}, (0, 1, 1): {"cost": 7}}
+        >>> nx.set_edge_attributes(MG, attributes)
+        >>> MG[0][1][0]["cost"]
+        21
+        >>> MG[0][1][1]["cost"]
+        7
+
+    If MultiGraph attributes are desired for a Graph, you must convert the 3-tuple
+    multiedge to a 2-tuple edge and the last multiedge's attribute value will
+    overwrite the previous values. Continuing from the previous case we get::
+
+        >>> H = nx.path_graph([0, 1, 2])
+        >>> nx.set_edge_attributes(H, {(u, v): ed for u, v, ed in MG.edges.data()})
+        >>> nx.get_edge_attributes(H, "cost")
+        {(0, 1): 7}
+
+    """
+    if name is not None:
+        # `values` does not contain attribute names
+        try:
+            # if `values` is a dict using `.items()` => {edge: value}
+            if G.is_multigraph():
+                for (u, v, key), value in values.items():
+                    try:
+                        G._adj[u][v][key][name] = value
+                    except KeyError:
+                        pass
+            else:
+                for (u, v), value in values.items():
+                    try:
+                        G._adj[u][v][name] = value
+                    except KeyError:
+                        pass
+        except AttributeError:
+            # treat `values` as a constant
+            for u, v, data in G.edges(data=True):
+                data[name] = values
+    else:
+        # `values` consists of doct-of-dict {edge: {attr: value}} shape
+        if G.is_multigraph():
+            for (u, v, key), d in values.items():
+                try:
+                    G._adj[u][v][key].update(d)
+                except KeyError:
+                    pass
+        else:
+            for (u, v), d in values.items():
+                try:
+                    G._adj[u][v].update(d)
+                except KeyError:
+                    pass
+    nx._clear_cache(G)
+
+
+@nx._dispatchable(edge_attrs={"name": "default"})
+def get_edge_attributes(G, name, default=None):
+    """Get edge attributes from graph
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    name : string
+       Attribute name
+
+    default: object (default=None)
+       Default value of the edge attribute if there is no value set for that
+       edge in graph. If `None` then edges without this attribute are not
+       included in the returned dict.
+
+    Returns
+    -------
+    Dictionary of attributes keyed by edge. For (di)graphs, the keys are
+    2-tuples of the form: (u, v). For multi(di)graphs, the keys are 3-tuples of
+    the form: (u, v, key).
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_path(G, [1, 2, 3], color="red")
+    >>> color = nx.get_edge_attributes(G, "color")
+    >>> color[(1, 2)]
+    'red'
+    >>> G.add_edge(3, 4)
+    >>> color = nx.get_edge_attributes(G, "color", default="yellow")
+    >>> color[(3, 4)]
+    'yellow'
+    """
+    if G.is_multigraph():
+        edges = G.edges(keys=True, data=True)
+    else:
+        edges = G.edges(data=True)
+    if default is not None:
+        return {x[:-1]: x[-1].get(name, default) for x in edges}
+    return {x[:-1]: x[-1][name] for x in edges if name in x[-1]}
+
+
+@nx._dispatchable(preserve_edge_attrs=True, mutates_input=True)
+def remove_edge_attributes(G, *attr_names, ebunch=None):
+    """Remove edge attributes from all edges in the graph.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    *attr_names : List of Strings
+        The attribute names to remove from the graph.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> nx.set_edge_attributes(G, {(u, v): u + v for u, v in G.edges()}, name="weight")
+    >>> nx.get_edge_attributes(G, "weight")
+    {(0, 1): 1, (1, 2): 3}
+    >>> remove_edge_attributes(G, "weight")
+    >>> nx.get_edge_attributes(G, "weight")
+    {}
+    """
+    if ebunch is None:
+        ebunch = G.edges(keys=True) if G.is_multigraph() else G.edges()
+
+    for attr in attr_names:
+        edges = (
+            G.edges(keys=True, data=True) if G.is_multigraph() else G.edges(data=True)
+        )
+        for *e, d in edges:
+            if tuple(e) in ebunch:
+                try:
+                    del d[attr]
+                except KeyError:
+                    pass
+
+
+def all_neighbors(graph, node):
+    """Returns all of the neighbors of a node in the graph.
+
+    If the graph is directed returns predecessors as well as successors.
+
+    Parameters
+    ----------
+    graph : NetworkX graph
+        Graph to find neighbors.
+
+    node : node
+        The node whose neighbors will be returned.
+
+    Returns
+    -------
+    neighbors : iterator
+        Iterator of neighbors
+    """
+    if graph.is_directed():
+        values = chain(graph.predecessors(node), graph.successors(node))
+    else:
+        values = graph.neighbors(node)
+    return values
+
+
+def non_neighbors(graph, node):
+    """Returns the non-neighbors of the node in the graph.
+
+    Parameters
+    ----------
+    graph : NetworkX graph
+        Graph to find neighbors.
+
+    node : node
+        The node whose neighbors will be returned.
+
+    Returns
+    -------
+    non_neighbors : set
+        Set of nodes in the graph that are not neighbors of the node.
+    """
+    return graph._adj.keys() - graph._adj[node].keys() - {node}
+
+
+def non_edges(graph):
+    """Returns the nonexistent edges in the graph.
+
+    Parameters
+    ----------
+    graph : NetworkX graph.
+        Graph to find nonexistent edges.
+
+    Returns
+    -------
+    non_edges : iterator
+        Iterator of edges that are not in the graph.
+    """
+    if graph.is_directed():
+        for u in graph:
+            for v in non_neighbors(graph, u):
+                yield (u, v)
+    else:
+        nodes = set(graph)
+        while nodes:
+            u = nodes.pop()
+            for v in nodes - set(graph[u]):
+                yield (u, v)
+
+
+@not_implemented_for("directed")
+def common_neighbors(G, u, v):
+    """Returns the common neighbors of two nodes in a graph.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX undirected graph.
+
+    u, v : nodes
+        Nodes in the graph.
+
+    Returns
+    -------
+    cnbors : set
+        Set of common neighbors of u and v in the graph.
+
+    Raises
+    ------
+    NetworkXError
+        If u or v is not a node in the graph.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(5)
+    >>> sorted(nx.common_neighbors(G, 0, 1))
+    [2, 3, 4]
+    """
+    if u not in G:
+        raise nx.NetworkXError("u is not in the graph.")
+    if v not in G:
+        raise nx.NetworkXError("v is not in the graph.")
+
+    return G._adj[u].keys() & G._adj[v].keys() - {u, v}
+
+
+@nx._dispatchable(preserve_edge_attrs=True)
+def is_weighted(G, edge=None, weight="weight"):
+    """Returns True if `G` has weighted edges.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+
+    edge : tuple, optional
+        A 2-tuple specifying the only edge in `G` that will be tested. If
+        None, then every edge in `G` is tested.
+
+    weight: string, optional
+        The attribute name used to query for edge weights.
+
+    Returns
+    -------
+    bool
+        A boolean signifying if `G`, or the specified edge, is weighted.
+
+    Raises
+    ------
+    NetworkXError
+        If the specified edge does not exist.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.is_weighted(G)
+    False
+    >>> nx.is_weighted(G, (2, 3))
+    False
+
+    >>> G = nx.DiGraph()
+    >>> G.add_edge(1, 2, weight=1)
+    >>> nx.is_weighted(G)
+    True
+
+    """
+    if edge is not None:
+        data = G.get_edge_data(*edge)
+        if data is None:
+            msg = f"Edge {edge!r} does not exist."
+            raise nx.NetworkXError(msg)
+        return weight in data
+
+    if is_empty(G):
+        # Special handling required since: all([]) == True
+        return False
+
+    return all(weight in data for u, v, data in G.edges(data=True))
+
+
+@nx._dispatchable(edge_attrs="weight")
+def is_negatively_weighted(G, edge=None, weight="weight"):
+    """Returns True if `G` has negatively weighted edges.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+
+    edge : tuple, optional
+        A 2-tuple specifying the only edge in `G` that will be tested. If
+        None, then every edge in `G` is tested.
+
+    weight: string, optional
+        The attribute name used to query for edge weights.
+
+    Returns
+    -------
+    bool
+        A boolean signifying if `G`, or the specified edge, is negatively
+        weighted.
+
+    Raises
+    ------
+    NetworkXError
+        If the specified edge does not exist.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_edges_from([(1, 3), (2, 4), (2, 6)])
+    >>> G.add_edge(1, 2, weight=4)
+    >>> nx.is_negatively_weighted(G, (1, 2))
+    False
+    >>> G[2][4]["weight"] = -2
+    >>> nx.is_negatively_weighted(G)
+    True
+    >>> G = nx.DiGraph()
+    >>> edges = [("0", "3", 3), ("0", "1", -5), ("1", "0", -2)]
+    >>> G.add_weighted_edges_from(edges)
+    >>> nx.is_negatively_weighted(G)
+    True
+
+    """
+    if edge is not None:
+        data = G.get_edge_data(*edge)
+        if data is None:
+            msg = f"Edge {edge!r} does not exist."
+            raise nx.NetworkXError(msg)
+        return weight in data and data[weight] < 0
+
+    return any(weight in data and data[weight] < 0 for u, v, data in G.edges(data=True))
+
+
+@nx._dispatchable
+def is_empty(G):
+    """Returns True if `G` has no edges.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+
+    Returns
+    -------
+    bool
+        True if `G` has no edges, and False otherwise.
+
+    Notes
+    -----
+    An empty graph can have nodes but not edges. The empty graph with zero
+    nodes is known as the null graph. This is an $O(n)$ operation where n
+    is the number of nodes in the graph.
+
+    """
+    return not any(G._adj.values())
+
+
+def nodes_with_selfloops(G):
+    """Returns an iterator over nodes with self loops.
+
+    A node with a self loop has an edge with both ends adjacent
+    to that node.
+
+    Returns
+    -------
+    nodelist : iterator
+        A iterator over nodes with self loops.
+
+    See Also
+    --------
+    selfloop_edges, number_of_selfloops
+
+    Examples
+    --------
+    >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+    >>> G.add_edge(1, 1)
+    >>> G.add_edge(1, 2)
+    >>> list(nx.nodes_with_selfloops(G))
+    [1]
+
+    """
+    return (n for n, nbrs in G._adj.items() if n in nbrs)
+
+
+def selfloop_edges(G, data=False, keys=False, default=None):
+    """Returns an iterator over selfloop edges.
+
+    A selfloop edge has the same node at both ends.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+    data : string or bool, optional (default=False)
+        Return selfloop edges as two tuples (u, v) (data=False)
+        or three-tuples (u, v, datadict) (data=True)
+        or three-tuples (u, v, datavalue) (data='attrname')
+    keys : bool, optional (default=False)
+        If True, return edge keys with each edge.
+    default : value, optional (default=None)
+        Value used for edges that don't have the requested attribute.
+        Only relevant if data is not True or False.
+
+    Returns
+    -------
+    edgeiter : iterator over edge tuples
+        An iterator over all selfloop edges.
+
+    See Also
+    --------
+    nodes_with_selfloops, number_of_selfloops
+
+    Examples
+    --------
+    >>> G = nx.MultiGraph()  # or Graph, DiGraph, MultiDiGraph, etc
+    >>> ekey = G.add_edge(1, 1)
+    >>> ekey = G.add_edge(1, 2)
+    >>> list(nx.selfloop_edges(G))
+    [(1, 1)]
+    >>> list(nx.selfloop_edges(G, data=True))
+    [(1, 1, {})]
+    >>> list(nx.selfloop_edges(G, keys=True))
+    [(1, 1, 0)]
+    >>> list(nx.selfloop_edges(G, keys=True, data=True))
+    [(1, 1, 0, {})]
+    """
+    if data is True:
+        if G.is_multigraph():
+            if keys is True:
+                return (
+                    (n, n, k, d)
+                    for n, nbrs in G._adj.items()
+                    if n in nbrs
+                    for k, d in nbrs[n].items()
+                )
+            else:
+                return (
+                    (n, n, d)
+                    for n, nbrs in G._adj.items()
+                    if n in nbrs
+                    for d in nbrs[n].values()
+                )
+        else:
+            return ((n, n, nbrs[n]) for n, nbrs in G._adj.items() if n in nbrs)
+    elif data is not False:
+        if G.is_multigraph():
+            if keys is True:
+                return (
+                    (n, n, k, d.get(data, default))
+                    for n, nbrs in G._adj.items()
+                    if n in nbrs
+                    for k, d in nbrs[n].items()
+                )
+            else:
+                return (
+                    (n, n, d.get(data, default))
+                    for n, nbrs in G._adj.items()
+                    if n in nbrs
+                    for d in nbrs[n].values()
+                )
+        else:
+            return (
+                (n, n, nbrs[n].get(data, default))
+                for n, nbrs in G._adj.items()
+                if n in nbrs
+            )
+    else:
+        if G.is_multigraph():
+            if keys is True:
+                return (
+                    (n, n, k)
+                    for n, nbrs in G._adj.items()
+                    if n in nbrs
+                    for k in nbrs[n]
+                )
+            else:
+                return (
+                    (n, n)
+                    for n, nbrs in G._adj.items()
+                    if n in nbrs
+                    for i in range(len(nbrs[n]))  # for easy edge removal (#4068)
+                )
+        else:
+            return ((n, n) for n, nbrs in G._adj.items() if n in nbrs)
+
+
+@nx._dispatchable
+def number_of_selfloops(G):
+    """Returns the number of selfloop edges.
+
+    A selfloop edge has the same node at both ends.
+
+    Returns
+    -------
+    nloops : int
+        The number of selfloops.
+
+    See Also
+    --------
+    nodes_with_selfloops, selfloop_edges
+
+    Examples
+    --------
+    >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+    >>> G.add_edge(1, 1)
+    >>> G.add_edge(1, 2)
+    >>> nx.number_of_selfloops(G)
+    1
+    """
+    return sum(1 for _ in nx.selfloop_edges(G))
+
+
+def is_path(G, path):
+    """Returns whether or not the specified path exists.
+
+    For it to return True, every node on the path must exist and
+    each consecutive pair must be connected via one or more edges.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+
+    path : list
+        A list of nodes which defines the path to traverse
+
+    Returns
+    -------
+    bool
+        True if `path` is a valid path in `G`
+
+    """
+    try:
+        return all(nbr in G._adj[node] for node, nbr in nx.utils.pairwise(path))
+    except (KeyError, TypeError):
+        return False
+
+
+def path_weight(G, path, weight):
+    """Returns total cost associated with specified path and weight
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph.
+
+    path: list
+        A list of node labels which defines the path to traverse
+
+    weight: string
+        A string indicating which edge attribute to use for path cost
+
+    Returns
+    -------
+    cost: int or float
+        An integer or a float representing the total cost with respect to the
+        specified weight of the specified path
+
+    Raises
+    ------
+    NetworkXNoPath
+        If the specified edge does not exist.
+    """
+    multigraph = G.is_multigraph()
+    cost = 0
+
+    if not nx.is_path(G, path):
+        raise nx.NetworkXNoPath("path does not exist")
+    for node, nbr in nx.utils.pairwise(path):
+        if multigraph:
+            cost += min(v[weight] for v in G._adj[node][nbr].values())
+        else:
+            cost += G._adj[node][nbr][weight]
+    return cost
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/graph.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6adcf0d0a33cc2487ca15141ba8da2a7a1729c1
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/graph.py
@@ -0,0 +1,2071 @@
+"""Base class for undirected graphs.
+
+The Graph class allows any hashable object as a node
+and can associate key/value attribute pairs with each undirected edge.
+
+Self-loops are allowed but multiple edges are not (see MultiGraph).
+
+For directed graphs see DiGraph and MultiDiGraph.
+"""
+
+from copy import deepcopy
+from functools import cached_property
+
+import networkx as nx
+from networkx import convert
+from networkx.classes.coreviews import AdjacencyView
+from networkx.classes.reportviews import DegreeView, EdgeView, NodeView
+from networkx.exception import NetworkXError
+
+__all__ = ["Graph"]
+
+
+class _CachedPropertyResetterAdj:
+    """Data Descriptor class for _adj that resets ``adj`` cached_property when needed
+
+    This assumes that the ``cached_property`` ``G.adj`` should be reset whenever
+    ``G._adj`` is set to a new value.
+
+    This object sits on a class and ensures that any instance of that
+    class clears its cached property "adj" whenever the underlying
+    instance attribute "_adj" is set to a new object. It only affects
+    the set process of the obj._adj attribute. All get/del operations
+    act as they normally would.
+
+    For info on Data Descriptors see: https://docs.python.org/3/howto/descriptor.html
+    """
+
+    def __set__(self, obj, value):
+        od = obj.__dict__
+        od["_adj"] = value
+        # reset cached properties
+        props = ["adj", "edges", "degree"]
+        for prop in props:
+            if prop in od:
+                del od[prop]
+
+
+class _CachedPropertyResetterNode:
+    """Data Descriptor class for _node that resets ``nodes`` cached_property when needed
+
+    This assumes that the ``cached_property`` ``G.node`` should be reset whenever
+    ``G._node`` is set to a new value.
+
+    This object sits on a class and ensures that any instance of that
+    class clears its cached property "nodes" whenever the underlying
+    instance attribute "_node" is set to a new object. It only affects
+    the set process of the obj._adj attribute. All get/del operations
+    act as they normally would.
+
+    For info on Data Descriptors see: https://docs.python.org/3/howto/descriptor.html
+    """
+
+    def __set__(self, obj, value):
+        od = obj.__dict__
+        od["_node"] = value
+        # reset cached properties
+        if "nodes" in od:
+            del od["nodes"]
+
+
+class Graph:
+    """
+    Base class for undirected graphs.
+
+    A Graph stores nodes and edges with optional data, or attributes.
+
+    Graphs hold undirected edges.  Self loops are allowed but multiple
+    (parallel) edges are not.
+
+    Nodes can be arbitrary (hashable) Python objects with optional
+    key/value attributes, except that `None` is not allowed as a node.
+
+    Edges are represented as links between nodes with optional
+    key/value attributes.
+
+    Parameters
+    ----------
+    incoming_graph_data : input graph (optional, default: None)
+        Data to initialize graph. If None (default) an empty
+        graph is created.  The data can be any format that is supported
+        by the to_networkx_graph() function, currently including edge list,
+        dict of dicts, dict of lists, NetworkX graph, 2D NumPy array, SciPy
+        sparse matrix, or PyGraphviz graph.
+
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to graph as key=value pairs.
+
+    See Also
+    --------
+    DiGraph
+    MultiGraph
+    MultiDiGraph
+
+    Examples
+    --------
+    Create an empty graph structure (a "null graph") with no nodes and
+    no edges.
+
+    >>> G = nx.Graph()
+
+    G can be grown in several ways.
+
+    **Nodes:**
+
+    Add one node at a time:
+
+    >>> G.add_node(1)
+
+    Add the nodes from any container (a list, dict, set or
+    even the lines from a file or the nodes from another graph).
+
+    >>> G.add_nodes_from([2, 3])
+    >>> G.add_nodes_from(range(100, 110))
+    >>> H = nx.path_graph(10)
+    >>> G.add_nodes_from(H)
+
+    In addition to strings and integers any hashable Python object
+    (except None) can represent a node, e.g. a customized node object,
+    or even another Graph.
+
+    >>> G.add_node(H)
+
+    **Edges:**
+
+    G can also be grown by adding edges.
+
+    Add one edge,
+
+    >>> G.add_edge(1, 2)
+
+    a list of edges,
+
+    >>> G.add_edges_from([(1, 2), (1, 3)])
+
+    or a collection of edges,
+
+    >>> G.add_edges_from(H.edges)
+
+    If some edges connect nodes not yet in the graph, the nodes
+    are added automatically.  There are no errors when adding
+    nodes or edges that already exist.
+
+    **Attributes:**
+
+    Each graph, node, and edge can hold key/value attribute pairs
+    in an associated attribute dictionary (the keys must be hashable).
+    By default these are empty, but can be added or changed using
+    add_edge, add_node or direct manipulation of the attribute
+    dictionaries named graph, node and edge respectively.
+
+    >>> G = nx.Graph(day="Friday")
+    >>> G.graph
+    {'day': 'Friday'}
+
+    Add node attributes using add_node(), add_nodes_from() or G.nodes
+
+    >>> G.add_node(1, time="5pm")
+    >>> G.add_nodes_from([3], time="2pm")
+    >>> G.nodes[1]
+    {'time': '5pm'}
+    >>> G.nodes[1]["room"] = 714  # node must exist already to use G.nodes
+    >>> del G.nodes[1]["room"]  # remove attribute
+    >>> list(G.nodes(data=True))
+    [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
+
+    Add edge attributes using add_edge(), add_edges_from(), subscript
+    notation, or G.edges.
+
+    >>> G.add_edge(1, 2, weight=4.7)
+    >>> G.add_edges_from([(3, 4), (4, 5)], color="red")
+    >>> G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
+    >>> G[1][2]["weight"] = 4.7
+    >>> G.edges[1, 2]["weight"] = 4
+
+    Warning: we protect the graph data structure by making `G.edges` a
+    read-only dict-like structure. However, you can assign to attributes
+    in e.g. `G.edges[1, 2]`. Thus, use 2 sets of brackets to add/change
+    data attributes: `G.edges[1, 2]['weight'] = 4`
+    (For multigraphs: `MG.edges[u, v, key][name] = value`).
+
+    **Shortcuts:**
+
+    Many common graph features allow python syntax to speed reporting.
+
+    >>> 1 in G  # check if node in graph
+    True
+    >>> [n for n in G if n < 3]  # iterate through nodes
+    [1, 2]
+    >>> len(G)  # number of nodes in graph
+    5
+
+    Often the best way to traverse all edges of a graph is via the neighbors.
+    The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`
+
+    >>> for n, nbrsdict in G.adjacency():
+    ...     for nbr, eattr in nbrsdict.items():
+    ...         if "weight" in eattr:
+    ...             # Do something useful with the edges
+    ...             pass
+
+    But the edges() method is often more convenient:
+
+    >>> for u, v, weight in G.edges.data("weight"):
+    ...     if weight is not None:
+    ...         # Do something useful with the edges
+    ...         pass
+
+    **Reporting:**
+
+    Simple graph information is obtained using object-attributes and methods.
+    Reporting typically provides views instead of containers to reduce memory
+    usage. The views update as the graph is updated similarly to dict-views.
+    The objects `nodes`, `edges` and `adj` provide access to data attributes
+    via lookup (e.g. `nodes[n]`, `edges[u, v]`, `adj[u][v]`) and iteration
+    (e.g. `nodes.items()`, `nodes.data('color')`,
+    `nodes.data('color', default='blue')` and similarly for `edges`)
+    Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
+
+    For details on these and other miscellaneous methods, see below.
+
+    **Subclasses (Advanced):**
+
+    The Graph class uses a dict-of-dict-of-dict data structure.
+    The outer dict (node_dict) holds adjacency information keyed by node.
+    The next dict (adjlist_dict) represents the adjacency information and holds
+    edge data keyed by neighbor.  The inner dict (edge_attr_dict) represents
+    the edge data and holds edge attribute values keyed by attribute names.
+
+    Each of these three dicts can be replaced in a subclass by a user defined
+    dict-like object. In general, the dict-like features should be
+    maintained but extra features can be added. To replace one of the
+    dicts create a new graph class by changing the class(!) variable
+    holding the factory for that dict-like structure.
+
+    node_dict_factory : function, (default: dict)
+        Factory function to be used to create the dict containing node
+        attributes, keyed by node id.
+        It should require no arguments and return a dict-like object
+
+    node_attr_dict_factory: function, (default: dict)
+        Factory function to be used to create the node attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object
+
+    adjlist_outer_dict_factory : function, (default: dict)
+        Factory function to be used to create the outer-most dict
+        in the data structure that holds adjacency info keyed by node.
+        It should require no arguments and return a dict-like object.
+
+    adjlist_inner_dict_factory : function, (default: dict)
+        Factory function to be used to create the adjacency list
+        dict which holds edge data keyed by neighbor.
+        It should require no arguments and return a dict-like object
+
+    edge_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the edge attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    graph_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the graph attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    Typically, if your extension doesn't impact the data structure all
+    methods will inherit without issue except: `to_directed/to_undirected`.
+    By default these methods create a DiGraph/Graph class and you probably
+    want them to create your extension of a DiGraph/Graph. To facilitate
+    this we define two class variables that you can set in your subclass.
+
+    to_directed_class : callable, (default: DiGraph or MultiDiGraph)
+        Class to create a new graph structure in the `to_directed` method.
+        If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
+
+    to_undirected_class : callable, (default: Graph or MultiGraph)
+        Class to create a new graph structure in the `to_undirected` method.
+        If `None`, a NetworkX class (Graph or MultiGraph) is used.
+
+    **Subclassing Example**
+
+    Create a low memory graph class that effectively disallows edge
+    attributes by using a single attribute dict for all edges.
+    This reduces the memory used, but you lose edge attributes.
+
+    >>> class ThinGraph(nx.Graph):
+    ...     all_edge_dict = {"weight": 1}
+    ...
+    ...     def single_edge_dict(self):
+    ...         return self.all_edge_dict
+    ...
+    ...     edge_attr_dict_factory = single_edge_dict
+    >>> G = ThinGraph()
+    >>> G.add_edge(2, 1)
+    >>> G[2][1]
+    {'weight': 1}
+    >>> G.add_edge(2, 2)
+    >>> G[2][1] is G[2][2]
+    True
+    """
+
+    __networkx_backend__ = "networkx"
+
+    _adj = _CachedPropertyResetterAdj()
+    _node = _CachedPropertyResetterNode()
+
+    node_dict_factory = dict
+    node_attr_dict_factory = dict
+    adjlist_outer_dict_factory = dict
+    adjlist_inner_dict_factory = dict
+    edge_attr_dict_factory = dict
+    graph_attr_dict_factory = dict
+
+    def to_directed_class(self):
+        """Returns the class to use for empty directed copies.
+
+        If you subclass the base classes, use this to designate
+        what directed class to use for `to_directed()` copies.
+        """
+        return nx.DiGraph
+
+    def to_undirected_class(self):
+        """Returns the class to use for empty undirected copies.
+
+        If you subclass the base classes, use this to designate
+        what directed class to use for `to_directed()` copies.
+        """
+        return Graph
+
+    def __init__(self, incoming_graph_data=None, **attr):
+        """Initialize a graph with edges, name, or graph attributes.
+
+        Parameters
+        ----------
+        incoming_graph_data : input graph (optional, default: None)
+            Data to initialize graph. If None (default) an empty
+            graph is created.  The data can be an edge list, or any
+            NetworkX graph object.  If the corresponding optional Python
+            packages are installed the data can also be a 2D NumPy array, a
+            SciPy sparse array, or a PyGraphviz graph.
+
+        attr : keyword arguments, optional (default= no attributes)
+            Attributes to add to graph as key=value pairs.
+
+        See Also
+        --------
+        convert
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G = nx.Graph(name="my graph")
+        >>> e = [(1, 2), (2, 3), (3, 4)]  # list of edges
+        >>> G = nx.Graph(e)
+
+        Arbitrary graph attribute pairs (key=value) may be assigned
+
+        >>> G = nx.Graph(e, day="Friday")
+        >>> G.graph
+        {'day': 'Friday'}
+
+        """
+        self.graph = self.graph_attr_dict_factory()  # dictionary for graph attributes
+        self._node = self.node_dict_factory()  # empty node attribute dict
+        self._adj = self.adjlist_outer_dict_factory()  # empty adjacency dict
+        self.__networkx_cache__ = {}
+        # attempt to load graph with data
+        if incoming_graph_data is not None:
+            convert.to_networkx_graph(incoming_graph_data, create_using=self)
+        # load graph attributes (must be after convert)
+        self.graph.update(attr)
+
+    @cached_property
+    def adj(self):
+        """Graph adjacency object holding the neighbors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edge-data-dict.  So `G.adj[3][2]['color'] = 'blue'` sets
+        the color of the edge `(3, 2)` to `"blue"`.
+
+        Iterating over G.adj behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.adj[n].items():`.
+
+        The neighbor information is also provided by subscripting the graph.
+        So `for nbr, foovalue in G[node].data('foo', default=1):` works.
+
+        For directed graphs, `G.adj` holds outgoing (successor) info.
+        """
+        return AdjacencyView(self._adj)
+
+    @property
+    def name(self):
+        """String identifier of the graph.
+
+        This graph attribute appears in the attribute dict G.graph
+        keyed by the string `"name"`. as well as an attribute (technically
+        a property) `G.name`. This is entirely user controlled.
+        """
+        return self.graph.get("name", "")
+
+    @name.setter
+    def name(self, s):
+        self.graph["name"] = s
+        nx._clear_cache(self)
+
+    def __str__(self):
+        """Returns a short summary of the graph.
+
+        Returns
+        -------
+        info : string
+            Graph information including the graph name (if any), graph type, and the
+            number of nodes and edges.
+
+        Examples
+        --------
+        >>> G = nx.Graph(name="foo")
+        >>> str(G)
+        "Graph named 'foo' with 0 nodes and 0 edges"
+
+        >>> G = nx.path_graph(3)
+        >>> str(G)
+        'Graph with 3 nodes and 2 edges'
+
+        """
+        return "".join(
+            [
+                type(self).__name__,
+                f" named {self.name!r}" if self.name else "",
+                f" with {self.number_of_nodes()} nodes and {self.number_of_edges()} edges",
+            ]
+        )
+
+    def __iter__(self):
+        """Iterate over the nodes. Use: 'for n in G'.
+
+        Returns
+        -------
+        niter : iterator
+            An iterator over all nodes in the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> [n for n in G]
+        [0, 1, 2, 3]
+        >>> list(G)
+        [0, 1, 2, 3]
+        """
+        return iter(self._node)
+
+    def __contains__(self, n):
+        """Returns True if n is a node, False otherwise. Use: 'n in G'.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> 1 in G
+        True
+        """
+        try:
+            return n in self._node
+        except TypeError:
+            return False
+
+    def __len__(self):
+        """Returns the number of nodes in the graph. Use: 'len(G)'.
+
+        Returns
+        -------
+        nnodes : int
+            The number of nodes in the graph.
+
+        See Also
+        --------
+        number_of_nodes: identical method
+        order: identical method
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> len(G)
+        4
+
+        """
+        return len(self._node)
+
+    def __getitem__(self, n):
+        """Returns a dict of neighbors of node n.  Use: 'G[n]'.
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph.
+
+        Returns
+        -------
+        adj_dict : dictionary
+           The adjacency dictionary for nodes connected to n.
+
+        Notes
+        -----
+        G[n] is the same as G.adj[n] and similar to G.neighbors(n)
+        (which is an iterator over G.adj[n])
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G[0]
+        AtlasView({1: {}})
+        """
+        return self.adj[n]
+
+    def add_node(self, node_for_adding, **attr):
+        """Add a single node `node_for_adding` and update node attributes.
+
+        Parameters
+        ----------
+        node_for_adding : node
+            A node can be any hashable Python object except None.
+        attr : keyword arguments, optional
+            Set or change node attributes using key=value.
+
+        See Also
+        --------
+        add_nodes_from
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_node(1)
+        >>> G.add_node("Hello")
+        >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
+        >>> G.add_node(K3)
+        >>> G.number_of_nodes()
+        3
+
+        Use keywords set/change node attributes:
+
+        >>> G.add_node(1, size=10)
+        >>> G.add_node(3, weight=0.4, UTM=("13S", 382871, 3972649))
+
+        Notes
+        -----
+        A hashable object is one that can be used as a key in a Python
+        dictionary. This includes strings, numbers, tuples of strings
+        and numbers, etc.
+
+        On many platforms hashable items also include mutables such as
+        NetworkX Graphs, though one should be careful that the hash
+        doesn't change on mutables.
+        """
+        if node_for_adding not in self._node:
+            if node_for_adding is None:
+                raise ValueError("None cannot be a node")
+            self._adj[node_for_adding] = self.adjlist_inner_dict_factory()
+            attr_dict = self._node[node_for_adding] = self.node_attr_dict_factory()
+            attr_dict.update(attr)
+        else:  # update attr even if node already exists
+            self._node[node_for_adding].update(attr)
+        nx._clear_cache(self)
+
+    def add_nodes_from(self, nodes_for_adding, **attr):
+        """Add multiple nodes.
+
+        Parameters
+        ----------
+        nodes_for_adding : iterable container
+            A container of nodes (list, dict, set, etc.).
+            OR
+            A container of (node, attribute dict) tuples.
+            Node attributes are updated using the attribute dict.
+        attr : keyword arguments, optional (default= no attributes)
+            Update attributes for all nodes in nodes.
+            Node attributes specified in nodes as a tuple take
+            precedence over attributes specified via keyword arguments.
+
+        See Also
+        --------
+        add_node
+
+        Notes
+        -----
+        When adding nodes from an iterator over the graph you are changing,
+        a `RuntimeError` can be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_nodes)`, and pass this
+        object to `G.add_nodes_from`.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_nodes_from("Hello")
+        >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
+        >>> G.add_nodes_from(K3)
+        >>> sorted(G.nodes(), key=str)
+        [0, 1, 2, 'H', 'e', 'l', 'o']
+
+        Use keywords to update specific node attributes for every node.
+
+        >>> G.add_nodes_from([1, 2], size=10)
+        >>> G.add_nodes_from([3, 4], weight=0.4)
+
+        Use (node, attrdict) tuples to update attributes for specific nodes.
+
+        >>> G.add_nodes_from([(1, dict(size=11)), (2, {"color": "blue"})])
+        >>> G.nodes[1]["size"]
+        11
+        >>> H = nx.Graph()
+        >>> H.add_nodes_from(G.nodes(data=True))
+        >>> H.nodes[1]["size"]
+        11
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.Graph([(0, 1), (1, 2), (3, 4)])
+        >>> # wrong way - will raise RuntimeError
+        >>> # G.add_nodes_from(n + 1 for n in G.nodes)
+        >>> # correct way
+        >>> G.add_nodes_from(list(n + 1 for n in G.nodes))
+        """
+        for n in nodes_for_adding:
+            try:
+                newnode = n not in self._node
+                newdict = attr
+            except TypeError:
+                n, ndict = n
+                newnode = n not in self._node
+                newdict = attr.copy()
+                newdict.update(ndict)
+            if newnode:
+                if n is None:
+                    raise ValueError("None cannot be a node")
+                self._adj[n] = self.adjlist_inner_dict_factory()
+                self._node[n] = self.node_attr_dict_factory()
+            self._node[n].update(newdict)
+        nx._clear_cache(self)
+
+    def remove_node(self, n):
+        """Remove node n.
+
+        Removes the node n and all adjacent edges.
+        Attempting to remove a nonexistent node will raise an exception.
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph
+
+        Raises
+        ------
+        NetworkXError
+           If n is not in the graph.
+
+        See Also
+        --------
+        remove_nodes_from
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> list(G.edges)
+        [(0, 1), (1, 2)]
+        >>> G.remove_node(1)
+        >>> list(G.edges)
+        []
+
+        """
+        adj = self._adj
+        try:
+            nbrs = list(adj[n])  # list handles self-loops (allows mutation)
+            del self._node[n]
+        except KeyError as err:  # NetworkXError if n not in self
+            raise NetworkXError(f"The node {n} is not in the graph.") from err
+        for u in nbrs:
+            del adj[u][n]  # remove all edges n-u in graph
+        del adj[n]  # now remove node
+        nx._clear_cache(self)
+
+    def remove_nodes_from(self, nodes):
+        """Remove multiple nodes.
+
+        Parameters
+        ----------
+        nodes : iterable container
+            A container of nodes (list, dict, set, etc.).  If a node
+            in the container is not in the graph it is silently
+            ignored.
+
+        See Also
+        --------
+        remove_node
+
+        Notes
+        -----
+        When removing nodes from an iterator over the graph you are changing,
+        a `RuntimeError` will be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_nodes)`, and pass this
+        object to `G.remove_nodes_from`.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> e = list(G.nodes)
+        >>> e
+        [0, 1, 2]
+        >>> G.remove_nodes_from(e)
+        >>> list(G.nodes)
+        []
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.Graph([(0, 1), (1, 2), (3, 4)])
+        >>> # this command will fail, as the graph's dict is modified during iteration
+        >>> # G.remove_nodes_from(n for n in G.nodes if n < 2)
+        >>> # this command will work, since the dictionary underlying graph is not modified
+        >>> G.remove_nodes_from(list(n for n in G.nodes if n < 2))
+        """
+        adj = self._adj
+        for n in nodes:
+            try:
+                del self._node[n]
+                for u in list(adj[n]):  # list handles self-loops
+                    del adj[u][n]  # (allows mutation of dict in loop)
+                del adj[n]
+            except KeyError:
+                pass
+        nx._clear_cache(self)
+
+    @cached_property
+    def nodes(self):
+        """A NodeView of the Graph as G.nodes or G.nodes().
+
+        Can be used as `G.nodes` for data lookup and for set-like operations.
+        Can also be used as `G.nodes(data='color', default=None)` to return a
+        NodeDataView which reports specific node data but no set operations.
+        It presents a dict-like interface as well with `G.nodes.items()`
+        iterating over `(node, nodedata)` 2-tuples and `G.nodes[3]['foo']`
+        providing the value of the `foo` attribute for node `3`. In addition,
+        a view `G.nodes.data('foo')` provides a dict-like interface to the
+        `foo` attribute of each node. `G.nodes.data('foo', default=1)`
+        provides a default for nodes that do not have attribute `foo`.
+
+        Parameters
+        ----------
+        data : string or bool, optional (default=False)
+            The node attribute returned in 2-tuple (n, ddict[data]).
+            If True, return entire node attribute dict as (n, ddict).
+            If False, return just the nodes n.
+
+        default : value, optional (default=None)
+            Value used for nodes that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        NodeView
+            Allows set-like operations over the nodes as well as node
+            attribute dict lookup and calling to get a NodeDataView.
+            A NodeDataView iterates over `(n, data)` and has no set operations.
+            A NodeView iterates over `n` and includes set operations.
+
+            When called, if data is False, an iterator over nodes.
+            Otherwise an iterator of 2-tuples (node, attribute value)
+            where the attribute is specified in `data`.
+            If data is True then the attribute becomes the
+            entire data dictionary.
+
+        Notes
+        -----
+        If your node data is not needed, it is simpler and equivalent
+        to use the expression ``for n in G``, or ``list(G)``.
+
+        Examples
+        --------
+        There are two simple ways of getting a list of all nodes in the graph:
+
+        >>> G = nx.path_graph(3)
+        >>> list(G.nodes)
+        [0, 1, 2]
+        >>> list(G)
+        [0, 1, 2]
+
+        To get the node data along with the nodes:
+
+        >>> G.add_node(1, time="5pm")
+        >>> G.nodes[0]["foo"] = "bar"
+        >>> list(G.nodes(data=True))
+        [(0, {'foo': 'bar'}), (1, {'time': '5pm'}), (2, {})]
+        >>> list(G.nodes.data())
+        [(0, {'foo': 'bar'}), (1, {'time': '5pm'}), (2, {})]
+
+        >>> list(G.nodes(data="foo"))
+        [(0, 'bar'), (1, None), (2, None)]
+        >>> list(G.nodes.data("foo"))
+        [(0, 'bar'), (1, None), (2, None)]
+
+        >>> list(G.nodes(data="time"))
+        [(0, None), (1, '5pm'), (2, None)]
+        >>> list(G.nodes.data("time"))
+        [(0, None), (1, '5pm'), (2, None)]
+
+        >>> list(G.nodes(data="time", default="Not Available"))
+        [(0, 'Not Available'), (1, '5pm'), (2, 'Not Available')]
+        >>> list(G.nodes.data("time", default="Not Available"))
+        [(0, 'Not Available'), (1, '5pm'), (2, 'Not Available')]
+
+        If some of your nodes have an attribute and the rest are assumed
+        to have a default attribute value you can create a dictionary
+        from node/attribute pairs using the `default` keyword argument
+        to guarantee the value is never None::
+
+            >>> G = nx.Graph()
+            >>> G.add_node(0)
+            >>> G.add_node(1, weight=2)
+            >>> G.add_node(2, weight=3)
+            >>> dict(G.nodes(data="weight", default=1))
+            {0: 1, 1: 2, 2: 3}
+
+        """
+        return NodeView(self)
+
+    def number_of_nodes(self):
+        """Returns the number of nodes in the graph.
+
+        Returns
+        -------
+        nnodes : int
+            The number of nodes in the graph.
+
+        See Also
+        --------
+        order: identical method
+        __len__: identical method
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.number_of_nodes()
+        3
+        """
+        return len(self._node)
+
+    def order(self):
+        """Returns the number of nodes in the graph.
+
+        Returns
+        -------
+        nnodes : int
+            The number of nodes in the graph.
+
+        See Also
+        --------
+        number_of_nodes: identical method
+        __len__: identical method
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.order()
+        3
+        """
+        return len(self._node)
+
+    def has_node(self, n):
+        """Returns True if the graph contains the node n.
+
+        Identical to `n in G`
+
+        Parameters
+        ----------
+        n : node
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.has_node(0)
+        True
+
+        It is more readable and simpler to use
+
+        >>> 0 in G
+        True
+
+        """
+        try:
+            return n in self._node
+        except TypeError:
+            return False
+
+    def add_edge(self, u_of_edge, v_of_edge, **attr):
+        """Add an edge between u and v.
+
+        The nodes u and v will be automatically added if they are
+        not already in the graph.
+
+        Edge attributes can be specified with keywords or by directly
+        accessing the edge's attribute dictionary. See examples below.
+
+        Parameters
+        ----------
+        u_of_edge, v_of_edge : nodes
+            Nodes can be, for example, strings or numbers.
+            Nodes must be hashable (and not None) Python objects.
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        See Also
+        --------
+        add_edges_from : add a collection of edges
+
+        Notes
+        -----
+        Adding an edge that already exists updates the edge data.
+
+        Many NetworkX algorithms designed for weighted graphs use
+        an edge attribute (by default `weight`) to hold a numerical value.
+
+        Examples
+        --------
+        The following all add the edge e=(1, 2) to graph G:
+
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> e = (1, 2)
+        >>> G.add_edge(1, 2)  # explicit two-node form
+        >>> G.add_edge(*e)  # single edge as tuple of two nodes
+        >>> G.add_edges_from([(1, 2)])  # add edges from iterable container
+
+        Associate data to edges using keywords:
+
+        >>> G.add_edge(1, 2, weight=3)
+        >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
+
+        For non-string attribute keys, use subscript notation.
+
+        >>> G.add_edge(1, 2)
+        >>> G[1][2].update({0: 5})
+        >>> G.edges[1, 2].update({0: 5})
+        """
+        u, v = u_of_edge, v_of_edge
+        # add nodes
+        if u not in self._node:
+            if u is None:
+                raise ValueError("None cannot be a node")
+            self._adj[u] = self.adjlist_inner_dict_factory()
+            self._node[u] = self.node_attr_dict_factory()
+        if v not in self._node:
+            if v is None:
+                raise ValueError("None cannot be a node")
+            self._adj[v] = self.adjlist_inner_dict_factory()
+            self._node[v] = self.node_attr_dict_factory()
+        # add the edge
+        datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
+        datadict.update(attr)
+        self._adj[u][v] = datadict
+        self._adj[v][u] = datadict
+        nx._clear_cache(self)
+
+    def add_edges_from(self, ebunch_to_add, **attr):
+        """Add all the edges in ebunch_to_add.
+
+        Parameters
+        ----------
+        ebunch_to_add : container of edges
+            Each edge given in the container will be added to the
+            graph. The edges must be given as 2-tuples (u, v) or
+            3-tuples (u, v, d) where d is a dictionary containing edge data.
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        See Also
+        --------
+        add_edge : add a single edge
+        add_weighted_edges_from : convenient way to add weighted edges
+
+        Notes
+        -----
+        Adding the same edge twice has no effect but any edge data
+        will be updated when each duplicate edge is added.
+
+        Edge attributes specified in an ebunch take precedence over
+        attributes specified via keyword arguments.
+
+        When adding edges from an iterator over the graph you are changing,
+        a `RuntimeError` can be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_edges)`, and pass this
+        object to `G.add_edges_from`.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_edges_from([(0, 1), (1, 2)])  # using a list of edge tuples
+        >>> e = zip(range(0, 3), range(1, 4))
+        >>> G.add_edges_from(e)  # Add the path graph 0-1-2-3
+
+        Associate data to edges
+
+        >>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
+        >>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.Graph([(1, 2), (2, 3), (3, 4)])
+        >>> # Grow graph by one new node, adding edges to all existing nodes.
+        >>> # wrong way - will raise RuntimeError
+        >>> # G.add_edges_from(((5, n) for n in G.nodes))
+        >>> # correct way - note that there will be no self-edge for node 5
+        >>> G.add_edges_from(list((5, n) for n in G.nodes))
+        """
+        for e in ebunch_to_add:
+            ne = len(e)
+            if ne == 3:
+                u, v, dd = e
+            elif ne == 2:
+                u, v = e
+                dd = {}  # doesn't need edge_attr_dict_factory
+            else:
+                raise NetworkXError(f"Edge tuple {e} must be a 2-tuple or 3-tuple.")
+            if u not in self._node:
+                if u is None:
+                    raise ValueError("None cannot be a node")
+                self._adj[u] = self.adjlist_inner_dict_factory()
+                self._node[u] = self.node_attr_dict_factory()
+            if v not in self._node:
+                if v is None:
+                    raise ValueError("None cannot be a node")
+                self._adj[v] = self.adjlist_inner_dict_factory()
+                self._node[v] = self.node_attr_dict_factory()
+            datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
+            datadict.update(attr)
+            datadict.update(dd)
+            self._adj[u][v] = datadict
+            self._adj[v][u] = datadict
+        nx._clear_cache(self)
+
+    def add_weighted_edges_from(self, ebunch_to_add, weight="weight", **attr):
+        """Add weighted edges in `ebunch_to_add` with specified weight attr
+
+        Parameters
+        ----------
+        ebunch_to_add : container of edges
+            Each edge given in the list or container will be added
+            to the graph. The edges must be given as 3-tuples (u, v, w)
+            where w is a number.
+        weight : string, optional (default= 'weight')
+            The attribute name for the edge weights to be added.
+        attr : keyword arguments, optional (default= no attributes)
+            Edge attributes to add/update for all edges.
+
+        See Also
+        --------
+        add_edge : add a single edge
+        add_edges_from : add multiple edges
+
+        Notes
+        -----
+        Adding the same edge twice for Graph/DiGraph simply updates
+        the edge data. For MultiGraph/MultiDiGraph, duplicate edges
+        are stored.
+
+        When adding edges from an iterator over the graph you are changing,
+        a `RuntimeError` can be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_edges)`, and pass this
+        object to `G.add_weighted_edges_from`.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_weighted_edges_from([(0, 1, 3.0), (1, 2, 7.5)])
+
+        Evaluate an iterator over edges before passing it
+
+        >>> G = nx.Graph([(1, 2), (2, 3), (3, 4)])
+        >>> weight = 0.1
+        >>> # Grow graph by one new node, adding edges to all existing nodes.
+        >>> # wrong way - will raise RuntimeError
+        >>> # G.add_weighted_edges_from(((5, n, weight) for n in G.nodes))
+        >>> # correct way - note that there will be no self-edge for node 5
+        >>> G.add_weighted_edges_from(list((5, n, weight) for n in G.nodes))
+        """
+        self.add_edges_from(((u, v, {weight: d}) for u, v, d in ebunch_to_add), **attr)
+        nx._clear_cache(self)
+
+    def remove_edge(self, u, v):
+        """Remove the edge between u and v.
+
+        Parameters
+        ----------
+        u, v : nodes
+            Remove the edge between nodes u and v.
+
+        Raises
+        ------
+        NetworkXError
+            If there is not an edge between u and v.
+
+        See Also
+        --------
+        remove_edges_from : remove a collection of edges
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, etc
+        >>> G.remove_edge(0, 1)
+        >>> e = (1, 2)
+        >>> G.remove_edge(*e)  # unpacks e from an edge tuple
+        >>> e = (2, 3, {"weight": 7})  # an edge with attribute data
+        >>> G.remove_edge(*e[:2])  # select first part of edge tuple
+        """
+        try:
+            del self._adj[u][v]
+            if u != v:  # self-loop needs only one entry removed
+                del self._adj[v][u]
+        except KeyError as err:
+            raise NetworkXError(f"The edge {u}-{v} is not in the graph") from err
+        nx._clear_cache(self)
+
+    def remove_edges_from(self, ebunch):
+        """Remove all edges specified in ebunch.
+
+        Parameters
+        ----------
+        ebunch: list or container of edge tuples
+            Each edge given in the list or container will be removed
+            from the graph. The edges can be:
+
+                - 2-tuples (u, v) edge between u and v.
+                - 3-tuples (u, v, k) where k is ignored.
+
+        See Also
+        --------
+        remove_edge : remove a single edge
+
+        Notes
+        -----
+        Will fail silently if an edge in ebunch is not in the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> ebunch = [(1, 2), (2, 3)]
+        >>> G.remove_edges_from(ebunch)
+        """
+        adj = self._adj
+        for e in ebunch:
+            u, v = e[:2]  # ignore edge data if present
+            if u in adj and v in adj[u]:
+                del adj[u][v]
+                if u != v:  # self loop needs only one entry removed
+                    del adj[v][u]
+        nx._clear_cache(self)
+
+    def update(self, edges=None, nodes=None):
+        """Update the graph using nodes/edges/graphs as input.
+
+        Like dict.update, this method takes a graph as input, adding the
+        graph's nodes and edges to this graph. It can also take two inputs:
+        edges and nodes. Finally it can take either edges or nodes.
+        To specify only nodes the keyword `nodes` must be used.
+
+        The collections of edges and nodes are treated similarly to
+        the add_edges_from/add_nodes_from methods. When iterated, they
+        should yield 2-tuples (u, v) or 3-tuples (u, v, datadict).
+
+        Parameters
+        ----------
+        edges : Graph object, collection of edges, or None
+            The first parameter can be a graph or some edges. If it has
+            attributes `nodes` and `edges`, then it is taken to be a
+            Graph-like object and those attributes are used as collections
+            of nodes and edges to be added to the graph.
+            If the first parameter does not have those attributes, it is
+            treated as a collection of edges and added to the graph.
+            If the first argument is None, no edges are added.
+        nodes : collection of nodes, or None
+            The second parameter is treated as a collection of nodes
+            to be added to the graph unless it is None.
+            If `edges is None` and `nodes is None` an exception is raised.
+            If the first parameter is a Graph, then `nodes` is ignored.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(5)
+        >>> G.update(nx.complete_graph(range(4, 10)))
+        >>> from itertools import combinations
+        >>> edges = (
+        ...     (u, v, {"power": u * v})
+        ...     for u, v in combinations(range(10, 20), 2)
+        ...     if u * v < 225
+        ... )
+        >>> nodes = [1000]  # for singleton, use a container
+        >>> G.update(edges, nodes)
+
+        Notes
+        -----
+        It you want to update the graph using an adjacency structure
+        it is straightforward to obtain the edges/nodes from adjacency.
+        The following examples provide common cases, your adjacency may
+        be slightly different and require tweaks of these examples::
+
+        >>> # dict-of-set/list/tuple
+        >>> adj = {1: {2, 3}, 2: {1, 3}, 3: {1, 2}}
+        >>> e = [(u, v) for u, nbrs in adj.items() for v in nbrs]
+        >>> G.update(edges=e, nodes=adj)
+
+        >>> DG = nx.DiGraph()
+        >>> # dict-of-dict-of-attribute
+        >>> adj = {1: {2: 1.3, 3: 0.7}, 2: {1: 1.4}, 3: {1: 0.7}}
+        >>> e = [
+        ...     (u, v, {"weight": d})
+        ...     for u, nbrs in adj.items()
+        ...     for v, d in nbrs.items()
+        ... ]
+        >>> DG.update(edges=e, nodes=adj)
+
+        >>> # dict-of-dict-of-dict
+        >>> adj = {1: {2: {"weight": 1.3}, 3: {"color": 0.7, "weight": 1.2}}}
+        >>> e = [
+        ...     (u, v, {"weight": d})
+        ...     for u, nbrs in adj.items()
+        ...     for v, d in nbrs.items()
+        ... ]
+        >>> DG.update(edges=e, nodes=adj)
+
+        >>> # predecessor adjacency (dict-of-set)
+        >>> pred = {1: {2, 3}, 2: {3}, 3: {3}}
+        >>> e = [(v, u) for u, nbrs in pred.items() for v in nbrs]
+
+        >>> # MultiGraph dict-of-dict-of-dict-of-attribute
+        >>> MDG = nx.MultiDiGraph()
+        >>> adj = {
+        ...     1: {2: {0: {"weight": 1.3}, 1: {"weight": 1.2}}},
+        ...     3: {2: {0: {"weight": 0.7}}},
+        ... }
+        >>> e = [
+        ...     (u, v, ekey, d)
+        ...     for u, nbrs in adj.items()
+        ...     for v, keydict in nbrs.items()
+        ...     for ekey, d in keydict.items()
+        ... ]
+        >>> MDG.update(edges=e)
+
+        See Also
+        --------
+        add_edges_from: add multiple edges to a graph
+        add_nodes_from: add multiple nodes to a graph
+        """
+        if edges is not None:
+            if nodes is not None:
+                self.add_nodes_from(nodes)
+                self.add_edges_from(edges)
+            else:
+                # check if edges is a Graph object
+                try:
+                    graph_nodes = edges.nodes
+                    graph_edges = edges.edges
+                except AttributeError:
+                    # edge not Graph-like
+                    self.add_edges_from(edges)
+                else:  # edges is Graph-like
+                    self.add_nodes_from(graph_nodes.data())
+                    self.add_edges_from(graph_edges.data())
+                    self.graph.update(edges.graph)
+        elif nodes is not None:
+            self.add_nodes_from(nodes)
+        else:
+            raise NetworkXError("update needs nodes or edges input")
+
+    def has_edge(self, u, v):
+        """Returns True if the edge (u, v) is in the graph.
+
+        This is the same as `v in G[u]` without KeyError exceptions.
+
+        Parameters
+        ----------
+        u, v : nodes
+            Nodes can be, for example, strings or numbers.
+            Nodes must be hashable (and not None) Python objects.
+
+        Returns
+        -------
+        edge_ind : bool
+            True if edge is in the graph, False otherwise.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.has_edge(0, 1)  # using two nodes
+        True
+        >>> e = (0, 1)
+        >>> G.has_edge(*e)  #  e is a 2-tuple (u, v)
+        True
+        >>> e = (0, 1, {"weight": 7})
+        >>> G.has_edge(*e[:2])  # e is a 3-tuple (u, v, data_dictionary)
+        True
+
+        The following syntax are equivalent:
+
+        >>> G.has_edge(0, 1)
+        True
+        >>> 1 in G[0]  # though this gives KeyError if 0 not in G
+        True
+
+        """
+        try:
+            return v in self._adj[u]
+        except KeyError:
+            return False
+
+    def neighbors(self, n):
+        """Returns an iterator over all neighbors of node n.
+
+        This is identical to `iter(G[n])`
+
+        Parameters
+        ----------
+        n : node
+           A node in the graph
+
+        Returns
+        -------
+        neighbors : iterator
+            An iterator over all neighbors of node n
+
+        Raises
+        ------
+        NetworkXError
+            If the node n is not in the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> [n for n in G.neighbors(0)]
+        [1]
+
+        Notes
+        -----
+        Alternate ways to access the neighbors are ``G.adj[n]`` or ``G[n]``:
+
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_edge("a", "b", weight=7)
+        >>> G["a"]
+        AtlasView({'b': {'weight': 7}})
+        >>> G = nx.path_graph(4)
+        >>> [n for n in G[0]]
+        [1]
+        """
+        try:
+            return iter(self._adj[n])
+        except KeyError as err:
+            raise NetworkXError(f"The node {n} is not in the graph.") from err
+
+    @cached_property
+    def edges(self):
+        """An EdgeView of the Graph as G.edges or G.edges().
+
+        edges(self, nbunch=None, data=False, default=None)
+
+        The EdgeView provides set-like operations on the edge-tuples
+        as well as edge attribute lookup. When called, it also provides
+        an EdgeDataView object which allows control of access to edge
+        attributes (but does not provide set-like operations).
+        Hence, `G.edges[u, v]['color']` provides the value of the color
+        attribute for edge `(u, v)` while
+        `for (u, v, c) in G.edges.data('color', default='red'):`
+        iterates through all the edges yielding the color attribute
+        with default `'red'` if no color attribute exists.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges from these nodes.
+        data : string or bool, optional (default=False)
+            The edge attribute returned in 3-tuple (u, v, ddict[data]).
+            If True, return edge attribute dict in 3-tuple (u, v, ddict).
+            If False, return 2-tuple (u, v).
+        default : value, optional (default=None)
+            Value used for edges that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        edges : EdgeView
+            A view of edge attributes, usually it iterates over (u, v)
+            or (u, v, d) tuples of edges, but can also be used for
+            attribute lookup as `edges[u, v]['foo']`.
+
+        Notes
+        -----
+        Nodes in nbunch that are not in the graph will be (quietly) ignored.
+        For directed graphs this returns the out-edges.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(3)  # or MultiGraph, etc
+        >>> G.add_edge(2, 3, weight=5)
+        >>> [e for e in G.edges]
+        [(0, 1), (1, 2), (2, 3)]
+        >>> G.edges.data()  # default data is {} (empty dict)
+        EdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})])
+        >>> G.edges.data("weight", default=1)
+        EdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)])
+        >>> G.edges([0, 3])  # only edges from these nodes
+        EdgeDataView([(0, 1), (3, 2)])
+        >>> G.edges(0)  # only edges from node 0
+        EdgeDataView([(0, 1)])
+        """
+        return EdgeView(self)
+
+    def get_edge_data(self, u, v, default=None):
+        """Returns the attribute dictionary associated with edge (u, v).
+
+        This is identical to `G[u][v]` except the default is returned
+        instead of an exception if the edge doesn't exist.
+
+        Parameters
+        ----------
+        u, v : nodes
+        default:  any Python object (default=None)
+            Value to return if the edge (u, v) is not found.
+
+        Returns
+        -------
+        edge_dict : dictionary
+            The edge attribute dictionary.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G[0][1]
+        {}
+
+        Warning: Assigning to `G[u][v]` is not permitted.
+        But it is safe to assign attributes `G[u][v]['foo']`
+
+        >>> G[0][1]["weight"] = 7
+        >>> G[0][1]["weight"]
+        7
+        >>> G[1][0]["weight"]
+        7
+
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.get_edge_data(0, 1)  # default edge data is {}
+        {}
+        >>> e = (0, 1)
+        >>> G.get_edge_data(*e)  # tuple form
+        {}
+        >>> G.get_edge_data("a", "b", default=0)  # edge not in graph, return 0
+        0
+        """
+        try:
+            return self._adj[u][v]
+        except KeyError:
+            return default
+
+    def adjacency(self):
+        """Returns an iterator over (node, adjacency dict) tuples for all nodes.
+
+        For directed graphs, only outgoing neighbors/adjacencies are included.
+
+        Returns
+        -------
+        adj_iter : iterator
+           An iterator over (node, adjacency dictionary) for all nodes in
+           the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> [(n, nbrdict) for n, nbrdict in G.adjacency()]
+        [(0, {1: {}}), (1, {0: {}, 2: {}}), (2, {1: {}, 3: {}}), (3, {2: {}})]
+
+        """
+        return iter(self._adj.items())
+
+    @cached_property
+    def degree(self):
+        """A DegreeView for the Graph as G.degree or G.degree().
+
+        The node degree is the number of edges adjacent to the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iterator for (node, degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The name of an edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        DegreeView or int
+            If multiple nodes are requested (the default), returns a `DegreeView`
+            mapping nodes to their degree.
+            If a single node is requested, returns the degree of the node as an integer.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.degree[0]  # node 0 has degree 1
+        1
+        >>> list(G.degree([0, 1, 2]))
+        [(0, 1), (1, 2), (2, 2)]
+        """
+        return DegreeView(self)
+
+    def clear(self):
+        """Remove all nodes and edges from the graph.
+
+        This also removes the name, and all graph, node, and edge attributes.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.clear()
+        >>> list(G.nodes)
+        []
+        >>> list(G.edges)
+        []
+
+        """
+        self._adj.clear()
+        self._node.clear()
+        self.graph.clear()
+        nx._clear_cache(self)
+
+    def clear_edges(self):
+        """Remove all edges from the graph without altering nodes.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.clear_edges()
+        >>> list(G.nodes)
+        [0, 1, 2, 3]
+        >>> list(G.edges)
+        []
+        """
+        for nbr_dict in self._adj.values():
+            nbr_dict.clear()
+        nx._clear_cache(self)
+
+    def is_multigraph(self):
+        """Returns True if graph is a multigraph, False otherwise."""
+        return False
+
+    def is_directed(self):
+        """Returns True if graph is directed, False otherwise."""
+        return False
+
+    def copy(self, as_view=False):
+        """Returns a copy of the graph.
+
+        The copy method by default returns an independent shallow copy
+        of the graph and attributes. That is, if an attribute is a
+        container, that container is shared by the original an the copy.
+        Use Python's `copy.deepcopy` for new containers.
+
+        If `as_view` is True then a view is returned instead of a copy.
+
+        Notes
+        -----
+        All copies reproduce the graph structure, but data attributes
+        may be handled in different ways. There are four types of copies
+        of a graph that people might want.
+
+        Deepcopy -- A "deepcopy" copies the graph structure as well as
+        all data attributes and any objects they might contain.
+        The entire graph object is new so that changes in the copy
+        do not affect the original object. (see Python's copy.deepcopy)
+
+        Data Reference (Shallow) -- For a shallow copy the graph structure
+        is copied but the edge, node and graph attribute dicts are
+        references to those in the original graph. This saves
+        time and memory but could cause confusion if you change an attribute
+        in one graph and it changes the attribute in the other.
+        NetworkX does not provide this level of shallow copy.
+
+        Independent Shallow -- This copy creates new independent attribute
+        dicts and then does a shallow copy of the attributes. That is, any
+        attributes that are containers are shared between the new graph
+        and the original. This is exactly what `dict.copy()` provides.
+        You can obtain this style copy using:
+
+            >>> G = nx.path_graph(5)
+            >>> H = G.copy()
+            >>> H = G.copy(as_view=False)
+            >>> H = nx.Graph(G)
+            >>> H = G.__class__(G)
+
+        Fresh Data -- For fresh data, the graph structure is copied while
+        new empty data attribute dicts are created. The resulting graph
+        is independent of the original and it has no edge, node or graph
+        attributes. Fresh copies are not enabled. Instead use:
+
+            >>> H = G.__class__()
+            >>> H.add_nodes_from(G)
+            >>> H.add_edges_from(G.edges)
+
+        View -- Inspired by dict-views, graph-views act like read-only
+        versions of the original graph, providing a copy of the original
+        structure without requiring any memory for copying the information.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Parameters
+        ----------
+        as_view : bool, optional (default=False)
+            If True, the returned graph-view provides a read-only view
+            of the original graph without actually copying any data.
+
+        Returns
+        -------
+        G : Graph
+            A copy of the graph.
+
+        See Also
+        --------
+        to_directed: return a directed copy of the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> H = G.copy()
+
+        """
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self)
+        G = self.__class__()
+        G.graph.update(self.graph)
+        G.add_nodes_from((n, d.copy()) for n, d in self._node.items())
+        G.add_edges_from(
+            (u, v, datadict.copy())
+            for u, nbrs in self._adj.items()
+            for v, datadict in nbrs.items()
+        )
+        return G
+
+    def to_directed(self, as_view=False):
+        """Returns a directed representation of the graph.
+
+        Returns
+        -------
+        G : DiGraph
+            A directed graph with the same name, same nodes, and with
+            each edge (u, v, data) replaced by two directed edges
+            (u, v, data) and (v, u, data).
+
+        Notes
+        -----
+        This returns a "deepcopy" of the edge, node, and
+        graph attributes which attempts to completely copy
+        all of the data and references.
+
+        This is in contrast to the similar D=DiGraph(G) which returns a
+        shallow copy of the data.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Warning: If you have subclassed Graph to use dict-like objects
+        in the data structure, those changes do not transfer to the
+        DiGraph created by this method.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or MultiGraph, etc
+        >>> G.add_edge(0, 1)
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1), (1, 0)]
+
+        If already directed, return a (deep) copy
+
+        >>> G = nx.DiGraph()  # or MultiDiGraph, etc
+        >>> G.add_edge(0, 1)
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1)]
+        """
+        graph_class = self.to_directed_class()
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self, graph_class)
+        # deepcopy when not a view
+        G = graph_class()
+        G.graph.update(deepcopy(self.graph))
+        G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+        G.add_edges_from(
+            (u, v, deepcopy(data))
+            for u, nbrs in self._adj.items()
+            for v, data in nbrs.items()
+        )
+        return G
+
+    def to_undirected(self, as_view=False):
+        """Returns an undirected copy of the graph.
+
+        Parameters
+        ----------
+        as_view : bool (optional, default=False)
+          If True return a view of the original undirected graph.
+
+        Returns
+        -------
+        G : Graph/MultiGraph
+            A deepcopy of the graph.
+
+        See Also
+        --------
+        Graph, copy, add_edge, add_edges_from
+
+        Notes
+        -----
+        This returns a "deepcopy" of the edge, node, and
+        graph attributes which attempts to completely copy
+        all of the data and references.
+
+        This is in contrast to the similar `G = nx.DiGraph(D)` which returns a
+        shallow copy of the data.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Warning: If you have subclassed DiGraph to use dict-like objects
+        in the data structure, those changes do not transfer to the
+        Graph created by this method.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(2)  # or MultiGraph, etc
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1), (1, 0)]
+        >>> G2 = H.to_undirected()
+        >>> list(G2.edges)
+        [(0, 1)]
+        """
+        graph_class = self.to_undirected_class()
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self, graph_class)
+        # deepcopy when not a view
+        G = graph_class()
+        G.graph.update(deepcopy(self.graph))
+        G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+        G.add_edges_from(
+            (u, v, deepcopy(d))
+            for u, nbrs in self._adj.items()
+            for v, d in nbrs.items()
+        )
+        return G
+
+    def subgraph(self, nodes):
+        """Returns a SubGraph view of the subgraph induced on `nodes`.
+
+        The induced subgraph of the graph contains the nodes in `nodes`
+        and the edges between those nodes.
+
+        Parameters
+        ----------
+        nodes : list, iterable
+            A container of nodes which will be iterated through once.
+
+        Returns
+        -------
+        G : SubGraph View
+            A subgraph view of the graph. The graph structure cannot be
+            changed but node/edge attributes can and are shared with the
+            original graph.
+
+        Notes
+        -----
+        The graph, edge and node attributes are shared with the original graph.
+        Changes to the graph structure is ruled out by the view, but changes
+        to attributes are reflected in the original graph.
+
+        To create a subgraph with its own copy of the edge/node attributes use:
+        G.subgraph(nodes).copy()
+
+        For an inplace reduction of a graph to a subgraph you can remove nodes:
+        G.remove_nodes_from([n for n in G if n not in set(nodes)])
+
+        Subgraph views are sometimes NOT what you want. In most cases where
+        you want to do more than simply look at the induced edges, it makes
+        more sense to just create the subgraph as its own graph with code like:
+
+        ::
+
+            # Create a subgraph SG based on a (possibly multigraph) G
+            SG = G.__class__()
+            SG.add_nodes_from((n, G.nodes[n]) for n in largest_wcc)
+            if SG.is_multigraph():
+                SG.add_edges_from(
+                    (n, nbr, key, d)
+                    for n, nbrs in G.adj.items()
+                    if n in largest_wcc
+                    for nbr, keydict in nbrs.items()
+                    if nbr in largest_wcc
+                    for key, d in keydict.items()
+                )
+            else:
+                SG.add_edges_from(
+                    (n, nbr, d)
+                    for n, nbrs in G.adj.items()
+                    if n in largest_wcc
+                    for nbr, d in nbrs.items()
+                    if nbr in largest_wcc
+                )
+            SG.graph.update(G.graph)
+
+        Subgraphs are not guaranteed to preserve the order of nodes or edges
+        as they appear in the original graph. For example:
+
+        >>> G = nx.Graph()
+        >>> G.add_nodes_from(reversed(range(10)))
+        >>> list(G)
+        [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+        >>> list(G.subgraph([1, 3, 2]))
+        [1, 2, 3]
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> H = G.subgraph([0, 1, 2])
+        >>> list(H.edges)
+        [(0, 1), (1, 2)]
+        """
+        induced_nodes = nx.filters.show_nodes(self.nbunch_iter(nodes))
+        # if already a subgraph, don't make a chain
+        subgraph = nx.subgraph_view
+        if hasattr(self, "_NODE_OK"):
+            return subgraph(
+                self._graph, filter_node=induced_nodes, filter_edge=self._EDGE_OK
+            )
+        return subgraph(self, filter_node=induced_nodes)
+
+    def edge_subgraph(self, edges):
+        """Returns the subgraph induced by the specified edges.
+
+        The induced subgraph contains each edge in `edges` and each
+        node incident to any one of those edges.
+
+        Parameters
+        ----------
+        edges : iterable
+            An iterable of edges in this graph.
+
+        Returns
+        -------
+        G : Graph
+            An edge-induced subgraph of this graph with the same edge
+            attributes.
+
+        Notes
+        -----
+        The graph, edge, and node attributes in the returned subgraph
+        view are references to the corresponding attributes in the original
+        graph. The view is read-only.
+
+        To create a full graph version of the subgraph with its own copy
+        of the edge or node attributes, use::
+
+            G.edge_subgraph(edges).copy()
+
+        Examples
+        --------
+        >>> G = nx.path_graph(5)
+        >>> H = G.edge_subgraph([(0, 1), (3, 4)])
+        >>> list(H.nodes)
+        [0, 1, 3, 4]
+        >>> list(H.edges)
+        [(0, 1), (3, 4)]
+
+        """
+        return nx.edge_subgraph(self, edges)
+
+    def size(self, weight=None):
+        """Returns the number of edges or total of all edge weights.
+
+        Parameters
+        ----------
+        weight : string or None, optional (default=None)
+            The edge attribute that holds the numerical value used
+            as a weight. If None, then each edge has weight 1.
+
+        Returns
+        -------
+        size : numeric
+            The number of edges or
+            (if weight keyword is provided) the total weight sum.
+
+            If weight is None, returns an int. Otherwise a float
+            (or more general numeric if the weights are more general).
+
+        See Also
+        --------
+        number_of_edges
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.size()
+        3
+
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_edge("a", "b", weight=2)
+        >>> G.add_edge("b", "c", weight=4)
+        >>> G.size()
+        2
+        >>> G.size(weight="weight")
+        6.0
+        """
+        s = sum(d for v, d in self.degree(weight=weight))
+        # If `weight` is None, the sum of the degrees is guaranteed to be
+        # even, so we can perform integer division and hence return an
+        # integer. Otherwise, the sum of the weighted degrees is not
+        # guaranteed to be an integer, so we perform "real" division.
+        return s // 2 if weight is None else s / 2
+
+    def number_of_edges(self, u=None, v=None):
+        """Returns the number of edges between two nodes.
+
+        Parameters
+        ----------
+        u, v : nodes, optional (default=all edges)
+            If u and v are specified, return the number of edges between
+            u and v. Otherwise return the total number of all edges.
+
+        Returns
+        -------
+        nedges : int
+            The number of edges in the graph.  If nodes `u` and `v` are
+            specified return the number of edges between those nodes. If
+            the graph is directed, this only returns the number of edges
+            from `u` to `v`.
+
+        See Also
+        --------
+        size
+
+        Examples
+        --------
+        For undirected graphs, this method counts the total number of
+        edges in the graph:
+
+        >>> G = nx.path_graph(4)
+        >>> G.number_of_edges()
+        3
+
+        If you specify two nodes, this counts the total number of edges
+        joining the two nodes:
+
+        >>> G.number_of_edges(0, 1)
+        1
+
+        For directed graphs, this method can count the total number of
+        directed edges from `u` to `v`:
+
+        >>> G = nx.DiGraph()
+        >>> G.add_edge(0, 1)
+        >>> G.add_edge(1, 0)
+        >>> G.number_of_edges(0, 1)
+        1
+
+        """
+        if u is None:
+            return int(self.size())
+        if v in self._adj[u]:
+            return 1
+        return 0
+
+    def nbunch_iter(self, nbunch=None):
+        """Returns an iterator over nodes contained in nbunch that are
+        also in the graph.
+
+        The nodes in an iterable nbunch are checked for membership in the graph
+        and if not are silently ignored.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        Returns
+        -------
+        niter : iterator
+            An iterator over nodes in nbunch that are also in the graph.
+            If nbunch is None, iterate over all nodes in the graph.
+
+        Raises
+        ------
+        NetworkXError
+            If nbunch is not a node or sequence of nodes.
+            If a node in nbunch is not hashable.
+
+        See Also
+        --------
+        Graph.__iter__
+
+        Notes
+        -----
+        When nbunch is an iterator, the returned iterator yields values
+        directly from nbunch, becoming exhausted when nbunch is exhausted.
+
+        To test whether nbunch is a single node, one can use
+        "if nbunch in self:", even after processing with this routine.
+
+        If nbunch is not a node or a (possibly empty) sequence/iterator
+        or None, a :exc:`NetworkXError` is raised.  Also, if any object in
+        nbunch is not hashable, a :exc:`NetworkXError` is raised.
+        """
+        if nbunch is None:  # include all nodes via iterator
+            bunch = iter(self._adj)
+        elif nbunch in self:  # if nbunch is a single node
+            bunch = iter([nbunch])
+        else:  # if nbunch is a sequence of nodes
+
+            def bunch_iter(nlist, adj):
+                try:
+                    for n in nlist:
+                        if n in adj:
+                            yield n
+                except TypeError as err:
+                    exc, message = err, err.args[0]
+                    # capture error for non-sequence/iterator nbunch.
+                    if "iter" in message:
+                        exc = NetworkXError(
+                            "nbunch is not a node or a sequence of nodes."
+                        )
+                    # capture single nodes that are not in the graph.
+                    if "object is not iterable" in message:
+                        exc = NetworkXError(f"Node {nbunch} is not in the graph.")
+                    # capture error for unhashable node.
+                    if "hashable" in message:
+                        exc = NetworkXError(
+                            f"Node {n} in sequence nbunch is not a valid node."
+                        )
+                    raise exc
+
+            bunch = bunch_iter(nbunch, self._adj)
+        return bunch
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/graphviews.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/graphviews.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b09df649ef48fa484d27e51d86cce1e10d593a7
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/graphviews.py
@@ -0,0 +1,269 @@
+"""View of Graphs as SubGraph, Reverse, Directed, Undirected.
+
+In some algorithms it is convenient to temporarily morph
+a graph to exclude some nodes or edges. It should be better
+to do that via a view than to remove and then re-add.
+In other algorithms it is convenient to temporarily morph
+a graph to reverse directed edges, or treat a directed graph
+as undirected, etc. This module provides those graph views.
+
+The resulting views are essentially read-only graphs that
+report data from the original graph object. We provide an
+attribute G._graph which points to the underlying graph object.
+
+Note: Since graphviews look like graphs, one can end up with
+view-of-view-of-view chains. Be careful with chains because
+they become very slow with about 15 nested views.
+For the common simple case of node induced subgraphs created
+from the graph class, we short-cut the chain by returning a
+subgraph of the original graph directly rather than a subgraph
+of a subgraph. We are careful not to disrupt any edge filter in
+the middle subgraph. In general, determining how to short-cut
+the chain is tricky and much harder with restricted_views than
+with induced subgraphs.
+Often it is easiest to use .copy() to avoid chains.
+"""
+
+import networkx as nx
+from networkx.classes.coreviews import (
+    FilterAdjacency,
+    FilterAtlas,
+    FilterMultiAdjacency,
+    UnionAdjacency,
+    UnionMultiAdjacency,
+)
+from networkx.classes.filters import no_filter
+from networkx.exception import NetworkXError
+from networkx.utils import not_implemented_for
+
+__all__ = ["generic_graph_view", "subgraph_view", "reverse_view"]
+
+
+def generic_graph_view(G, create_using=None):
+    """Returns a read-only view of `G`.
+
+    The graph `G` and its attributes are not copied but viewed through the new graph object
+    of the same class as `G` (or of the class specified in `create_using`).
+
+    Parameters
+    ----------
+    G : graph
+        A directed/undirected graph/multigraph.
+
+    create_using : NetworkX graph constructor, optional (default=None)
+       Graph type to create. If graph instance, then cleared before populated.
+       If `None`, then the appropriate Graph type is inferred from `G`.
+
+    Returns
+    -------
+    newG : graph
+        A view of the input graph `G` and its attributes as viewed through
+        the `create_using` class.
+
+    Raises
+    ------
+    NetworkXError
+        If `G` is a multigraph (or multidigraph) but `create_using` is not, or vice versa.
+
+    Notes
+    -----
+    The returned graph view is read-only (cannot modify the graph).
+    Yet the view reflects any changes in `G`. The intent is to mimic dict views.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_edge(1, 2, weight=0.3)
+    >>> G.add_edge(2, 3, weight=0.5)
+    >>> G.edges(data=True)
+    EdgeDataView([(1, 2, {'weight': 0.3}), (2, 3, {'weight': 0.5})])
+
+    The view exposes the attributes from the original graph.
+
+    >>> viewG = nx.graphviews.generic_graph_view(G)
+    >>> viewG.edges(data=True)
+    EdgeDataView([(1, 2, {'weight': 0.3}), (2, 3, {'weight': 0.5})])
+
+    Changes to `G` are reflected in `viewG`.
+
+    >>> G.remove_edge(2, 3)
+    >>> G.edges(data=True)
+    EdgeDataView([(1, 2, {'weight': 0.3})])
+
+    >>> viewG.edges(data=True)
+    EdgeDataView([(1, 2, {'weight': 0.3})])
+
+    We can change the graph type with the `create_using` parameter.
+
+    >>> type(G)
+    <class 'networkx.classes.graph.Graph'>
+    >>> viewDG = nx.graphviews.generic_graph_view(G, create_using=nx.DiGraph)
+    >>> type(viewDG)
+    <class 'networkx.classes.digraph.DiGraph'>
+    """
+    if create_using is None:
+        newG = G.__class__()
+    else:
+        newG = nx.empty_graph(0, create_using)
+    if G.is_multigraph() != newG.is_multigraph():
+        raise NetworkXError("Multigraph for G must agree with create_using")
+    newG = nx.freeze(newG)
+
+    # create view by assigning attributes from G
+    newG._graph = G
+    newG.graph = G.graph
+
+    newG._node = G._node
+    if newG.is_directed():
+        if G.is_directed():
+            newG._succ = G._succ
+            newG._pred = G._pred
+            # newG._adj is synced with _succ
+        else:
+            newG._succ = G._adj
+            newG._pred = G._adj
+            # newG._adj is synced with _succ
+    elif G.is_directed():
+        if G.is_multigraph():
+            newG._adj = UnionMultiAdjacency(G._succ, G._pred)
+        else:
+            newG._adj = UnionAdjacency(G._succ, G._pred)
+    else:
+        newG._adj = G._adj
+    return newG
+
+
+def subgraph_view(G, *, filter_node=no_filter, filter_edge=no_filter):
+    """View of `G` applying a filter on nodes and edges.
+
+    `subgraph_view` provides a read-only view of the input graph that excludes
+    nodes and edges based on the outcome of two filter functions `filter_node`
+    and `filter_edge`.
+
+    The `filter_node` function takes one argument --- the node --- and returns
+    `True` if the node should be included in the subgraph, and `False` if it
+    should not be included.
+
+    The `filter_edge` function takes two (or three arguments if `G` is a
+    multi-graph) --- the nodes describing an edge, plus the edge-key if
+    parallel edges are possible --- and returns `True` if the edge should be
+    included in the subgraph, and `False` if it should not be included.
+
+    Both node and edge filter functions are called on graph elements as they
+    are queried, meaning there is no up-front cost to creating the view.
+
+    Parameters
+    ----------
+    G : networkx.Graph
+        A directed/undirected graph/multigraph
+
+    filter_node : callable, optional
+        A function taking a node as input, which returns `True` if the node
+        should appear in the view.
+
+    filter_edge : callable, optional
+        A function taking as input the two nodes describing an edge (plus the
+        edge-key if `G` is a multi-graph), which returns `True` if the edge
+        should appear in the view.
+
+    Returns
+    -------
+    graph : networkx.Graph
+        A read-only graph view of the input graph.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(6)
+
+    Filter functions operate on the node, and return `True` if the node should
+    appear in the view:
+
+    >>> def filter_node(n1):
+    ...     return n1 != 5
+    >>> view = nx.subgraph_view(G, filter_node=filter_node)
+    >>> view.nodes()
+    NodeView((0, 1, 2, 3, 4))
+
+    We can use a closure pattern to filter graph elements based on additional
+    data --- for example, filtering on edge data attached to the graph:
+
+    >>> G[3][4]["cross_me"] = False
+    >>> def filter_edge(n1, n2):
+    ...     return G[n1][n2].get("cross_me", True)
+    >>> view = nx.subgraph_view(G, filter_edge=filter_edge)
+    >>> view.edges()
+    EdgeView([(0, 1), (1, 2), (2, 3), (4, 5)])
+
+    >>> view = nx.subgraph_view(
+    ...     G,
+    ...     filter_node=filter_node,
+    ...     filter_edge=filter_edge,
+    ... )
+    >>> view.nodes()
+    NodeView((0, 1, 2, 3, 4))
+    >>> view.edges()
+    EdgeView([(0, 1), (1, 2), (2, 3)])
+    """
+    newG = nx.freeze(G.__class__())
+    newG._NODE_OK = filter_node
+    newG._EDGE_OK = filter_edge
+
+    # create view by assigning attributes from G
+    newG._graph = G
+    newG.graph = G.graph
+
+    newG._node = FilterAtlas(G._node, filter_node)
+    if G.is_multigraph():
+        Adj = FilterMultiAdjacency
+
+        def reverse_edge(u, v, k=None):
+            return filter_edge(v, u, k)
+
+    else:
+        Adj = FilterAdjacency
+
+        def reverse_edge(u, v, k=None):
+            return filter_edge(v, u)
+
+    if G.is_directed():
+        newG._succ = Adj(G._succ, filter_node, filter_edge)
+        newG._pred = Adj(G._pred, filter_node, reverse_edge)
+        # newG._adj is synced with _succ
+    else:
+        newG._adj = Adj(G._adj, filter_node, filter_edge)
+    return newG
+
+
+@not_implemented_for("undirected")
+def reverse_view(G):
+    """View of `G` with edge directions reversed
+
+    `reverse_view` returns a read-only view of the input graph where
+    edge directions are reversed.
+
+    Identical to digraph.reverse(copy=False)
+
+    Parameters
+    ----------
+    G : networkx.DiGraph
+
+    Returns
+    -------
+    graph : networkx.DiGraph
+
+    Examples
+    --------
+    >>> G = nx.DiGraph()
+    >>> G.add_edge(1, 2)
+    >>> G.add_edge(2, 3)
+    >>> G.edges()
+    OutEdgeView([(1, 2), (2, 3)])
+
+    >>> view = nx.reverse_view(G)
+    >>> view.edges()
+    OutEdgeView([(2, 1), (3, 2)])
+    """
+    newG = generic_graph_view(G)
+    newG._succ, newG._pred = G._pred, G._succ
+    # newG._adj is synced with _succ
+    return newG
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/multidigraph.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/multidigraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..597af796c8253ed94a1ac8c4bd40b6024b35208d
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/multidigraph.py
@@ -0,0 +1,966 @@
+"""Base class for MultiDiGraph."""
+
+from copy import deepcopy
+from functools import cached_property
+
+import networkx as nx
+from networkx import convert
+from networkx.classes.coreviews import MultiAdjacencyView
+from networkx.classes.digraph import DiGraph
+from networkx.classes.multigraph import MultiGraph
+from networkx.classes.reportviews import (
+    DiMultiDegreeView,
+    InMultiDegreeView,
+    InMultiEdgeView,
+    OutMultiDegreeView,
+    OutMultiEdgeView,
+)
+from networkx.exception import NetworkXError
+
+__all__ = ["MultiDiGraph"]
+
+
+class MultiDiGraph(MultiGraph, DiGraph):
+    """A directed graph class that can store multiedges.
+
+    Multiedges are multiple edges between two nodes.  Each edge
+    can hold optional data or attributes.
+
+    A MultiDiGraph holds directed edges.  Self loops are allowed.
+
+    Nodes can be arbitrary (hashable) Python objects with optional
+    key/value attributes. By convention `None` is not used as a node.
+
+    Edges are represented as links between nodes with optional
+    key/value attributes.
+
+    Parameters
+    ----------
+    incoming_graph_data : input graph (optional, default: None)
+        Data to initialize graph. If None (default) an empty
+        graph is created.  The data can be any format that is supported
+        by the to_networkx_graph() function, currently including edge list,
+        dict of dicts, dict of lists, NetworkX graph, 2D NumPy array, SciPy
+        sparse matrix, or PyGraphviz graph.
+
+    multigraph_input : bool or None (default None)
+        Note: Only used when `incoming_graph_data` is a dict.
+        If True, `incoming_graph_data` is assumed to be a
+        dict-of-dict-of-dict-of-dict structure keyed by
+        node to neighbor to edge keys to edge data for multi-edges.
+        A NetworkXError is raised if this is not the case.
+        If False, :func:`to_networkx_graph` is used to try to determine
+        the dict's graph data structure as either a dict-of-dict-of-dict
+        keyed by node to neighbor to edge data, or a dict-of-iterable
+        keyed by node to neighbors.
+        If None, the treatment for True is tried, but if it fails,
+        the treatment for False is tried.
+
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to graph as key=value pairs.
+
+    See Also
+    --------
+    Graph
+    DiGraph
+    MultiGraph
+
+    Examples
+    --------
+    Create an empty graph structure (a "null graph") with no nodes and
+    no edges.
+
+    >>> G = nx.MultiDiGraph()
+
+    G can be grown in several ways.
+
+    **Nodes:**
+
+    Add one node at a time:
+
+    >>> G.add_node(1)
+
+    Add the nodes from any container (a list, dict, set or
+    even the lines from a file or the nodes from another graph).
+
+    >>> G.add_nodes_from([2, 3])
+    >>> G.add_nodes_from(range(100, 110))
+    >>> H = nx.path_graph(10)
+    >>> G.add_nodes_from(H)
+
+    In addition to strings and integers any hashable Python object
+    (except None) can represent a node, e.g. a customized node object,
+    or even another Graph.
+
+    >>> G.add_node(H)
+
+    **Edges:**
+
+    G can also be grown by adding edges.
+
+    Add one edge,
+
+    >>> key = G.add_edge(1, 2)
+
+    a list of edges,
+
+    >>> keys = G.add_edges_from([(1, 2), (1, 3)])
+
+    or a collection of edges,
+
+    >>> keys = G.add_edges_from(H.edges)
+
+    If some edges connect nodes not yet in the graph, the nodes
+    are added automatically.  If an edge already exists, an additional
+    edge is created and stored using a key to identify the edge.
+    By default the key is the lowest unused integer.
+
+    >>> keys = G.add_edges_from([(4, 5, dict(route=282)), (4, 5, dict(route=37))])
+    >>> G[4]
+    AdjacencyView({5: {0: {}, 1: {'route': 282}, 2: {'route': 37}}})
+
+    **Attributes:**
+
+    Each graph, node, and edge can hold key/value attribute pairs
+    in an associated attribute dictionary (the keys must be hashable).
+    By default these are empty, but can be added or changed using
+    add_edge, add_node or direct manipulation of the attribute
+    dictionaries named graph, node and edge respectively.
+
+    >>> G = nx.MultiDiGraph(day="Friday")
+    >>> G.graph
+    {'day': 'Friday'}
+
+    Add node attributes using add_node(), add_nodes_from() or G.nodes
+
+    >>> G.add_node(1, time="5pm")
+    >>> G.add_nodes_from([3], time="2pm")
+    >>> G.nodes[1]
+    {'time': '5pm'}
+    >>> G.nodes[1]["room"] = 714
+    >>> del G.nodes[1]["room"]  # remove attribute
+    >>> list(G.nodes(data=True))
+    [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
+
+    Add edge attributes using add_edge(), add_edges_from(), subscript
+    notation, or G.edges.
+
+    >>> key = G.add_edge(1, 2, weight=4.7)
+    >>> keys = G.add_edges_from([(3, 4), (4, 5)], color="red")
+    >>> keys = G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
+    >>> G[1][2][0]["weight"] = 4.7
+    >>> G.edges[1, 2, 0]["weight"] = 4
+
+    Warning: we protect the graph data structure by making `G.edges[1,
+    2, 0]` a read-only dict-like structure. However, you can assign to
+    attributes in e.g. `G.edges[1, 2, 0]`. Thus, use 2 sets of brackets
+    to add/change data attributes: `G.edges[1, 2, 0]['weight'] = 4`
+    (for multigraphs the edge key is required: `MG.edges[u, v,
+    key][name] = value`).
+
+    **Shortcuts:**
+
+    Many common graph features allow python syntax to speed reporting.
+
+    >>> 1 in G  # check if node in graph
+    True
+    >>> [n for n in G if n < 3]  # iterate through nodes
+    [1, 2]
+    >>> len(G)  # number of nodes in graph
+    5
+    >>> G[1]  # adjacency dict-like view mapping neighbor -> edge key -> edge attributes
+    AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}})
+
+    Often the best way to traverse all edges of a graph is via the neighbors.
+    The neighbors are available as an adjacency-view `G.adj` object or via
+    the method `G.adjacency()`.
+
+    >>> for n, nbrsdict in G.adjacency():
+    ...     for nbr, keydict in nbrsdict.items():
+    ...         for key, eattr in keydict.items():
+    ...             if "weight" in eattr:
+    ...                 # Do something useful with the edges
+    ...                 pass
+
+    But the edges() method is often more convenient:
+
+    >>> for u, v, keys, weight in G.edges(data="weight", keys=True):
+    ...     if weight is not None:
+    ...         # Do something useful with the edges
+    ...         pass
+
+    **Reporting:**
+
+    Simple graph information is obtained using methods and object-attributes.
+    Reporting usually provides views instead of containers to reduce memory
+    usage. The views update as the graph is updated similarly to dict-views.
+    The objects `nodes`, `edges` and `adj` provide access to data attributes
+    via lookup (e.g. `nodes[n]`, `edges[u, v, k]`, `adj[u][v]`) and iteration
+    (e.g. `nodes.items()`, `nodes.data('color')`,
+    `nodes.data('color', default='blue')` and similarly for `edges`)
+    Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
+
+    For details on these and other miscellaneous methods, see below.
+
+    **Subclasses (Advanced):**
+
+    The MultiDiGraph class uses a dict-of-dict-of-dict-of-dict structure.
+    The outer dict (node_dict) holds adjacency information keyed by node.
+    The next dict (adjlist_dict) represents the adjacency information
+    and holds edge_key dicts keyed by neighbor. The edge_key dict holds
+    each edge_attr dict keyed by edge key. The inner dict
+    (edge_attr_dict) represents the edge data and holds edge attribute
+    values keyed by attribute names.
+
+    Each of these four dicts in the dict-of-dict-of-dict-of-dict
+    structure can be replaced by a user defined dict-like object.
+    In general, the dict-like features should be maintained but
+    extra features can be added. To replace one of the dicts create
+    a new graph class by changing the class(!) variable holding the
+    factory for that dict-like structure. The variable names are
+    node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,
+    adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory
+    and graph_attr_dict_factory.
+
+    node_dict_factory : function, (default: dict)
+        Factory function to be used to create the dict containing node
+        attributes, keyed by node id.
+        It should require no arguments and return a dict-like object
+
+    node_attr_dict_factory: function, (default: dict)
+        Factory function to be used to create the node attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object
+
+    adjlist_outer_dict_factory : function, (default: dict)
+        Factory function to be used to create the outer-most dict
+        in the data structure that holds adjacency info keyed by node.
+        It should require no arguments and return a dict-like object.
+
+    adjlist_inner_dict_factory : function, (default: dict)
+        Factory function to be used to create the adjacency list
+        dict which holds multiedge key dicts keyed by neighbor.
+        It should require no arguments and return a dict-like object.
+
+    edge_key_dict_factory : function, (default: dict)
+        Factory function to be used to create the edge key dict
+        which holds edge data keyed by edge key.
+        It should require no arguments and return a dict-like object.
+
+    edge_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the edge attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    graph_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the graph attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    Typically, if your extension doesn't impact the data structure all
+    methods will inherited without issue except: `to_directed/to_undirected`.
+    By default these methods create a DiGraph/Graph class and you probably
+    want them to create your extension of a DiGraph/Graph. To facilitate
+    this we define two class variables that you can set in your subclass.
+
+    to_directed_class : callable, (default: DiGraph or MultiDiGraph)
+        Class to create a new graph structure in the `to_directed` method.
+        If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
+
+    to_undirected_class : callable, (default: Graph or MultiGraph)
+        Class to create a new graph structure in the `to_undirected` method.
+        If `None`, a NetworkX class (Graph or MultiGraph) is used.
+
+    **Subclassing Example**
+
+    Create a low memory graph class that effectively disallows edge
+    attributes by using a single attribute dict for all edges.
+    This reduces the memory used, but you lose edge attributes.
+
+    >>> class ThinGraph(nx.Graph):
+    ...     all_edge_dict = {"weight": 1}
+    ...
+    ...     def single_edge_dict(self):
+    ...         return self.all_edge_dict
+    ...
+    ...     edge_attr_dict_factory = single_edge_dict
+    >>> G = ThinGraph()
+    >>> G.add_edge(2, 1)
+    >>> G[2][1]
+    {'weight': 1}
+    >>> G.add_edge(2, 2)
+    >>> G[2][1] is G[2][2]
+    True
+    """
+
+    # node_dict_factory = dict    # already assigned in Graph
+    # adjlist_outer_dict_factory = dict
+    # adjlist_inner_dict_factory = dict
+    edge_key_dict_factory = dict
+    # edge_attr_dict_factory = dict
+
+    def __init__(self, incoming_graph_data=None, multigraph_input=None, **attr):
+        """Initialize a graph with edges, name, or graph attributes.
+
+        Parameters
+        ----------
+        incoming_graph_data : input graph
+            Data to initialize graph.  If incoming_graph_data=None (default)
+            an empty graph is created.  The data can be an edge list, or any
+            NetworkX graph object.  If the corresponding optional Python
+            packages are installed the data can also be a 2D NumPy array, a
+            SciPy sparse array, or a PyGraphviz graph.
+
+        multigraph_input : bool or None (default None)
+            Note: Only used when `incoming_graph_data` is a dict.
+            If True, `incoming_graph_data` is assumed to be a
+            dict-of-dict-of-dict-of-dict structure keyed by
+            node to neighbor to edge keys to edge data for multi-edges.
+            A NetworkXError is raised if this is not the case.
+            If False, :func:`to_networkx_graph` is used to try to determine
+            the dict's graph data structure as either a dict-of-dict-of-dict
+            keyed by node to neighbor to edge data, or a dict-of-iterable
+            keyed by node to neighbors.
+            If None, the treatment for True is tried, but if it fails,
+            the treatment for False is tried.
+
+        attr : keyword arguments, optional (default= no attributes)
+            Attributes to add to graph as key=value pairs.
+
+        See Also
+        --------
+        convert
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G = nx.Graph(name="my graph")
+        >>> e = [(1, 2), (2, 3), (3, 4)]  # list of edges
+        >>> G = nx.Graph(e)
+
+        Arbitrary graph attribute pairs (key=value) may be assigned
+
+        >>> G = nx.Graph(e, day="Friday")
+        >>> G.graph
+        {'day': 'Friday'}
+
+        """
+        # multigraph_input can be None/True/False. So check "is not False"
+        if isinstance(incoming_graph_data, dict) and multigraph_input is not False:
+            DiGraph.__init__(self)
+            try:
+                convert.from_dict_of_dicts(
+                    incoming_graph_data, create_using=self, multigraph_input=True
+                )
+                self.graph.update(attr)
+            except Exception as err:
+                if multigraph_input is True:
+                    raise nx.NetworkXError(
+                        f"converting multigraph_input raised:\n{type(err)}: {err}"
+                    )
+                DiGraph.__init__(self, incoming_graph_data, **attr)
+        else:
+            DiGraph.__init__(self, incoming_graph_data, **attr)
+
+    @cached_property
+    def adj(self):
+        """Graph adjacency object holding the neighbors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edgekey-dict.  So `G.adj[3][2][0]['color'] = 'blue'` sets
+        the color of the edge `(3, 2, 0)` to `"blue"`.
+
+        Iterating over G.adj behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.adj[n].items():`.
+
+        The neighbor information is also provided by subscripting the graph.
+        So `for nbr, foovalue in G[node].data('foo', default=1):` works.
+
+        For directed graphs, `G.adj` holds outgoing (successor) info.
+        """
+        return MultiAdjacencyView(self._succ)
+
+    @cached_property
+    def succ(self):
+        """Graph adjacency object holding the successors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edgekey-dict.  So `G.adj[3][2][0]['color'] = 'blue'` sets
+        the color of the edge `(3, 2, 0)` to `"blue"`.
+
+        Iterating over G.adj behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.adj[n].items():`.
+
+        The neighbor information is also provided by subscripting the graph.
+        So `for nbr, foovalue in G[node].data('foo', default=1):` works.
+
+        For directed graphs, `G.succ` is identical to `G.adj`.
+        """
+        return MultiAdjacencyView(self._succ)
+
+    @cached_property
+    def pred(self):
+        """Graph adjacency object holding the predecessors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edgekey-dict.  So `G.adj[3][2][0]['color'] = 'blue'` sets
+        the color of the edge `(3, 2, 0)` to `"blue"`.
+
+        Iterating over G.adj behaves like a dict. Useful idioms include
+        `for nbr, datadict in G.adj[n].items():`.
+        """
+        return MultiAdjacencyView(self._pred)
+
+    def add_edge(self, u_for_edge, v_for_edge, key=None, **attr):
+        """Add an edge between u and v.
+
+        The nodes u and v will be automatically added if they are
+        not already in the graph.
+
+        Edge attributes can be specified with keywords or by directly
+        accessing the edge's attribute dictionary. See examples below.
+
+        Parameters
+        ----------
+        u_for_edge, v_for_edge : nodes
+            Nodes can be, for example, strings or numbers.
+            Nodes must be hashable (and not None) Python objects.
+        key : hashable identifier, optional (default=lowest unused integer)
+            Used to distinguish multiedges between a pair of nodes.
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        Returns
+        -------
+        The edge key assigned to the edge.
+
+        See Also
+        --------
+        add_edges_from : add a collection of edges
+
+        Notes
+        -----
+        To replace/update edge data, use the optional key argument
+        to identify a unique edge.  Otherwise a new edge will be created.
+
+        NetworkX algorithms designed for weighted graphs cannot use
+        multigraphs directly because it is not clear how to handle
+        multiedge weights.  Convert to Graph using edge attribute
+        'weight' to enable weighted graph algorithms.
+
+        Default keys are generated using the method `new_edge_key()`.
+        This method can be overridden by subclassing the base class and
+        providing a custom `new_edge_key()` method.
+
+        Examples
+        --------
+        The following all add the edge e=(1, 2) to graph G:
+
+        >>> G = nx.MultiDiGraph()
+        >>> e = (1, 2)
+        >>> key = G.add_edge(1, 2)  # explicit two-node form
+        >>> G.add_edge(*e)  # single edge as tuple of two nodes
+        1
+        >>> G.add_edges_from([(1, 2)])  # add edges from iterable container
+        [2]
+
+        Associate data to edges using keywords:
+
+        >>> key = G.add_edge(1, 2, weight=3)
+        >>> key = G.add_edge(1, 2, key=0, weight=4)  # update data for key=0
+        >>> key = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
+
+        For non-string attribute keys, use subscript notation.
+
+        >>> ekey = G.add_edge(1, 2)
+        >>> G[1][2][0].update({0: 5})
+        >>> G.edges[1, 2, 0].update({0: 5})
+        """
+        u, v = u_for_edge, v_for_edge
+        # add nodes
+        if u not in self._succ:
+            if u is None:
+                raise ValueError("None cannot be a node")
+            self._succ[u] = self.adjlist_inner_dict_factory()
+            self._pred[u] = self.adjlist_inner_dict_factory()
+            self._node[u] = self.node_attr_dict_factory()
+        if v not in self._succ:
+            if v is None:
+                raise ValueError("None cannot be a node")
+            self._succ[v] = self.adjlist_inner_dict_factory()
+            self._pred[v] = self.adjlist_inner_dict_factory()
+            self._node[v] = self.node_attr_dict_factory()
+        if key is None:
+            key = self.new_edge_key(u, v)
+        if v in self._succ[u]:
+            keydict = self._adj[u][v]
+            datadict = keydict.get(key, self.edge_attr_dict_factory())
+            datadict.update(attr)
+            keydict[key] = datadict
+        else:
+            # selfloops work this way without special treatment
+            datadict = self.edge_attr_dict_factory()
+            datadict.update(attr)
+            keydict = self.edge_key_dict_factory()
+            keydict[key] = datadict
+            self._succ[u][v] = keydict
+            self._pred[v][u] = keydict
+        nx._clear_cache(self)
+        return key
+
+    def remove_edge(self, u, v, key=None):
+        """Remove an edge between u and v.
+
+        Parameters
+        ----------
+        u, v : nodes
+            Remove an edge between nodes u and v.
+        key : hashable identifier, optional (default=None)
+            Used to distinguish multiple edges between a pair of nodes.
+            If None, remove a single edge between u and v. If there are
+            multiple edges, removes the last edge added in terms of
+            insertion order.
+
+        Raises
+        ------
+        NetworkXError
+            If there is not an edge between u and v, or
+            if there is no edge with the specified key.
+
+        See Also
+        --------
+        remove_edges_from : remove a collection of edges
+
+        Examples
+        --------
+        >>> G = nx.MultiDiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.remove_edge(0, 1)
+        >>> e = (1, 2)
+        >>> G.remove_edge(*e)  # unpacks e from an edge tuple
+
+        For multiple edges
+
+        >>> G = nx.MultiDiGraph()
+        >>> G.add_edges_from([(1, 2), (1, 2), (1, 2)])  # key_list returned
+        [0, 1, 2]
+
+        When ``key=None`` (the default), edges are removed in the opposite
+        order that they were added:
+
+        >>> G.remove_edge(1, 2)
+        >>> G.edges(keys=True)
+        OutMultiEdgeView([(1, 2, 0), (1, 2, 1)])
+
+        For edges with keys
+
+        >>> G = nx.MultiDiGraph()
+        >>> G.add_edge(1, 2, key="first")
+        'first'
+        >>> G.add_edge(1, 2, key="second")
+        'second'
+        >>> G.remove_edge(1, 2, key="first")
+        >>> G.edges(keys=True)
+        OutMultiEdgeView([(1, 2, 'second')])
+
+        """
+        try:
+            d = self._adj[u][v]
+        except KeyError as err:
+            raise NetworkXError(f"The edge {u}-{v} is not in the graph.") from err
+        # remove the edge with specified data
+        if key is None:
+            d.popitem()
+        else:
+            try:
+                del d[key]
+            except KeyError as err:
+                msg = f"The edge {u}-{v} with key {key} is not in the graph."
+                raise NetworkXError(msg) from err
+        if len(d) == 0:
+            # remove the key entries if last edge
+            del self._succ[u][v]
+            del self._pred[v][u]
+        nx._clear_cache(self)
+
+    @cached_property
+    def edges(self):
+        """An OutMultiEdgeView of the Graph as G.edges or G.edges().
+
+        edges(self, nbunch=None, data=False, keys=False, default=None)
+
+        The OutMultiEdgeView provides set-like operations on the edge-tuples
+        as well as edge attribute lookup. When called, it also provides
+        an EdgeDataView object which allows control of access to edge
+        attributes (but does not provide set-like operations).
+        Hence, ``G.edges[u, v, k]['color']`` provides the value of the color
+        attribute for the edge from ``u`` to ``v`` with key ``k`` while
+        ``for (u, v, k, c) in G.edges(data='color', default='red', keys=True):``
+        iterates through all the edges yielding the color attribute with
+        default `'red'` if no color attribute exists.
+
+        Edges are returned as tuples with optional data and keys
+        in the order (node, neighbor, key, data). If ``keys=True`` is not
+        provided, the tuples will just be (node, neighbor, data), but
+        multiple tuples with the same node and neighbor will be
+        generated when multiple edges between two nodes exist.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges from these nodes.
+        data : string or bool, optional (default=False)
+            The edge attribute returned in 3-tuple (u, v, ddict[data]).
+            If True, return edge attribute dict in 3-tuple (u, v, ddict).
+            If False, return 2-tuple (u, v).
+        keys : bool, optional (default=False)
+            If True, return edge keys with each edge, creating (u, v, k,
+            d) tuples when data is also requested (the default) and (u,
+            v, k) tuples when data is not requested.
+        default : value, optional (default=None)
+            Value used for edges that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        edges : OutMultiEdgeView
+            A view of edge attributes, usually it iterates over (u, v)
+            (u, v, k) or (u, v, k, d) tuples of edges, but can also be
+            used for attribute lookup as ``edges[u, v, k]['foo']``.
+
+        Notes
+        -----
+        Nodes in nbunch that are not in the graph will be (quietly) ignored.
+        For directed graphs this returns the out-edges.
+
+        Examples
+        --------
+        >>> G = nx.MultiDiGraph()
+        >>> nx.add_path(G, [0, 1, 2])
+        >>> key = G.add_edge(2, 3, weight=5)
+        >>> key2 = G.add_edge(1, 2)  # second edge between these nodes
+        >>> [e for e in G.edges()]
+        [(0, 1), (1, 2), (1, 2), (2, 3)]
+        >>> list(G.edges(data=True))  # default data is {} (empty dict)
+        [(0, 1, {}), (1, 2, {}), (1, 2, {}), (2, 3, {'weight': 5})]
+        >>> list(G.edges(data="weight", default=1))
+        [(0, 1, 1), (1, 2, 1), (1, 2, 1), (2, 3, 5)]
+        >>> list(G.edges(keys=True))  # default keys are integers
+        [(0, 1, 0), (1, 2, 0), (1, 2, 1), (2, 3, 0)]
+        >>> list(G.edges(data=True, keys=True))
+        [(0, 1, 0, {}), (1, 2, 0, {}), (1, 2, 1, {}), (2, 3, 0, {'weight': 5})]
+        >>> list(G.edges(data="weight", default=1, keys=True))
+        [(0, 1, 0, 1), (1, 2, 0, 1), (1, 2, 1, 1), (2, 3, 0, 5)]
+        >>> list(G.edges([0, 2]))
+        [(0, 1), (2, 3)]
+        >>> list(G.edges(0))
+        [(0, 1)]
+        >>> list(G.edges(1))
+        [(1, 2), (1, 2)]
+
+        See Also
+        --------
+        in_edges, out_edges
+        """
+        return OutMultiEdgeView(self)
+
+    # alias out_edges to edges
+    @cached_property
+    def out_edges(self):
+        return OutMultiEdgeView(self)
+
+    out_edges.__doc__ = edges.__doc__
+
+    @cached_property
+    def in_edges(self):
+        """A view of the in edges of the graph as G.in_edges or G.in_edges().
+
+        in_edges(self, nbunch=None, data=False, keys=False, default=None)
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+        data : string or bool, optional (default=False)
+            The edge attribute returned in 3-tuple (u, v, ddict[data]).
+            If True, return edge attribute dict in 3-tuple (u, v, ddict).
+            If False, return 2-tuple (u, v).
+        keys : bool, optional (default=False)
+            If True, return edge keys with each edge, creating 3-tuples
+            (u, v, k) or with data, 4-tuples (u, v, k, d).
+        default : value, optional (default=None)
+            Value used for edges that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        in_edges : InMultiEdgeView or InMultiEdgeDataView
+            A view of edge attributes, usually it iterates over (u, v)
+            or (u, v, k) or (u, v, k, d) tuples of edges, but can also be
+            used for attribute lookup as `edges[u, v, k]['foo']`.
+
+        See Also
+        --------
+        edges
+        """
+        return InMultiEdgeView(self)
+
+    @cached_property
+    def degree(self):
+        """A DegreeView for the Graph as G.degree or G.degree().
+
+        The node degree is the number of edges adjacent to the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iterator for (node, degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The name of an edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        DiMultiDegreeView or int
+            If multiple nodes are requested (the default), returns a `DiMultiDegreeView`
+            mapping nodes to their degree.
+            If a single node is requested, returns the degree of the node as an integer.
+
+        See Also
+        --------
+        out_degree, in_degree
+
+        Examples
+        --------
+        >>> G = nx.MultiDiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.degree(0)  # node 0 with degree 1
+        1
+        >>> list(G.degree([0, 1, 2]))
+        [(0, 1), (1, 2), (2, 2)]
+        >>> G.add_edge(0, 1)  # parallel edge
+        1
+        >>> list(G.degree([0, 1, 2]))  # parallel edges are counted
+        [(0, 2), (1, 3), (2, 2)]
+
+        """
+        return DiMultiDegreeView(self)
+
+    @cached_property
+    def in_degree(self):
+        """A DegreeView for (node, in_degree) or in_degree for single node.
+
+        The node in-degree is the number of edges pointing into the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iterator for (node, degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        If a single node is requested
+        deg : int
+            Degree of the node
+
+        OR if multiple nodes are requested
+        nd_iter : iterator
+            The iterator returns two-tuples of (node, in-degree).
+
+        See Also
+        --------
+        degree, out_degree
+
+        Examples
+        --------
+        >>> G = nx.MultiDiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.in_degree(0)  # node 0 with degree 0
+        0
+        >>> list(G.in_degree([0, 1, 2]))
+        [(0, 0), (1, 1), (2, 1)]
+        >>> G.add_edge(0, 1)  # parallel edge
+        1
+        >>> list(G.in_degree([0, 1, 2]))  # parallel edges counted
+        [(0, 0), (1, 2), (2, 1)]
+
+        """
+        return InMultiDegreeView(self)
+
+    @cached_property
+    def out_degree(self):
+        """Returns an iterator for (node, out-degree) or out-degree for single node.
+
+        out_degree(self, nbunch=None, weight=None)
+
+        The node out-degree is the number of edges pointing out of the node.
+        This function returns the out-degree for a single node or an iterator
+        for a bunch of nodes or if nothing is passed as argument.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights.
+
+        Returns
+        -------
+        If a single node is requested
+        deg : int
+            Degree of the node
+
+        OR if multiple nodes are requested
+        nd_iter : iterator
+            The iterator returns two-tuples of (node, out-degree).
+
+        See Also
+        --------
+        degree, in_degree
+
+        Examples
+        --------
+        >>> G = nx.MultiDiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.out_degree(0)  # node 0 with degree 1
+        1
+        >>> list(G.out_degree([0, 1, 2]))
+        [(0, 1), (1, 1), (2, 1)]
+        >>> G.add_edge(0, 1)  # parallel edge
+        1
+        >>> list(G.out_degree([0, 1, 2]))  # counts parallel edges
+        [(0, 2), (1, 1), (2, 1)]
+
+        """
+        return OutMultiDegreeView(self)
+
+    def is_multigraph(self):
+        """Returns True if graph is a multigraph, False otherwise."""
+        return True
+
+    def is_directed(self):
+        """Returns True if graph is directed, False otherwise."""
+        return True
+
+    def to_undirected(self, reciprocal=False, as_view=False):
+        """Returns an undirected representation of the digraph.
+
+        Parameters
+        ----------
+        reciprocal : bool (optional)
+          If True only keep edges that appear in both directions
+          in the original digraph.
+        as_view : bool (optional, default=False)
+          If True return an undirected view of the original directed graph.
+
+        Returns
+        -------
+        G : MultiGraph
+            An undirected graph with the same name and nodes and
+            with edge (u, v, data) if either (u, v, data) or (v, u, data)
+            is in the digraph.  If both edges exist in digraph and
+            their edge data is different, only one edge is created
+            with an arbitrary choice of which edge data to use.
+            You must check and correct for this manually if desired.
+
+        See Also
+        --------
+        MultiGraph, copy, add_edge, add_edges_from
+
+        Notes
+        -----
+        This returns a "deepcopy" of the edge, node, and
+        graph attributes which attempts to completely copy
+        all of the data and references.
+
+        This is in contrast to the similar D=MultiDiGraph(G) which
+        returns a shallow copy of the data.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Warning: If you have subclassed MultiDiGraph to use dict-like
+        objects in the data structure, those changes do not transfer
+        to the MultiGraph created by this method.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(2)  # or MultiGraph, etc
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1), (1, 0)]
+        >>> G2 = H.to_undirected()
+        >>> list(G2.edges)
+        [(0, 1)]
+        """
+        graph_class = self.to_undirected_class()
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self, graph_class)
+        # deepcopy when not a view
+        G = graph_class()
+        G.graph.update(deepcopy(self.graph))
+        G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+        if reciprocal is True:
+            G.add_edges_from(
+                (u, v, key, deepcopy(data))
+                for u, nbrs in self._adj.items()
+                for v, keydict in nbrs.items()
+                for key, data in keydict.items()
+                if v in self._pred[u] and key in self._pred[u][v]
+            )
+        else:
+            G.add_edges_from(
+                (u, v, key, deepcopy(data))
+                for u, nbrs in self._adj.items()
+                for v, keydict in nbrs.items()
+                for key, data in keydict.items()
+            )
+        return G
+
+    def reverse(self, copy=True):
+        """Returns the reverse of the graph.
+
+        The reverse is a graph with the same nodes and edges
+        but with the directions of the edges reversed.
+
+        Parameters
+        ----------
+        copy : bool optional (default=True)
+            If True, return a new DiGraph holding the reversed edges.
+            If False, the reverse graph is created using a view of
+            the original graph.
+        """
+        if copy:
+            H = self.__class__()
+            H.graph.update(deepcopy(self.graph))
+            H.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+            H.add_edges_from(
+                (v, u, k, deepcopy(d))
+                for u, v, k, d in self.edges(keys=True, data=True)
+            )
+            return H
+        return nx.reverse_view(self)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/multigraph.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/multigraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e3f1aecdd3ba76ecaa746af7f5b487b0fc7c7f3
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/multigraph.py
@@ -0,0 +1,1283 @@
+"""Base class for MultiGraph."""
+
+from copy import deepcopy
+from functools import cached_property
+
+import networkx as nx
+from networkx import NetworkXError, convert
+from networkx.classes.coreviews import MultiAdjacencyView
+from networkx.classes.graph import Graph
+from networkx.classes.reportviews import MultiDegreeView, MultiEdgeView
+
+__all__ = ["MultiGraph"]
+
+
+class MultiGraph(Graph):
+    """
+    An undirected graph class that can store multiedges.
+
+    Multiedges are multiple edges between two nodes.  Each edge
+    can hold optional data or attributes.
+
+    A MultiGraph holds undirected edges.  Self loops are allowed.
+
+    Nodes can be arbitrary (hashable) Python objects with optional
+    key/value attributes. By convention `None` is not used as a node.
+
+    Edges are represented as links between nodes with optional
+    key/value attributes, in a MultiGraph each edge has a key to
+    distinguish between multiple edges that have the same source and
+    destination nodes.
+
+    Parameters
+    ----------
+    incoming_graph_data : input graph (optional, default: None)
+        Data to initialize graph. If None (default) an empty
+        graph is created.  The data can be any format that is supported
+        by the to_networkx_graph() function, currently including edge list,
+        dict of dicts, dict of lists, NetworkX graph, 2D NumPy array,
+        SciPy sparse array, or PyGraphviz graph.
+
+    multigraph_input : bool or None (default None)
+        Note: Only used when `incoming_graph_data` is a dict.
+        If True, `incoming_graph_data` is assumed to be a
+        dict-of-dict-of-dict-of-dict structure keyed by
+        node to neighbor to edge keys to edge data for multi-edges.
+        A NetworkXError is raised if this is not the case.
+        If False, :func:`to_networkx_graph` is used to try to determine
+        the dict's graph data structure as either a dict-of-dict-of-dict
+        keyed by node to neighbor to edge data, or a dict-of-iterable
+        keyed by node to neighbors.
+        If None, the treatment for True is tried, but if it fails,
+        the treatment for False is tried.
+
+    attr : keyword arguments, optional (default= no attributes)
+        Attributes to add to graph as key=value pairs.
+
+    See Also
+    --------
+    Graph
+    DiGraph
+    MultiDiGraph
+
+    Examples
+    --------
+    Create an empty graph structure (a "null graph") with no nodes and
+    no edges.
+
+    >>> G = nx.MultiGraph()
+
+    G can be grown in several ways.
+
+    **Nodes:**
+
+    Add one node at a time:
+
+    >>> G.add_node(1)
+
+    Add the nodes from any container (a list, dict, set or
+    even the lines from a file or the nodes from another graph).
+
+    >>> G.add_nodes_from([2, 3])
+    >>> G.add_nodes_from(range(100, 110))
+    >>> H = nx.path_graph(10)
+    >>> G.add_nodes_from(H)
+
+    In addition to strings and integers any hashable Python object
+    (except None) can represent a node, e.g. a customized node object,
+    or even another Graph.
+
+    >>> G.add_node(H)
+
+    **Edges:**
+
+    G can also be grown by adding edges.
+
+    Add one edge,
+
+    >>> key = G.add_edge(1, 2)
+
+    a list of edges,
+
+    >>> keys = G.add_edges_from([(1, 2), (1, 3)])
+
+    or a collection of edges,
+
+    >>> keys = G.add_edges_from(H.edges)
+
+    If some edges connect nodes not yet in the graph, the nodes
+    are added automatically.  If an edge already exists, an additional
+    edge is created and stored using a key to identify the edge.
+    By default the key is the lowest unused integer.
+
+    >>> keys = G.add_edges_from([(4, 5, {"route": 28}), (4, 5, {"route": 37})])
+    >>> G[4]
+    AdjacencyView({3: {0: {}}, 5: {0: {}, 1: {'route': 28}, 2: {'route': 37}}})
+
+    **Attributes:**
+
+    Each graph, node, and edge can hold key/value attribute pairs
+    in an associated attribute dictionary (the keys must be hashable).
+    By default these are empty, but can be added or changed using
+    add_edge, add_node or direct manipulation of the attribute
+    dictionaries named graph, node and edge respectively.
+
+    >>> G = nx.MultiGraph(day="Friday")
+    >>> G.graph
+    {'day': 'Friday'}
+
+    Add node attributes using add_node(), add_nodes_from() or G.nodes
+
+    >>> G.add_node(1, time="5pm")
+    >>> G.add_nodes_from([3], time="2pm")
+    >>> G.nodes[1]
+    {'time': '5pm'}
+    >>> G.nodes[1]["room"] = 714
+    >>> del G.nodes[1]["room"]  # remove attribute
+    >>> list(G.nodes(data=True))
+    [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
+
+    Add edge attributes using add_edge(), add_edges_from(), subscript
+    notation, or G.edges.
+
+    >>> key = G.add_edge(1, 2, weight=4.7)
+    >>> keys = G.add_edges_from([(3, 4), (4, 5)], color="red")
+    >>> keys = G.add_edges_from([(1, 2, {"color": "blue"}), (2, 3, {"weight": 8})])
+    >>> G[1][2][0]["weight"] = 4.7
+    >>> G.edges[1, 2, 0]["weight"] = 4
+
+    Warning: we protect the graph data structure by making `G.edges[1,
+    2, 0]` a read-only dict-like structure. However, you can assign to
+    attributes in e.g. `G.edges[1, 2, 0]`. Thus, use 2 sets of brackets
+    to add/change data attributes: `G.edges[1, 2, 0]['weight'] = 4`.
+
+    **Shortcuts:**
+
+    Many common graph features allow python syntax to speed reporting.
+
+    >>> 1 in G  # check if node in graph
+    True
+    >>> [n for n in G if n < 3]  # iterate through nodes
+    [1, 2]
+    >>> len(G)  # number of nodes in graph
+    5
+    >>> G[1]  # adjacency dict-like view mapping neighbor -> edge key -> edge attributes
+    AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}})
+
+    Often the best way to traverse all edges of a graph is via the neighbors.
+    The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`.
+
+    >>> for n, nbrsdict in G.adjacency():
+    ...     for nbr, keydict in nbrsdict.items():
+    ...         for key, eattr in keydict.items():
+    ...             if "weight" in eattr:
+    ...                 # Do something useful with the edges
+    ...                 pass
+
+    But the edges() method is often more convenient:
+
+    >>> for u, v, keys, weight in G.edges(data="weight", keys=True):
+    ...     if weight is not None:
+    ...         # Do something useful with the edges
+    ...         pass
+
+    **Reporting:**
+
+    Simple graph information is obtained using methods and object-attributes.
+    Reporting usually provides views instead of containers to reduce memory
+    usage. The views update as the graph is updated similarly to dict-views.
+    The objects `nodes`, `edges` and `adj` provide access to data attributes
+    via lookup (e.g. `nodes[n]`, `edges[u, v, k]`, `adj[u][v]`) and iteration
+    (e.g. `nodes.items()`, `nodes.data('color')`,
+    `nodes.data('color', default='blue')` and similarly for `edges`)
+    Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
+
+    For details on these and other miscellaneous methods, see below.
+
+    **Subclasses (Advanced):**
+
+    The MultiGraph class uses a dict-of-dict-of-dict-of-dict data structure.
+    The outer dict (node_dict) holds adjacency information keyed by node.
+    The next dict (adjlist_dict) represents the adjacency information
+    and holds edge_key dicts keyed by neighbor. The edge_key dict holds
+    each edge_attr dict keyed by edge key. The inner dict
+    (edge_attr_dict) represents the edge data and holds edge attribute
+    values keyed by attribute names.
+
+    Each of these four dicts in the dict-of-dict-of-dict-of-dict
+    structure can be replaced by a user defined dict-like object.
+    In general, the dict-like features should be maintained but
+    extra features can be added. To replace one of the dicts create
+    a new graph class by changing the class(!) variable holding the
+    factory for that dict-like structure. The variable names are
+    node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,
+    adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory
+    and graph_attr_dict_factory.
+
+    node_dict_factory : function, (default: dict)
+        Factory function to be used to create the dict containing node
+        attributes, keyed by node id.
+        It should require no arguments and return a dict-like object
+
+    node_attr_dict_factory: function, (default: dict)
+        Factory function to be used to create the node attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object
+
+    adjlist_outer_dict_factory : function, (default: dict)
+        Factory function to be used to create the outer-most dict
+        in the data structure that holds adjacency info keyed by node.
+        It should require no arguments and return a dict-like object.
+
+    adjlist_inner_dict_factory : function, (default: dict)
+        Factory function to be used to create the adjacency list
+        dict which holds multiedge key dicts keyed by neighbor.
+        It should require no arguments and return a dict-like object.
+
+    edge_key_dict_factory : function, (default: dict)
+        Factory function to be used to create the edge key dict
+        which holds edge data keyed by edge key.
+        It should require no arguments and return a dict-like object.
+
+    edge_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the edge attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    graph_attr_dict_factory : function, (default: dict)
+        Factory function to be used to create the graph attribute
+        dict which holds attribute values keyed by attribute name.
+        It should require no arguments and return a dict-like object.
+
+    Typically, if your extension doesn't impact the data structure all
+    methods will inherited without issue except: `to_directed/to_undirected`.
+    By default these methods create a DiGraph/Graph class and you probably
+    want them to create your extension of a DiGraph/Graph. To facilitate
+    this we define two class variables that you can set in your subclass.
+
+    to_directed_class : callable, (default: DiGraph or MultiDiGraph)
+        Class to create a new graph structure in the `to_directed` method.
+        If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
+
+    to_undirected_class : callable, (default: Graph or MultiGraph)
+        Class to create a new graph structure in the `to_undirected` method.
+        If `None`, a NetworkX class (Graph or MultiGraph) is used.
+
+    **Subclassing Example**
+
+    Create a low memory graph class that effectively disallows edge
+    attributes by using a single attribute dict for all edges.
+    This reduces the memory used, but you lose edge attributes.
+
+    >>> class ThinGraph(nx.Graph):
+    ...     all_edge_dict = {"weight": 1}
+    ...
+    ...     def single_edge_dict(self):
+    ...         return self.all_edge_dict
+    ...
+    ...     edge_attr_dict_factory = single_edge_dict
+    >>> G = ThinGraph()
+    >>> G.add_edge(2, 1)
+    >>> G[2][1]
+    {'weight': 1}
+    >>> G.add_edge(2, 2)
+    >>> G[2][1] is G[2][2]
+    True
+    """
+
+    # node_dict_factory = dict    # already assigned in Graph
+    # adjlist_outer_dict_factory = dict
+    # adjlist_inner_dict_factory = dict
+    edge_key_dict_factory = dict
+    # edge_attr_dict_factory = dict
+
+    def to_directed_class(self):
+        """Returns the class to use for empty directed copies.
+
+        If you subclass the base classes, use this to designate
+        what directed class to use for `to_directed()` copies.
+        """
+        return nx.MultiDiGraph
+
+    def to_undirected_class(self):
+        """Returns the class to use for empty undirected copies.
+
+        If you subclass the base classes, use this to designate
+        what directed class to use for `to_directed()` copies.
+        """
+        return MultiGraph
+
+    def __init__(self, incoming_graph_data=None, multigraph_input=None, **attr):
+        """Initialize a graph with edges, name, or graph attributes.
+
+        Parameters
+        ----------
+        incoming_graph_data : input graph
+            Data to initialize graph.  If incoming_graph_data=None (default)
+            an empty graph is created.  The data can be an edge list, or any
+            NetworkX graph object.  If the corresponding optional Python
+            packages are installed the data can also be a 2D NumPy array, a
+            SciPy sparse array, or a PyGraphviz graph.
+
+        multigraph_input : bool or None (default None)
+            Note: Only used when `incoming_graph_data` is a dict.
+            If True, `incoming_graph_data` is assumed to be a
+            dict-of-dict-of-dict-of-dict structure keyed by
+            node to neighbor to edge keys to edge data for multi-edges.
+            A NetworkXError is raised if this is not the case.
+            If False, :func:`to_networkx_graph` is used to try to determine
+            the dict's graph data structure as either a dict-of-dict-of-dict
+            keyed by node to neighbor to edge data, or a dict-of-iterable
+            keyed by node to neighbors.
+            If None, the treatment for True is tried, but if it fails,
+            the treatment for False is tried.
+
+        attr : keyword arguments, optional (default= no attributes)
+            Attributes to add to graph as key=value pairs.
+
+        See Also
+        --------
+        convert
+
+        Examples
+        --------
+        >>> G = nx.MultiGraph()
+        >>> G = nx.MultiGraph(name="my graph")
+        >>> e = [(1, 2), (1, 2), (2, 3), (3, 4)]  # list of edges
+        >>> G = nx.MultiGraph(e)
+
+        Arbitrary graph attribute pairs (key=value) may be assigned
+
+        >>> G = nx.MultiGraph(e, day="Friday")
+        >>> G.graph
+        {'day': 'Friday'}
+
+        """
+        # multigraph_input can be None/True/False. So check "is not False"
+        if isinstance(incoming_graph_data, dict) and multigraph_input is not False:
+            Graph.__init__(self)
+            try:
+                convert.from_dict_of_dicts(
+                    incoming_graph_data, create_using=self, multigraph_input=True
+                )
+                self.graph.update(attr)
+            except Exception as err:
+                if multigraph_input is True:
+                    raise nx.NetworkXError(
+                        f"converting multigraph_input raised:\n{type(err)}: {err}"
+                    )
+                Graph.__init__(self, incoming_graph_data, **attr)
+        else:
+            Graph.__init__(self, incoming_graph_data, **attr)
+
+    @cached_property
+    def adj(self):
+        """Graph adjacency object holding the neighbors of each node.
+
+        This object is a read-only dict-like structure with node keys
+        and neighbor-dict values.  The neighbor-dict is keyed by neighbor
+        to the edgekey-data-dict.  So `G.adj[3][2][0]['color'] = 'blue'` sets
+        the color of the edge `(3, 2, 0)` to `"blue"`.
+
+        Iterating over G.adj behaves like a dict. Useful idioms include
+        `for nbr, edgesdict in G.adj[n].items():`.
+
+        The neighbor information is also provided by subscripting the graph.
+
+        Examples
+        --------
+        >>> e = [(1, 2), (1, 2), (1, 3), (3, 4)]  # list of edges
+        >>> G = nx.MultiGraph(e)
+        >>> G.edges[1, 2, 0]["weight"] = 3
+        >>> result = set()
+        >>> for edgekey, data in G[1][2].items():
+        ...     result.add(data.get("weight", 1))
+        >>> result
+        {1, 3}
+
+        For directed graphs, `G.adj` holds outgoing (successor) info.
+        """
+        return MultiAdjacencyView(self._adj)
+
+    def new_edge_key(self, u, v):
+        """Returns an unused key for edges between nodes `u` and `v`.
+
+        The nodes `u` and `v` do not need to be already in the graph.
+
+        Notes
+        -----
+        In the standard MultiGraph class the new key is the number of existing
+        edges between `u` and `v` (increased if necessary to ensure unused).
+        The first edge will have key 0, then 1, etc. If an edge is removed
+        further new_edge_keys may not be in this order.
+
+        Parameters
+        ----------
+        u, v : nodes
+
+        Returns
+        -------
+        key : int
+        """
+        try:
+            keydict = self._adj[u][v]
+        except KeyError:
+            return 0
+        key = len(keydict)
+        while key in keydict:
+            key += 1
+        return key
+
+    def add_edge(self, u_for_edge, v_for_edge, key=None, **attr):
+        """Add an edge between u and v.
+
+        The nodes u and v will be automatically added if they are
+        not already in the graph.
+
+        Edge attributes can be specified with keywords or by directly
+        accessing the edge's attribute dictionary. See examples below.
+
+        Parameters
+        ----------
+        u_for_edge, v_for_edge : nodes
+            Nodes can be, for example, strings or numbers.
+            Nodes must be hashable (and not None) Python objects.
+        key : hashable identifier, optional (default=lowest unused integer)
+            Used to distinguish multiedges between a pair of nodes.
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        Returns
+        -------
+        The edge key assigned to the edge.
+
+        See Also
+        --------
+        add_edges_from : add a collection of edges
+
+        Notes
+        -----
+        To replace/update edge data, use the optional key argument
+        to identify a unique edge.  Otherwise a new edge will be created.
+
+        NetworkX algorithms designed for weighted graphs cannot use
+        multigraphs directly because it is not clear how to handle
+        multiedge weights.  Convert to Graph using edge attribute
+        'weight' to enable weighted graph algorithms.
+
+        Default keys are generated using the method `new_edge_key()`.
+        This method can be overridden by subclassing the base class and
+        providing a custom `new_edge_key()` method.
+
+        Examples
+        --------
+        The following each add an additional edge e=(1, 2) to graph G:
+
+        >>> G = nx.MultiGraph()
+        >>> e = (1, 2)
+        >>> ekey = G.add_edge(1, 2)  # explicit two-node form
+        >>> G.add_edge(*e)  # single edge as tuple of two nodes
+        1
+        >>> G.add_edges_from([(1, 2)])  # add edges from iterable container
+        [2]
+
+        Associate data to edges using keywords:
+
+        >>> ekey = G.add_edge(1, 2, weight=3)
+        >>> ekey = G.add_edge(1, 2, key=0, weight=4)  # update data for key=0
+        >>> ekey = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
+
+        For non-string attribute keys, use subscript notation.
+
+        >>> ekey = G.add_edge(1, 2)
+        >>> G[1][2][0].update({0: 5})
+        >>> G.edges[1, 2, 0].update({0: 5})
+        """
+        u, v = u_for_edge, v_for_edge
+        # add nodes
+        if u not in self._adj:
+            if u is None:
+                raise ValueError("None cannot be a node")
+            self._adj[u] = self.adjlist_inner_dict_factory()
+            self._node[u] = self.node_attr_dict_factory()
+        if v not in self._adj:
+            if v is None:
+                raise ValueError("None cannot be a node")
+            self._adj[v] = self.adjlist_inner_dict_factory()
+            self._node[v] = self.node_attr_dict_factory()
+        if key is None:
+            key = self.new_edge_key(u, v)
+        if v in self._adj[u]:
+            keydict = self._adj[u][v]
+            datadict = keydict.get(key, self.edge_attr_dict_factory())
+            datadict.update(attr)
+            keydict[key] = datadict
+        else:
+            # selfloops work this way without special treatment
+            datadict = self.edge_attr_dict_factory()
+            datadict.update(attr)
+            keydict = self.edge_key_dict_factory()
+            keydict[key] = datadict
+            self._adj[u][v] = keydict
+            self._adj[v][u] = keydict
+        nx._clear_cache(self)
+        return key
+
+    def add_edges_from(self, ebunch_to_add, **attr):
+        """Add all the edges in ebunch_to_add.
+
+        Parameters
+        ----------
+        ebunch_to_add : container of edges
+            Each edge given in the container will be added to the
+            graph. The edges can be:
+
+                - 2-tuples (u, v) or
+                - 3-tuples (u, v, d) for an edge data dict d, or
+                - 3-tuples (u, v, k) for not iterable key k, or
+                - 4-tuples (u, v, k, d) for an edge with data and key k
+
+        attr : keyword arguments, optional
+            Edge data (or labels or objects) can be assigned using
+            keyword arguments.
+
+        Returns
+        -------
+        A list of edge keys assigned to the edges in `ebunch`.
+
+        See Also
+        --------
+        add_edge : add a single edge
+        add_weighted_edges_from : convenient way to add weighted edges
+
+        Notes
+        -----
+        Adding the same edge twice has no effect but any edge data
+        will be updated when each duplicate edge is added.
+
+        Edge attributes specified in an ebunch take precedence over
+        attributes specified via keyword arguments.
+
+        Default keys are generated using the method ``new_edge_key()``.
+        This method can be overridden by subclassing the base class and
+        providing a custom ``new_edge_key()`` method.
+
+        When adding edges from an iterator over the graph you are changing,
+        a `RuntimeError` can be raised with message:
+        `RuntimeError: dictionary changed size during iteration`. This
+        happens when the graph's underlying dictionary is modified during
+        iteration. To avoid this error, evaluate the iterator into a separate
+        object, e.g. by using `list(iterator_of_edges)`, and pass this
+        object to `G.add_edges_from`.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> G.add_edges_from([(0, 1), (1, 2)])  # using a list of edge tuples
+        >>> e = zip(range(0, 3), range(1, 4))
+        >>> G.add_edges_from(e)  # Add the path graph 0-1-2-3
+
+        Associate data to edges
+
+        >>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
+        >>> G.add_edges_from([(3, 4), (1, 4)], label="WN2898")
+
+        Evaluate an iterator over a graph if using it to modify the same graph
+
+        >>> G = nx.MultiGraph([(1, 2), (2, 3), (3, 4)])
+        >>> # Grow graph by one new node, adding edges to all existing nodes.
+        >>> # wrong way - will raise RuntimeError
+        >>> # G.add_edges_from(((5, n) for n in G.nodes))
+        >>> # right way - note that there will be no self-edge for node 5
+        >>> assigned_keys = G.add_edges_from(list((5, n) for n in G.nodes))
+        """
+        keylist = []
+        for e in ebunch_to_add:
+            ne = len(e)
+            if ne == 4:
+                u, v, key, dd = e
+            elif ne == 3:
+                u, v, dd = e
+                key = None
+            elif ne == 2:
+                u, v = e
+                dd = {}
+                key = None
+            else:
+                msg = f"Edge tuple {e} must be a 2-tuple, 3-tuple or 4-tuple."
+                raise NetworkXError(msg)
+            ddd = {}
+            ddd.update(attr)
+            try:
+                ddd.update(dd)
+            except (TypeError, ValueError):
+                if ne != 3:
+                    raise
+                key = dd  # ne == 3 with 3rd value not dict, must be a key
+            key = self.add_edge(u, v, key)
+            self[u][v][key].update(ddd)
+            keylist.append(key)
+        nx._clear_cache(self)
+        return keylist
+
+    def remove_edge(self, u, v, key=None):
+        """Remove an edge between u and v.
+
+        Parameters
+        ----------
+        u, v : nodes
+            Remove an edge between nodes u and v.
+        key : hashable identifier, optional (default=None)
+            Used to distinguish multiple edges between a pair of nodes.
+            If None, remove a single edge between u and v. If there are
+            multiple edges, removes the last edge added in terms of
+            insertion order.
+
+        Raises
+        ------
+        NetworkXError
+            If there is not an edge between u and v, or
+            if there is no edge with the specified key.
+
+        See Also
+        --------
+        remove_edges_from : remove a collection of edges
+
+        Examples
+        --------
+        >>> G = nx.MultiGraph()
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.remove_edge(0, 1)
+        >>> e = (1, 2)
+        >>> G.remove_edge(*e)  # unpacks e from an edge tuple
+
+        For multiple edges
+
+        >>> G = nx.MultiGraph()  # or MultiDiGraph, etc
+        >>> G.add_edges_from([(1, 2), (1, 2), (1, 2)])  # key_list returned
+        [0, 1, 2]
+
+        When ``key=None`` (the default), edges are removed in the opposite
+        order that they were added:
+
+        >>> G.remove_edge(1, 2)
+        >>> G.edges(keys=True)
+        MultiEdgeView([(1, 2, 0), (1, 2, 1)])
+        >>> G.remove_edge(2, 1)  # edges are not directed
+        >>> G.edges(keys=True)
+        MultiEdgeView([(1, 2, 0)])
+
+        For edges with keys
+
+        >>> G = nx.MultiGraph()
+        >>> G.add_edge(1, 2, key="first")
+        'first'
+        >>> G.add_edge(1, 2, key="second")
+        'second'
+        >>> G.remove_edge(1, 2, key="first")
+        >>> G.edges(keys=True)
+        MultiEdgeView([(1, 2, 'second')])
+
+        """
+        try:
+            d = self._adj[u][v]
+        except KeyError as err:
+            raise NetworkXError(f"The edge {u}-{v} is not in the graph.") from err
+        # remove the edge with specified data
+        if key is None:
+            d.popitem()
+        else:
+            try:
+                del d[key]
+            except KeyError as err:
+                msg = f"The edge {u}-{v} with key {key} is not in the graph."
+                raise NetworkXError(msg) from err
+        if len(d) == 0:
+            # remove the key entries if last edge
+            del self._adj[u][v]
+            if u != v:  # check for selfloop
+                del self._adj[v][u]
+        nx._clear_cache(self)
+
+    def remove_edges_from(self, ebunch):
+        """Remove all edges specified in ebunch.
+
+        Parameters
+        ----------
+        ebunch: list or container of edge tuples
+            Each edge given in the list or container will be removed
+            from the graph. The edges can be:
+
+                - 2-tuples (u, v) A single edge between u and v is removed.
+                - 3-tuples (u, v, key) The edge identified by key is removed.
+                - 4-tuples (u, v, key, data) where data is ignored.
+
+        See Also
+        --------
+        remove_edge : remove a single edge
+
+        Notes
+        -----
+        Will fail silently if an edge in ebunch is not in the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> ebunch = [(1, 2), (2, 3)]
+        >>> G.remove_edges_from(ebunch)
+
+        Removing multiple copies of edges
+
+        >>> G = nx.MultiGraph()
+        >>> keys = G.add_edges_from([(1, 2), (1, 2), (1, 2)])
+        >>> G.remove_edges_from([(1, 2), (2, 1)])  # edges aren't directed
+        >>> list(G.edges())
+        [(1, 2)]
+        >>> G.remove_edges_from([(1, 2), (1, 2)])  # silently ignore extra copy
+        >>> list(G.edges)  # now empty graph
+        []
+
+        When the edge is a 2-tuple ``(u, v)`` but there are multiple edges between
+        u and v in the graph, the most recent edge (in terms of insertion
+        order) is removed.
+
+        >>> G = nx.MultiGraph()
+        >>> for key in ("x", "y", "a"):
+        ...     k = G.add_edge(0, 1, key=key)
+        >>> G.edges(keys=True)
+        MultiEdgeView([(0, 1, 'x'), (0, 1, 'y'), (0, 1, 'a')])
+        >>> G.remove_edges_from([(0, 1)])
+        >>> G.edges(keys=True)
+        MultiEdgeView([(0, 1, 'x'), (0, 1, 'y')])
+
+        """
+        for e in ebunch:
+            try:
+                self.remove_edge(*e[:3])
+            except NetworkXError:
+                pass
+        nx._clear_cache(self)
+
+    def has_edge(self, u, v, key=None):
+        """Returns True if the graph has an edge between nodes u and v.
+
+        This is the same as `v in G[u] or key in G[u][v]`
+        without KeyError exceptions.
+
+        Parameters
+        ----------
+        u, v : nodes
+            Nodes can be, for example, strings or numbers.
+
+        key : hashable identifier, optional (default=None)
+            If specified return True only if the edge with
+            key is found.
+
+        Returns
+        -------
+        edge_ind : bool
+            True if edge is in the graph, False otherwise.
+
+        Examples
+        --------
+        Can be called either using two nodes u, v, an edge tuple (u, v),
+        or an edge tuple (u, v, key).
+
+        >>> G = nx.MultiGraph()  # or MultiDiGraph
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.has_edge(0, 1)  # using two nodes
+        True
+        >>> e = (0, 1)
+        >>> G.has_edge(*e)  #  e is a 2-tuple (u, v)
+        True
+        >>> G.add_edge(0, 1, key="a")
+        'a'
+        >>> G.has_edge(0, 1, key="a")  # specify key
+        True
+        >>> G.has_edge(1, 0, key="a")  # edges aren't directed
+        True
+        >>> e = (0, 1, "a")
+        >>> G.has_edge(*e)  # e is a 3-tuple (u, v, 'a')
+        True
+
+        The following syntax are equivalent:
+
+        >>> G.has_edge(0, 1)
+        True
+        >>> 1 in G[0]  # though this gives :exc:`KeyError` if 0 not in G
+        True
+        >>> 0 in G[1]  # other order; also gives :exc:`KeyError` if 0 not in G
+        True
+
+        """
+        try:
+            if key is None:
+                return v in self._adj[u]
+            else:
+                return key in self._adj[u][v]
+        except KeyError:
+            return False
+
+    @cached_property
+    def edges(self):
+        """Returns an iterator over the edges.
+
+        edges(self, nbunch=None, data=False, keys=False, default=None)
+
+        The MultiEdgeView provides set-like operations on the edge-tuples
+        as well as edge attribute lookup. When called, it also provides
+        an EdgeDataView object which allows control of access to edge
+        attributes (but does not provide set-like operations).
+        Hence, ``G.edges[u, v, k]['color']`` provides the value of the color
+        attribute for the edge from ``u`` to ``v`` with key ``k`` while
+        ``for (u, v, k, c) in G.edges(data='color', keys=True, default="red"):``
+        iterates through all the edges yielding the color attribute with
+        default `'red'` if no color attribute exists.
+
+        Edges are returned as tuples with optional data and keys
+        in the order (node, neighbor, key, data). If ``keys=True`` is not
+        provided, the tuples will just be (node, neighbor, data), but
+        multiple tuples with the same node and neighbor will be generated
+        when multiple edges exist between two nodes.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges from these nodes.
+        data : string or bool, optional (default=False)
+            The edge attribute returned in 3-tuple (u, v, ddict[data]).
+            If True, return edge attribute dict in 3-tuple (u, v, ddict).
+            If False, return 2-tuple (u, v).
+        keys : bool, optional (default=False)
+            If True, return edge keys with each edge, creating (u, v, k)
+            tuples or (u, v, k, d) tuples if data is also requested.
+        default : value, optional (default=None)
+            Value used for edges that don't have the requested attribute.
+            Only relevant if data is not True or False.
+
+        Returns
+        -------
+        edges : MultiEdgeView
+            A view of edge attributes, usually it iterates over (u, v)
+            (u, v, k) or (u, v, k, d) tuples of edges, but can also be
+            used for attribute lookup as ``edges[u, v, k]['foo']``.
+
+        Notes
+        -----
+        Nodes in nbunch that are not in the graph will be (quietly) ignored.
+        For directed graphs this returns the out-edges.
+
+        Examples
+        --------
+        >>> G = nx.MultiGraph()
+        >>> nx.add_path(G, [0, 1, 2])
+        >>> key = G.add_edge(2, 3, weight=5)
+        >>> key2 = G.add_edge(2, 1, weight=2)  # multi-edge
+        >>> [e for e in G.edges()]
+        [(0, 1), (1, 2), (1, 2), (2, 3)]
+        >>> G.edges.data()  # default data is {} (empty dict)
+        MultiEdgeDataView([(0, 1, {}), (1, 2, {}), (1, 2, {'weight': 2}), (2, 3, {'weight': 5})])
+        >>> G.edges.data("weight", default=1)
+        MultiEdgeDataView([(0, 1, 1), (1, 2, 1), (1, 2, 2), (2, 3, 5)])
+        >>> G.edges(keys=True)  # default keys are integers
+        MultiEdgeView([(0, 1, 0), (1, 2, 0), (1, 2, 1), (2, 3, 0)])
+        >>> G.edges.data(keys=True)
+        MultiEdgeDataView([(0, 1, 0, {}), (1, 2, 0, {}), (1, 2, 1, {'weight': 2}), (2, 3, 0, {'weight': 5})])
+        >>> G.edges.data("weight", default=1, keys=True)
+        MultiEdgeDataView([(0, 1, 0, 1), (1, 2, 0, 1), (1, 2, 1, 2), (2, 3, 0, 5)])
+        >>> G.edges([0, 3])  # Note ordering of tuples from listed sources
+        MultiEdgeDataView([(0, 1), (3, 2)])
+        >>> G.edges([0, 3, 2, 1])  # Note ordering of tuples
+        MultiEdgeDataView([(0, 1), (3, 2), (2, 1), (2, 1)])
+        >>> G.edges(0)
+        MultiEdgeDataView([(0, 1)])
+        """
+        return MultiEdgeView(self)
+
+    def get_edge_data(self, u, v, key=None, default=None):
+        """Returns the attribute dictionary associated with edge (u, v,
+        key).
+
+        If a key is not provided, returns a dictionary mapping edge keys
+        to attribute dictionaries for each edge between u and v.
+
+        This is identical to `G[u][v][key]` except the default is returned
+        instead of an exception is the edge doesn't exist.
+
+        Parameters
+        ----------
+        u, v : nodes
+
+        default :  any Python object (default=None)
+            Value to return if the specific edge (u, v, key) is not
+            found, OR if there are no edges between u and v and no key
+            is specified.
+
+        key : hashable identifier, optional (default=None)
+            Return data only for the edge with specified key, as an
+            attribute dictionary (rather than a dictionary mapping keys
+            to attribute dictionaries).
+
+        Returns
+        -------
+        edge_dict : dictionary
+            The edge attribute dictionary, OR a dictionary mapping edge
+            keys to attribute dictionaries for each of those edges if no
+            specific key is provided (even if there's only one edge
+            between u and v).
+
+        Examples
+        --------
+        >>> G = nx.MultiGraph()  # or MultiDiGraph
+        >>> key = G.add_edge(0, 1, key="a", weight=7)
+        >>> G[0][1]["a"]  # key='a'
+        {'weight': 7}
+        >>> G.edges[0, 1, "a"]  # key='a'
+        {'weight': 7}
+
+        Warning: we protect the graph data structure by making
+        `G.edges` and `G[1][2]` read-only dict-like structures.
+        However, you can assign values to attributes in e.g.
+        `G.edges[1, 2, 'a']` or `G[1][2]['a']` using an additional
+        bracket as shown next. You need to specify all edge info
+        to assign to the edge data associated with an edge.
+
+        >>> G[0][1]["a"]["weight"] = 10
+        >>> G.edges[0, 1, "a"]["weight"] = 10
+        >>> G[0][1]["a"]["weight"]
+        10
+        >>> G.edges[1, 0, "a"]["weight"]
+        10
+
+        >>> G = nx.MultiGraph()  # or MultiDiGraph
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.edges[0, 1, 0]["weight"] = 5
+        >>> G.get_edge_data(0, 1)
+        {0: {'weight': 5}}
+        >>> e = (0, 1)
+        >>> G.get_edge_data(*e)  # tuple form
+        {0: {'weight': 5}}
+        >>> G.get_edge_data(3, 0)  # edge not in graph, returns None
+        >>> G.get_edge_data(3, 0, default=0)  # edge not in graph, return default
+        0
+        >>> G.get_edge_data(1, 0, 0)  # specific key gives back
+        {'weight': 5}
+        """
+        try:
+            if key is None:
+                return self._adj[u][v]
+            else:
+                return self._adj[u][v][key]
+        except KeyError:
+            return default
+
+    @cached_property
+    def degree(self):
+        """A DegreeView for the Graph as G.degree or G.degree().
+
+        The node degree is the number of edges adjacent to the node.
+        The weighted node degree is the sum of the edge weights for
+        edges incident to that node.
+
+        This object provides an iterator for (node, degree) as well as
+        lookup for the degree for a single node.
+
+        Parameters
+        ----------
+        nbunch : single node, container, or all nodes (default= all nodes)
+            The view will only report edges incident to these nodes.
+
+        weight : string or None, optional (default=None)
+           The name of an edge attribute that holds the numerical value used
+           as a weight.  If None, then each edge has weight 1.
+           The degree is the sum of the edge weights adjacent to the node.
+
+        Returns
+        -------
+        MultiDegreeView or int
+            If multiple nodes are requested (the default), returns a `MultiDegreeView`
+            mapping nodes to their degree.
+            If a single node is requested, returns the degree of the node as an integer.
+
+        Examples
+        --------
+        >>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> nx.add_path(G, [0, 1, 2, 3])
+        >>> G.degree(0)  # node 0 with degree 1
+        1
+        >>> list(G.degree([0, 1]))
+        [(0, 1), (1, 2)]
+
+        """
+        return MultiDegreeView(self)
+
+    def is_multigraph(self):
+        """Returns True if graph is a multigraph, False otherwise."""
+        return True
+
+    def is_directed(self):
+        """Returns True if graph is directed, False otherwise."""
+        return False
+
+    def copy(self, as_view=False):
+        """Returns a copy of the graph.
+
+        The copy method by default returns an independent shallow copy
+        of the graph and attributes. That is, if an attribute is a
+        container, that container is shared by the original an the copy.
+        Use Python's `copy.deepcopy` for new containers.
+
+        If `as_view` is True then a view is returned instead of a copy.
+
+        Notes
+        -----
+        All copies reproduce the graph structure, but data attributes
+        may be handled in different ways. There are four types of copies
+        of a graph that people might want.
+
+        Deepcopy -- A "deepcopy" copies the graph structure as well as
+        all data attributes and any objects they might contain.
+        The entire graph object is new so that changes in the copy
+        do not affect the original object. (see Python's copy.deepcopy)
+
+        Data Reference (Shallow) -- For a shallow copy the graph structure
+        is copied but the edge, node and graph attribute dicts are
+        references to those in the original graph. This saves
+        time and memory but could cause confusion if you change an attribute
+        in one graph and it changes the attribute in the other.
+        NetworkX does not provide this level of shallow copy.
+
+        Independent Shallow -- This copy creates new independent attribute
+        dicts and then does a shallow copy of the attributes. That is, any
+        attributes that are containers are shared between the new graph
+        and the original. This is exactly what `dict.copy()` provides.
+        You can obtain this style copy using:
+
+            >>> G = nx.path_graph(5)
+            >>> H = G.copy()
+            >>> H = G.copy(as_view=False)
+            >>> H = nx.Graph(G)
+            >>> H = G.__class__(G)
+
+        Fresh Data -- For fresh data, the graph structure is copied while
+        new empty data attribute dicts are created. The resulting graph
+        is independent of the original and it has no edge, node or graph
+        attributes. Fresh copies are not enabled. Instead use:
+
+            >>> H = G.__class__()
+            >>> H.add_nodes_from(G)
+            >>> H.add_edges_from(G.edges)
+
+        View -- Inspired by dict-views, graph-views act like read-only
+        versions of the original graph, providing a copy of the original
+        structure without requiring any memory for copying the information.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Parameters
+        ----------
+        as_view : bool, optional (default=False)
+            If True, the returned graph-view provides a read-only view
+            of the original graph without actually copying any data.
+
+        Returns
+        -------
+        G : Graph
+            A copy of the graph.
+
+        See Also
+        --------
+        to_directed: return a directed copy of the graph.
+
+        Examples
+        --------
+        >>> G = nx.path_graph(4)  # or DiGraph, MultiGraph, MultiDiGraph, etc
+        >>> H = G.copy()
+
+        """
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self)
+        G = self.__class__()
+        G.graph.update(self.graph)
+        G.add_nodes_from((n, d.copy()) for n, d in self._node.items())
+        G.add_edges_from(
+            (u, v, key, datadict.copy())
+            for u, nbrs in self._adj.items()
+            for v, keydict in nbrs.items()
+            for key, datadict in keydict.items()
+        )
+        return G
+
+    def to_directed(self, as_view=False):
+        """Returns a directed representation of the graph.
+
+        Returns
+        -------
+        G : MultiDiGraph
+            A directed graph with the same name, same nodes, and with
+            each edge (u, v, k, data) replaced by two directed edges
+            (u, v, k, data) and (v, u, k, data).
+
+        Notes
+        -----
+        This returns a "deepcopy" of the edge, node, and
+        graph attributes which attempts to completely copy
+        all of the data and references.
+
+        This is in contrast to the similar D=MultiDiGraph(G) which
+        returns a shallow copy of the data.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Warning: If you have subclassed MultiGraph to use dict-like objects
+        in the data structure, those changes do not transfer to the
+        MultiDiGraph created by this method.
+
+        Examples
+        --------
+        >>> G = nx.MultiGraph()
+        >>> G.add_edge(0, 1)
+        0
+        >>> G.add_edge(0, 1)
+        1
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1)]
+
+        If already directed, return a (deep) copy
+
+        >>> G = nx.MultiDiGraph()
+        >>> G.add_edge(0, 1)
+        0
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1, 0)]
+        """
+        graph_class = self.to_directed_class()
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self, graph_class)
+        # deepcopy when not a view
+        G = graph_class()
+        G.graph.update(deepcopy(self.graph))
+        G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+        G.add_edges_from(
+            (u, v, key, deepcopy(datadict))
+            for u, nbrs in self.adj.items()
+            for v, keydict in nbrs.items()
+            for key, datadict in keydict.items()
+        )
+        return G
+
+    def to_undirected(self, as_view=False):
+        """Returns an undirected copy of the graph.
+
+        Returns
+        -------
+        G : Graph/MultiGraph
+            A deepcopy of the graph.
+
+        See Also
+        --------
+        copy, add_edge, add_edges_from
+
+        Notes
+        -----
+        This returns a "deepcopy" of the edge, node, and
+        graph attributes which attempts to completely copy
+        all of the data and references.
+
+        This is in contrast to the similar `G = nx.MultiGraph(D)`
+        which returns a shallow copy of the data.
+
+        See the Python copy module for more information on shallow
+        and deep copies, https://docs.python.org/3/library/copy.html.
+
+        Warning: If you have subclassed MultiGraph to use dict-like
+        objects in the data structure, those changes do not transfer
+        to the MultiGraph created by this method.
+
+        Examples
+        --------
+        >>> G = nx.MultiGraph([(0, 1), (0, 1), (1, 2)])
+        >>> H = G.to_directed()
+        >>> list(H.edges)
+        [(0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 2, 0), (2, 1, 0)]
+        >>> G2 = H.to_undirected()
+        >>> list(G2.edges)
+        [(0, 1, 0), (0, 1, 1), (1, 2, 0)]
+        """
+        graph_class = self.to_undirected_class()
+        if as_view is True:
+            return nx.graphviews.generic_graph_view(self, graph_class)
+        # deepcopy when not a view
+        G = graph_class()
+        G.graph.update(deepcopy(self.graph))
+        G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
+        G.add_edges_from(
+            (u, v, key, deepcopy(datadict))
+            for u, nbrs in self._adj.items()
+            for v, keydict in nbrs.items()
+            for key, datadict in keydict.items()
+        )
+        return G
+
+    def number_of_edges(self, u=None, v=None):
+        """Returns the number of edges between two nodes.
+
+        Parameters
+        ----------
+        u, v : nodes, optional (Default=all edges)
+            If u and v are specified, return the number of edges between
+            u and v. Otherwise return the total number of all edges.
+
+        Returns
+        -------
+        nedges : int
+            The number of edges in the graph.  If nodes `u` and `v` are
+            specified return the number of edges between those nodes. If
+            the graph is directed, this only returns the number of edges
+            from `u` to `v`.
+
+        See Also
+        --------
+        size
+
+        Examples
+        --------
+        For undirected multigraphs, this method counts the total number
+        of edges in the graph::
+
+            >>> G = nx.MultiGraph()
+            >>> G.add_edges_from([(0, 1), (0, 1), (1, 2)])
+            [0, 1, 0]
+            >>> G.number_of_edges()
+            3
+
+        If you specify two nodes, this counts the total number of edges
+        joining the two nodes::
+
+            >>> G.number_of_edges(0, 1)
+            2
+
+        For directed multigraphs, this method can count the total number
+        of directed edges from `u` to `v`::
+
+            >>> G = nx.MultiDiGraph()
+            >>> G.add_edges_from([(0, 1), (0, 1), (1, 0)])
+            [0, 1, 0]
+            >>> G.number_of_edges(0, 1)
+            2
+            >>> G.number_of_edges(1, 0)
+            1
+
+        """
+        if u is None:
+            return self.size()
+        try:
+            edgedata = self._adj[u][v]
+        except KeyError:
+            return 0  # no such edge
+        return len(edgedata)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/reportviews.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/reportviews.py
new file mode 100644
index 0000000000000000000000000000000000000000..789662de19600ec2a7922db612c525dfb75695ea
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/classes/reportviews.py
@@ -0,0 +1,1447 @@
+"""
+View Classes provide node, edge and degree "views" of a graph.
+
+Views for nodes, edges and degree are provided for all base graph classes.
+A view means a read-only object that is quick to create, automatically
+updated when the graph changes, and provides basic access like `n in V`,
+`for n in V`, `V[n]` and sometimes set operations.
+
+The views are read-only iterable containers that are updated as the
+graph is updated. As with dicts, the graph should not be updated
+while iterating through the view. Views can be iterated multiple times.
+
+Edge and Node views also allow data attribute lookup.
+The resulting attribute dict is writable as `G.edges[3, 4]['color']='red'`
+Degree views allow lookup of degree values for single nodes.
+Weighted degree is supported with the `weight` argument.
+
+NodeView
+========
+
+    `V = G.nodes` (or `V = G.nodes()`) allows `len(V)`, `n in V`, set
+    operations e.g. "G.nodes & H.nodes", and `dd = G.nodes[n]`, where
+    `dd` is the node data dict. Iteration is over the nodes by default.
+
+NodeDataView
+============
+
+    To iterate over (node, data) pairs, use arguments to `G.nodes()`
+    to create a DataView e.g. `DV = G.nodes(data='color', default='red')`.
+    The DataView iterates as `for n, color in DV` and allows
+    `(n, 'red') in DV`. Using `DV = G.nodes(data=True)`, the DataViews
+    use the full datadict in writeable form also allowing contain testing as
+    `(n, {'color': 'red'}) in VD`. DataViews allow set operations when
+    data attributes are hashable.
+
+DegreeView
+==========
+
+    `V = G.degree` allows iteration over (node, degree) pairs as well
+    as lookup: `deg=V[n]`. There are many flavors of DegreeView
+    for In/Out/Directed/Multi. For Directed Graphs, `G.degree`
+    counts both in and out going edges. `G.out_degree` and
+    `G.in_degree` count only specific directions.
+    Weighted degree using edge data attributes is provide via
+    `V = G.degree(weight='attr_name')` where any string with the
+    attribute name can be used. `weight=None` is the default.
+    No set operations are implemented for degrees, use NodeView.
+
+    The argument `nbunch` restricts iteration to nodes in nbunch.
+    The DegreeView can still lookup any node even if nbunch is specified.
+
+EdgeView
+========
+
+    `V = G.edges` or `V = G.edges()` allows iteration over edges as well as
+    `e in V`, set operations and edge data lookup `dd = G.edges[2, 3]`.
+    Iteration is over 2-tuples `(u, v)` for Graph/DiGraph. For multigraphs
+    edges 3-tuples `(u, v, key)` are the default but 2-tuples can be obtained
+    via `V = G.edges(keys=False)`.
+
+    Set operations for directed graphs treat the edges as a set of 2-tuples.
+    For undirected graphs, 2-tuples are not a unique representation of edges.
+    So long as the set being compared to contains unique representations
+    of its edges, the set operations will act as expected. If the other
+    set contains both `(0, 1)` and `(1, 0)` however, the result of set
+    operations may contain both representations of the same edge.
+
+EdgeDataView
+============
+
+    Edge data can be reported using an EdgeDataView typically created
+    by calling an EdgeView: `DV = G.edges(data='weight', default=1)`.
+    The EdgeDataView allows iteration over edge tuples, membership checking
+    but no set operations.
+
+    Iteration depends on `data` and `default` and for multigraph `keys`
+    If `data is False` (the default) then iterate over 2-tuples `(u, v)`.
+    If `data is True` iterate over 3-tuples `(u, v, datadict)`.
+    Otherwise iterate over `(u, v, datadict.get(data, default))`.
+    For Multigraphs, if `keys is True`, replace `u, v` with `u, v, key`
+    to create 3-tuples and 4-tuples.
+
+    The argument `nbunch` restricts edges to those incident to nodes in nbunch.
+"""
+
+from abc import ABC
+from collections.abc import Mapping, Set
+
+import networkx as nx
+
+__all__ = [
+    "NodeView",
+    "NodeDataView",
+    "EdgeView",
+    "OutEdgeView",
+    "InEdgeView",
+    "EdgeDataView",
+    "OutEdgeDataView",
+    "InEdgeDataView",
+    "MultiEdgeView",
+    "OutMultiEdgeView",
+    "InMultiEdgeView",
+    "MultiEdgeDataView",
+    "OutMultiEdgeDataView",
+    "InMultiEdgeDataView",
+    "DegreeView",
+    "DiDegreeView",
+    "InDegreeView",
+    "OutDegreeView",
+    "MultiDegreeView",
+    "DiMultiDegreeView",
+    "InMultiDegreeView",
+    "OutMultiDegreeView",
+]
+
+
+# NodeViews
+class NodeView(Mapping, Set):
+    """A NodeView class to act as G.nodes for a NetworkX Graph
+
+    Set operations act on the nodes without considering data.
+    Iteration is over nodes. Node data can be looked up like a dict.
+    Use NodeDataView to iterate over node data or to specify a data
+    attribute for lookup. NodeDataView is created by calling the NodeView.
+
+    Parameters
+    ----------
+    graph : NetworkX graph-like class
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> NV = G.nodes()
+    >>> 2 in NV
+    True
+    >>> for n in NV:
+    ...     print(n)
+    0
+    1
+    2
+    >>> assert NV & {1, 2, 3} == {1, 2}
+
+    >>> G.add_node(2, color="blue")
+    >>> NV[2]
+    {'color': 'blue'}
+    >>> G.add_node(8, color="red")
+    >>> NDV = G.nodes(data=True)
+    >>> (2, NV[2]) in NDV
+    True
+    >>> for n, dd in NDV:
+    ...     print((n, dd.get("color", "aqua")))
+    (0, 'aqua')
+    (1, 'aqua')
+    (2, 'blue')
+    (8, 'red')
+    >>> NDV[2] == NV[2]
+    True
+
+    >>> NVdata = G.nodes(data="color", default="aqua")
+    >>> (2, NVdata[2]) in NVdata
+    True
+    >>> for n, dd in NVdata:
+    ...     print((n, dd))
+    (0, 'aqua')
+    (1, 'aqua')
+    (2, 'blue')
+    (8, 'red')
+    >>> NVdata[2] == NV[2]  # NVdata gets 'color', NV gets datadict
+    False
+    """
+
+    __slots__ = ("_nodes",)
+
+    def __getstate__(self):
+        return {"_nodes": self._nodes}
+
+    def __setstate__(self, state):
+        self._nodes = state["_nodes"]
+
+    def __init__(self, graph):
+        self._nodes = graph._node
+
+    # Mapping methods
+    def __len__(self):
+        return len(self._nodes)
+
+    def __iter__(self):
+        return iter(self._nodes)
+
+    def __getitem__(self, n):
+        if isinstance(n, slice):
+            raise nx.NetworkXError(
+                f"{type(self).__name__} does not support slicing, "
+                f"try list(G.nodes)[{n.start}:{n.stop}:{n.step}]"
+            )
+        return self._nodes[n]
+
+    # Set methods
+    def __contains__(self, n):
+        return n in self._nodes
+
+    @classmethod
+    def _from_iterable(cls, it):
+        return set(it)
+
+    # DataView method
+    def __call__(self, data=False, default=None):
+        if data is False:
+            return self
+        return NodeDataView(self._nodes, data, default)
+
+    def data(self, data=True, default=None):
+        """
+        Return a read-only view of node data.
+
+        Parameters
+        ----------
+        data : bool or node data key, default=True
+            If ``data=True`` (the default), return a `NodeDataView` object that
+            maps each node to *all* of its attributes. `data` may also be an
+            arbitrary key, in which case the `NodeDataView` maps each node to
+            the value for the keyed attribute. In this case, if a node does
+            not have the `data` attribute, the `default` value is used.
+        default : object, default=None
+            The value used when a node does not have a specific attribute.
+
+        Returns
+        -------
+        NodeDataView
+            The layout of the returned NodeDataView depends on the value of the
+            `data` parameter.
+
+        Notes
+        -----
+        If ``data=False``, returns a `NodeView` object without data.
+
+        See Also
+        --------
+        NodeDataView
+
+        Examples
+        --------
+        >>> G = nx.Graph()
+        >>> G.add_nodes_from(
+        ...     [
+        ...         (0, {"color": "red", "weight": 10}),
+        ...         (1, {"color": "blue"}),
+        ...         (2, {"color": "yellow", "weight": 2}),
+        ...     ]
+        ... )
+
+        Accessing node data with ``data=True`` (the default) returns a
+        NodeDataView mapping each node to all of its attributes:
+
+        >>> G.nodes.data()
+        NodeDataView({0: {'color': 'red', 'weight': 10}, 1: {'color': 'blue'}, 2: {'color': 'yellow', 'weight': 2}})
+
+        If `data` represents  a key in the node attribute dict, a NodeDataView mapping
+        the nodes to the value for that specific key is returned:
+
+        >>> G.nodes.data("color")
+        NodeDataView({0: 'red', 1: 'blue', 2: 'yellow'}, data='color')
+
+        If a specific key is not found in an attribute dict, the value specified
+        by `default` is returned:
+
+        >>> G.nodes.data("weight", default=-999)
+        NodeDataView({0: 10, 1: -999, 2: 2}, data='weight')
+
+        Note that there is no check that the `data` key is in any of the
+        node attribute dictionaries:
+
+        >>> G.nodes.data("height")
+        NodeDataView({0: None, 1: None, 2: None}, data='height')
+        """
+        if data is False:
+            return self
+        return NodeDataView(self._nodes, data, default)
+
+    def __str__(self):
+        return str(list(self))
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({tuple(self)})"
+
+
+class NodeDataView(Set):
+    """A DataView class for nodes of a NetworkX Graph
+
+    The main use for this class is to iterate through node-data pairs.
+    The data can be the entire data-dictionary for each node, or it
+    can be a specific attribute (with default) for each node.
+    Set operations are enabled with NodeDataView, but don't work in
+    cases where the data is not hashable. Use with caution.
+    Typically, set operations on nodes use NodeView, not NodeDataView.
+    That is, they use `G.nodes` instead of `G.nodes(data='foo')`.
+
+    Parameters
+    ==========
+    graph : NetworkX graph-like class
+    data : bool or string (default=False)
+    default : object (default=None)
+    """
+
+    __slots__ = ("_nodes", "_data", "_default")
+
+    def __getstate__(self):
+        return {"_nodes": self._nodes, "_data": self._data, "_default": self._default}
+
+    def __setstate__(self, state):
+        self._nodes = state["_nodes"]
+        self._data = state["_data"]
+        self._default = state["_default"]
+
+    def __init__(self, nodedict, data=False, default=None):
+        self._nodes = nodedict
+        self._data = data
+        self._default = default
+
+    @classmethod
+    def _from_iterable(cls, it):
+        try:
+            return set(it)
+        except TypeError as err:
+            if "unhashable" in str(err):
+                msg = " : Could be b/c data=True or your values are unhashable"
+                raise TypeError(str(err) + msg) from err
+            raise
+
+    def __len__(self):
+        return len(self._nodes)
+
+    def __iter__(self):
+        data = self._data
+        if data is False:
+            return iter(self._nodes)
+        if data is True:
+            return iter(self._nodes.items())
+        return (
+            (n, dd[data] if data in dd else self._default)
+            for n, dd in self._nodes.items()
+        )
+
+    def __contains__(self, n):
+        try:
+            node_in = n in self._nodes
+        except TypeError:
+            n, d = n
+            return n in self._nodes and self[n] == d
+        if node_in is True:
+            return node_in
+        try:
+            n, d = n
+        except (TypeError, ValueError):
+            return False
+        return n in self._nodes and self[n] == d
+
+    def __getitem__(self, n):
+        if isinstance(n, slice):
+            raise nx.NetworkXError(
+                f"{type(self).__name__} does not support slicing, "
+                f"try list(G.nodes.data())[{n.start}:{n.stop}:{n.step}]"
+            )
+        ddict = self._nodes[n]
+        data = self._data
+        if data is False or data is True:
+            return ddict
+        return ddict[data] if data in ddict else self._default
+
+    def __str__(self):
+        return str(list(self))
+
+    def __repr__(self):
+        name = self.__class__.__name__
+        if self._data is False:
+            return f"{name}({tuple(self)})"
+        if self._data is True:
+            return f"{name}({dict(self)})"
+        return f"{name}({dict(self)}, data={self._data!r})"
+
+
+# DegreeViews
+class DiDegreeView:
+    """A View class for degree of nodes in a NetworkX Graph
+
+    The functionality is like dict.items() with (node, degree) pairs.
+    Additional functionality includes read-only lookup of node degree,
+    and calling with optional features nbunch (for only a subset of nodes)
+    and weight (use edge weights to compute degree).
+
+    Parameters
+    ==========
+    graph : NetworkX graph-like class
+    nbunch : node, container of nodes, or None meaning all nodes (default=None)
+    weight : bool or string (default=None)
+
+    Notes
+    -----
+    DegreeView can still lookup any node even if nbunch is specified.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> DV = G.degree()
+    >>> assert DV[2] == 1
+    >>> assert sum(deg for n, deg in DV) == 4
+
+    >>> DVweight = G.degree(weight="span")
+    >>> G.add_edge(1, 2, span=34)
+    >>> DVweight[2]
+    34
+    >>> DVweight[0]  #  default edge weight is 1
+    1
+    >>> sum(span for n, span in DVweight)  # sum weighted degrees
+    70
+
+    >>> DVnbunch = G.degree(nbunch=(1, 2))
+    >>> assert len(list(DVnbunch)) == 2  # iteration over nbunch only
+    """
+
+    def __init__(self, G, nbunch=None, weight=None):
+        self._graph = G
+        self._succ = G._succ if hasattr(G, "_succ") else G._adj
+        self._pred = G._pred if hasattr(G, "_pred") else G._adj
+        self._nodes = self._succ if nbunch is None else list(G.nbunch_iter(nbunch))
+        self._weight = weight
+
+    def __call__(self, nbunch=None, weight=None):
+        if nbunch is None:
+            if weight == self._weight:
+                return self
+            return self.__class__(self._graph, None, weight)
+        try:
+            if nbunch in self._nodes:
+                if weight == self._weight:
+                    return self[nbunch]
+                return self.__class__(self._graph, None, weight)[nbunch]
+        except TypeError:
+            pass
+        return self.__class__(self._graph, nbunch, weight)
+
+    def __getitem__(self, n):
+        weight = self._weight
+        succs = self._succ[n]
+        preds = self._pred[n]
+        if weight is None:
+            return len(succs) + len(preds)
+        return sum(dd.get(weight, 1) for dd in succs.values()) + sum(
+            dd.get(weight, 1) for dd in preds.values()
+        )
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                succs = self._succ[n]
+                preds = self._pred[n]
+                yield (n, len(succs) + len(preds))
+        else:
+            for n in self._nodes:
+                succs = self._succ[n]
+                preds = self._pred[n]
+                deg = sum(dd.get(weight, 1) for dd in succs.values()) + sum(
+                    dd.get(weight, 1) for dd in preds.values()
+                )
+                yield (n, deg)
+
+    def __len__(self):
+        return len(self._nodes)
+
+    def __str__(self):
+        return str(list(self))
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({dict(self)})"
+
+
+class DegreeView(DiDegreeView):
+    """A DegreeView class to act as G.degree for a NetworkX Graph
+
+    Typical usage focuses on iteration over `(node, degree)` pairs.
+    The degree is by default the number of edges incident to the node.
+    Optional argument `weight` enables weighted degree using the edge
+    attribute named in the `weight` argument.  Reporting and iteration
+    can also be restricted to a subset of nodes using `nbunch`.
+
+    Additional functionality include node lookup so that `G.degree[n]`
+    reported the (possibly weighted) degree of node `n`. Calling the
+    view creates a view with different arguments `nbunch` or `weight`.
+
+    Parameters
+    ==========
+    graph : NetworkX graph-like class
+    nbunch : node, container of nodes, or None meaning all nodes (default=None)
+    weight : string or None (default=None)
+
+    Notes
+    -----
+    DegreeView can still lookup any node even if nbunch is specified.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> DV = G.degree()
+    >>> assert DV[2] == 1
+    >>> assert G.degree[2] == 1
+    >>> assert sum(deg for n, deg in DV) == 4
+
+    >>> DVweight = G.degree(weight="span")
+    >>> G.add_edge(1, 2, span=34)
+    >>> DVweight[2]
+    34
+    >>> DVweight[0]  #  default edge weight is 1
+    1
+    >>> sum(span for n, span in DVweight)  # sum weighted degrees
+    70
+
+    >>> DVnbunch = G.degree(nbunch=(1, 2))
+    >>> assert len(list(DVnbunch)) == 2  # iteration over nbunch only
+    """
+
+    def __getitem__(self, n):
+        weight = self._weight
+        nbrs = self._succ[n]
+        if weight is None:
+            return len(nbrs) + (n in nbrs)
+        return sum(dd.get(weight, 1) for dd in nbrs.values()) + (
+            n in nbrs and nbrs[n].get(weight, 1)
+        )
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                nbrs = self._succ[n]
+                yield (n, len(nbrs) + (n in nbrs))
+        else:
+            for n in self._nodes:
+                nbrs = self._succ[n]
+                deg = sum(dd.get(weight, 1) for dd in nbrs.values()) + (
+                    n in nbrs and nbrs[n].get(weight, 1)
+                )
+                yield (n, deg)
+
+
+class OutDegreeView(DiDegreeView):
+    """A DegreeView class to report out_degree for a DiGraph; See DegreeView"""
+
+    def __getitem__(self, n):
+        weight = self._weight
+        nbrs = self._succ[n]
+        if self._weight is None:
+            return len(nbrs)
+        return sum(dd.get(self._weight, 1) for dd in nbrs.values())
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                succs = self._succ[n]
+                yield (n, len(succs))
+        else:
+            for n in self._nodes:
+                succs = self._succ[n]
+                deg = sum(dd.get(weight, 1) for dd in succs.values())
+                yield (n, deg)
+
+
+class InDegreeView(DiDegreeView):
+    """A DegreeView class to report in_degree for a DiGraph; See DegreeView"""
+
+    def __getitem__(self, n):
+        weight = self._weight
+        nbrs = self._pred[n]
+        if weight is None:
+            return len(nbrs)
+        return sum(dd.get(weight, 1) for dd in nbrs.values())
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                preds = self._pred[n]
+                yield (n, len(preds))
+        else:
+            for n in self._nodes:
+                preds = self._pred[n]
+                deg = sum(dd.get(weight, 1) for dd in preds.values())
+                yield (n, deg)
+
+
+class MultiDegreeView(DiDegreeView):
+    """A DegreeView class for undirected multigraphs; See DegreeView"""
+
+    def __getitem__(self, n):
+        weight = self._weight
+        nbrs = self._succ[n]
+        if weight is None:
+            return sum(len(keys) for keys in nbrs.values()) + (
+                n in nbrs and len(nbrs[n])
+            )
+        # edge weighted graph - degree is sum of nbr edge weights
+        deg = sum(
+            d.get(weight, 1) for key_dict in nbrs.values() for d in key_dict.values()
+        )
+        if n in nbrs:
+            deg += sum(d.get(weight, 1) for d in nbrs[n].values())
+        return deg
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                nbrs = self._succ[n]
+                deg = sum(len(keys) for keys in nbrs.values()) + (
+                    n in nbrs and len(nbrs[n])
+                )
+                yield (n, deg)
+        else:
+            for n in self._nodes:
+                nbrs = self._succ[n]
+                deg = sum(
+                    d.get(weight, 1)
+                    for key_dict in nbrs.values()
+                    for d in key_dict.values()
+                )
+                if n in nbrs:
+                    deg += sum(d.get(weight, 1) for d in nbrs[n].values())
+                yield (n, deg)
+
+
+class DiMultiDegreeView(DiDegreeView):
+    """A DegreeView class for MultiDiGraph; See DegreeView"""
+
+    def __getitem__(self, n):
+        weight = self._weight
+        succs = self._succ[n]
+        preds = self._pred[n]
+        if weight is None:
+            return sum(len(keys) for keys in succs.values()) + sum(
+                len(keys) for keys in preds.values()
+            )
+        # edge weighted graph - degree is sum of nbr edge weights
+        deg = sum(
+            d.get(weight, 1) for key_dict in succs.values() for d in key_dict.values()
+        ) + sum(
+            d.get(weight, 1) for key_dict in preds.values() for d in key_dict.values()
+        )
+        return deg
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                succs = self._succ[n]
+                preds = self._pred[n]
+                deg = sum(len(keys) for keys in succs.values()) + sum(
+                    len(keys) for keys in preds.values()
+                )
+                yield (n, deg)
+        else:
+            for n in self._nodes:
+                succs = self._succ[n]
+                preds = self._pred[n]
+                deg = sum(
+                    d.get(weight, 1)
+                    for key_dict in succs.values()
+                    for d in key_dict.values()
+                ) + sum(
+                    d.get(weight, 1)
+                    for key_dict in preds.values()
+                    for d in key_dict.values()
+                )
+                yield (n, deg)
+
+
+class InMultiDegreeView(DiDegreeView):
+    """A DegreeView class for inward degree of MultiDiGraph; See DegreeView"""
+
+    def __getitem__(self, n):
+        weight = self._weight
+        nbrs = self._pred[n]
+        if weight is None:
+            return sum(len(data) for data in nbrs.values())
+        # edge weighted graph - degree is sum of nbr edge weights
+        return sum(
+            d.get(weight, 1) for key_dict in nbrs.values() for d in key_dict.values()
+        )
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                nbrs = self._pred[n]
+                deg = sum(len(data) for data in nbrs.values())
+                yield (n, deg)
+        else:
+            for n in self._nodes:
+                nbrs = self._pred[n]
+                deg = sum(
+                    d.get(weight, 1)
+                    for key_dict in nbrs.values()
+                    for d in key_dict.values()
+                )
+                yield (n, deg)
+
+
+class OutMultiDegreeView(DiDegreeView):
+    """A DegreeView class for outward degree of MultiDiGraph; See DegreeView"""
+
+    def __getitem__(self, n):
+        weight = self._weight
+        nbrs = self._succ[n]
+        if weight is None:
+            return sum(len(data) for data in nbrs.values())
+        # edge weighted graph - degree is sum of nbr edge weights
+        return sum(
+            d.get(weight, 1) for key_dict in nbrs.values() for d in key_dict.values()
+        )
+
+    def __iter__(self):
+        weight = self._weight
+        if weight is None:
+            for n in self._nodes:
+                nbrs = self._succ[n]
+                deg = sum(len(data) for data in nbrs.values())
+                yield (n, deg)
+        else:
+            for n in self._nodes:
+                nbrs = self._succ[n]
+                deg = sum(
+                    d.get(weight, 1)
+                    for key_dict in nbrs.values()
+                    for d in key_dict.values()
+                )
+                yield (n, deg)
+
+
+# A base class for all edge views. Ensures all edge view and edge data view
+# objects/classes are captured by `isinstance(obj, EdgeViewABC)` and
+# `issubclass(cls, EdgeViewABC)` respectively
+class EdgeViewABC(ABC):
+    pass
+
+
+# EdgeDataViews
+class OutEdgeDataView(EdgeViewABC):
+    """EdgeDataView for outward edges of DiGraph; See EdgeDataView"""
+
+    __slots__ = (
+        "_viewer",
+        "_nbunch",
+        "_data",
+        "_default",
+        "_adjdict",
+        "_nodes_nbrs",
+        "_report",
+    )
+
+    def __getstate__(self):
+        return {
+            "viewer": self._viewer,
+            "nbunch": self._nbunch,
+            "data": self._data,
+            "default": self._default,
+        }
+
+    def __setstate__(self, state):
+        self.__init__(**state)
+
+    def __init__(self, viewer, nbunch=None, data=False, *, default=None):
+        self._viewer = viewer
+        adjdict = self._adjdict = viewer._adjdict
+        if nbunch is None:
+            self._nodes_nbrs = adjdict.items
+        else:
+            # dict retains order of nodes but acts like a set
+            nbunch = dict.fromkeys(viewer._graph.nbunch_iter(nbunch))
+            self._nodes_nbrs = lambda: [(n, adjdict[n]) for n in nbunch]
+        self._nbunch = nbunch
+        self._data = data
+        self._default = default
+        # Set _report based on data and default
+        if data is True:
+            self._report = lambda n, nbr, dd: (n, nbr, dd)
+        elif data is False:
+            self._report = lambda n, nbr, dd: (n, nbr)
+        else:  # data is attribute name
+            self._report = (
+                lambda n, nbr, dd: (n, nbr, dd[data])
+                if data in dd
+                else (n, nbr, default)
+            )
+
+    def __len__(self):
+        return sum(len(nbrs) for n, nbrs in self._nodes_nbrs())
+
+    def __iter__(self):
+        return (
+            self._report(n, nbr, dd)
+            for n, nbrs in self._nodes_nbrs()
+            for nbr, dd in nbrs.items()
+        )
+
+    def __contains__(self, e):
+        u, v = e[:2]
+        if self._nbunch is not None and u not in self._nbunch:
+            return False  # this edge doesn't start in nbunch
+        try:
+            ddict = self._adjdict[u][v]
+        except KeyError:
+            return False
+        return e == self._report(u, v, ddict)
+
+    def __str__(self):
+        return str(list(self))
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({list(self)})"
+
+
+class EdgeDataView(OutEdgeDataView):
+    """A EdgeDataView class for edges of Graph
+
+    This view is primarily used to iterate over the edges reporting
+    edges as node-tuples with edge data optionally reported. The
+    argument `nbunch` allows restriction to edges incident to nodes
+    in that container/singleton. The default (nbunch=None)
+    reports all edges. The arguments `data` and `default` control
+    what edge data is reported. The default `data is False` reports
+    only node-tuples for each edge. If `data is True` the entire edge
+    data dict is returned. Otherwise `data` is assumed to hold the name
+    of the edge attribute to report with default `default` if  that
+    edge attribute is not present.
+
+    Parameters
+    ----------
+    nbunch : container of nodes, node or None (default None)
+    data : False, True or string (default False)
+    default : default value (default None)
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> G.add_edge(1, 2, foo="bar")
+    >>> list(G.edges(data="foo", default="biz"))
+    [(0, 1, 'biz'), (1, 2, 'bar')]
+    >>> assert (0, 1, "biz") in G.edges(data="foo", default="biz")
+    """
+
+    __slots__ = ()
+
+    def __len__(self):
+        return sum(1 for e in self)
+
+    def __iter__(self):
+        seen = {}
+        for n, nbrs in self._nodes_nbrs():
+            for nbr, dd in nbrs.items():
+                if nbr not in seen:
+                    yield self._report(n, nbr, dd)
+            seen[n] = 1
+        del seen
+
+    def __contains__(self, e):
+        u, v = e[:2]
+        if self._nbunch is not None and u not in self._nbunch and v not in self._nbunch:
+            return False  # this edge doesn't start and it doesn't end in nbunch
+        try:
+            ddict = self._adjdict[u][v]
+        except KeyError:
+            return False
+        return e == self._report(u, v, ddict)
+
+
+class InEdgeDataView(OutEdgeDataView):
+    """An EdgeDataView class for outward edges of DiGraph; See EdgeDataView"""
+
+    __slots__ = ()
+
+    def __iter__(self):
+        return (
+            self._report(nbr, n, dd)
+            for n, nbrs in self._nodes_nbrs()
+            for nbr, dd in nbrs.items()
+        )
+
+    def __contains__(self, e):
+        u, v = e[:2]
+        if self._nbunch is not None and v not in self._nbunch:
+            return False  # this edge doesn't end in nbunch
+        try:
+            ddict = self._adjdict[v][u]
+        except KeyError:
+            return False
+        return e == self._report(u, v, ddict)
+
+
+class OutMultiEdgeDataView(OutEdgeDataView):
+    """An EdgeDataView for outward edges of MultiDiGraph; See EdgeDataView"""
+
+    __slots__ = ("keys",)
+
+    def __getstate__(self):
+        return {
+            "viewer": self._viewer,
+            "nbunch": self._nbunch,
+            "keys": self.keys,
+            "data": self._data,
+            "default": self._default,
+        }
+
+    def __setstate__(self, state):
+        self.__init__(**state)
+
+    def __init__(self, viewer, nbunch=None, data=False, *, default=None, keys=False):
+        self._viewer = viewer
+        adjdict = self._adjdict = viewer._adjdict
+        self.keys = keys
+        if nbunch is None:
+            self._nodes_nbrs = adjdict.items
+        else:
+            # dict retains order of nodes but acts like a set
+            nbunch = dict.fromkeys(viewer._graph.nbunch_iter(nbunch))
+            self._nodes_nbrs = lambda: [(n, adjdict[n]) for n in nbunch]
+        self._nbunch = nbunch
+        self._data = data
+        self._default = default
+        # Set _report based on data and default
+        if data is True:
+            if keys is True:
+                self._report = lambda n, nbr, k, dd: (n, nbr, k, dd)
+            else:
+                self._report = lambda n, nbr, k, dd: (n, nbr, dd)
+        elif data is False:
+            if keys is True:
+                self._report = lambda n, nbr, k, dd: (n, nbr, k)
+            else:
+                self._report = lambda n, nbr, k, dd: (n, nbr)
+        else:  # data is attribute name
+            if keys is True:
+                self._report = (
+                    lambda n, nbr, k, dd: (n, nbr, k, dd[data])
+                    if data in dd
+                    else (n, nbr, k, default)
+                )
+            else:
+                self._report = (
+                    lambda n, nbr, k, dd: (n, nbr, dd[data])
+                    if data in dd
+                    else (n, nbr, default)
+                )
+
+    def __len__(self):
+        return sum(1 for e in self)
+
+    def __iter__(self):
+        return (
+            self._report(n, nbr, k, dd)
+            for n, nbrs in self._nodes_nbrs()
+            for nbr, kd in nbrs.items()
+            for k, dd in kd.items()
+        )
+
+    def __contains__(self, e):
+        u, v = e[:2]
+        if self._nbunch is not None and u not in self._nbunch:
+            return False  # this edge doesn't start in nbunch
+        try:
+            kdict = self._adjdict[u][v]
+        except KeyError:
+            return False
+        if self.keys is True:
+            k = e[2]
+            try:
+                dd = kdict[k]
+            except KeyError:
+                return False
+            return e == self._report(u, v, k, dd)
+        return any(e == self._report(u, v, k, dd) for k, dd in kdict.items())
+
+
+class MultiEdgeDataView(OutMultiEdgeDataView):
+    """An EdgeDataView class for edges of MultiGraph; See EdgeDataView"""
+
+    __slots__ = ()
+
+    def __iter__(self):
+        seen = {}
+        for n, nbrs in self._nodes_nbrs():
+            for nbr, kd in nbrs.items():
+                if nbr not in seen:
+                    for k, dd in kd.items():
+                        yield self._report(n, nbr, k, dd)
+            seen[n] = 1
+        del seen
+
+    def __contains__(self, e):
+        u, v = e[:2]
+        if self._nbunch is not None and u not in self._nbunch and v not in self._nbunch:
+            return False  # this edge doesn't start and doesn't end in nbunch
+        try:
+            kdict = self._adjdict[u][v]
+        except KeyError:
+            try:
+                kdict = self._adjdict[v][u]
+            except KeyError:
+                return False
+        if self.keys is True:
+            k = e[2]
+            try:
+                dd = kdict[k]
+            except KeyError:
+                return False
+            return e == self._report(u, v, k, dd)
+        return any(e == self._report(u, v, k, dd) for k, dd in kdict.items())
+
+
+class InMultiEdgeDataView(OutMultiEdgeDataView):
+    """An EdgeDataView for inward edges of MultiDiGraph; See EdgeDataView"""
+
+    __slots__ = ()
+
+    def __iter__(self):
+        return (
+            self._report(nbr, n, k, dd)
+            for n, nbrs in self._nodes_nbrs()
+            for nbr, kd in nbrs.items()
+            for k, dd in kd.items()
+        )
+
+    def __contains__(self, e):
+        u, v = e[:2]
+        if self._nbunch is not None and v not in self._nbunch:
+            return False  # this edge doesn't end in nbunch
+        try:
+            kdict = self._adjdict[v][u]
+        except KeyError:
+            return False
+        if self.keys is True:
+            k = e[2]
+            dd = kdict[k]
+            return e == self._report(u, v, k, dd)
+        return any(e == self._report(u, v, k, dd) for k, dd in kdict.items())
+
+
+# EdgeViews    have set operations and no data reported
+class OutEdgeView(Set, Mapping, EdgeViewABC):
+    """A EdgeView class for outward edges of a DiGraph"""
+
+    __slots__ = ("_adjdict", "_graph", "_nodes_nbrs")
+
+    def __getstate__(self):
+        return {"_graph": self._graph, "_adjdict": self._adjdict}
+
+    def __setstate__(self, state):
+        self._graph = state["_graph"]
+        self._adjdict = state["_adjdict"]
+        self._nodes_nbrs = self._adjdict.items
+
+    @classmethod
+    def _from_iterable(cls, it):
+        return set(it)
+
+    dataview = OutEdgeDataView
+
+    def __init__(self, G):
+        self._graph = G
+        self._adjdict = G._succ if hasattr(G, "succ") else G._adj
+        self._nodes_nbrs = self._adjdict.items
+
+    # Set methods
+    def __len__(self):
+        return sum(len(nbrs) for n, nbrs in self._nodes_nbrs())
+
+    def __iter__(self):
+        for n, nbrs in self._nodes_nbrs():
+            for nbr in nbrs:
+                yield (n, nbr)
+
+    def __contains__(self, e):
+        try:
+            u, v = e
+            return v in self._adjdict[u]
+        except KeyError:
+            return False
+
+    # Mapping Methods
+    def __getitem__(self, e):
+        if isinstance(e, slice):
+            raise nx.NetworkXError(
+                f"{type(self).__name__} does not support slicing, "
+                f"try list(G.edges)[{e.start}:{e.stop}:{e.step}]"
+            )
+        u, v = e
+        try:
+            return self._adjdict[u][v]
+        except KeyError as ex:  # Customize msg to indicate exception origin
+            raise KeyError(f"The edge {e} is not in the graph.")
+
+    # EdgeDataView methods
+    def __call__(self, nbunch=None, data=False, *, default=None):
+        if nbunch is None and data is False:
+            return self
+        return self.dataview(self, nbunch, data, default=default)
+
+    def data(self, data=True, default=None, nbunch=None):
+        """
+        Return a read-only view of edge data.
+
+        Parameters
+        ----------
+        data : bool or edge attribute key
+            If ``data=True``, then the data view maps each edge to a dictionary
+            containing all of its attributes. If `data` is a key in the edge
+            dictionary, then the data view maps each edge to its value for
+            the keyed attribute. In this case, if the edge doesn't have the
+            attribute, the `default` value is returned.
+        default : object, default=None
+            The value used when an edge does not have a specific attribute
+        nbunch : container of nodes, optional (default=None)
+            Allows restriction to edges only involving certain nodes. All edges
+            are considered by default.
+
+        Returns
+        -------
+        dataview
+            Returns an `EdgeDataView` for undirected Graphs, `OutEdgeDataView`
+            for DiGraphs, `MultiEdgeDataView` for MultiGraphs and
+            `OutMultiEdgeDataView` for MultiDiGraphs.
+
+        Notes
+        -----
+        If ``data=False``, returns an `EdgeView` without any edge data.
+
+        See Also
+        --------
+        EdgeDataView
+        OutEdgeDataView
+        MultiEdgeDataView
+        OutMultiEdgeDataView
+
+        Examples
+        --------
+        >>> G = nx.Graph()
+        >>> G.add_edges_from(
+        ...     [
+        ...         (0, 1, {"dist": 3, "capacity": 20}),
+        ...         (1, 2, {"dist": 4}),
+        ...         (2, 0, {"dist": 5}),
+        ...     ]
+        ... )
+
+        Accessing edge data with ``data=True`` (the default) returns an
+        edge data view object listing each edge with all of its attributes:
+
+        >>> G.edges.data()
+        EdgeDataView([(0, 1, {'dist': 3, 'capacity': 20}), (0, 2, {'dist': 5}), (1, 2, {'dist': 4})])
+
+        If `data` represents a key in the edge attribute dict, a dataview listing
+        each edge with its value for that specific key is returned:
+
+        >>> G.edges.data("dist")
+        EdgeDataView([(0, 1, 3), (0, 2, 5), (1, 2, 4)])
+
+        `nbunch` can be used to limit the edges:
+
+        >>> G.edges.data("dist", nbunch=[0])
+        EdgeDataView([(0, 1, 3), (0, 2, 5)])
+
+        If a specific key is not found in an edge attribute dict, the value
+        specified by `default` is used:
+
+        >>> G.edges.data("capacity")
+        EdgeDataView([(0, 1, 20), (0, 2, None), (1, 2, None)])
+
+        Note that there is no check that the `data` key is present in any of
+        the edge attribute dictionaries:
+
+        >>> G.edges.data("speed")
+        EdgeDataView([(0, 1, None), (0, 2, None), (1, 2, None)])
+        """
+        if nbunch is None and data is False:
+            return self
+        return self.dataview(self, nbunch, data, default=default)
+
+    # String Methods
+    def __str__(self):
+        return str(list(self))
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({list(self)})"
+
+
+class EdgeView(OutEdgeView):
+    """A EdgeView class for edges of a Graph
+
+    This densely packed View allows iteration over edges, data lookup
+    like a dict and set operations on edges represented by node-tuples.
+    In addition, edge data can be controlled by calling this object
+    possibly creating an EdgeDataView. Typically edges are iterated over
+    and reported as `(u, v)` node tuples or `(u, v, key)` node/key tuples
+    for multigraphs. Those edge representations can also be using to
+    lookup the data dict for any edge. Set operations also are available
+    where those tuples are the elements of the set.
+    Calling this object with optional arguments `data`, `default` and `keys`
+    controls the form of the tuple (see EdgeDataView). Optional argument
+    `nbunch` allows restriction to edges only involving certain nodes.
+
+    If `data is False` (the default) then iterate over 2-tuples `(u, v)`.
+    If `data is True` iterate over 3-tuples `(u, v, datadict)`.
+    Otherwise iterate over `(u, v, datadict.get(data, default))`.
+    For Multigraphs, if `keys is True`, replace `u, v` with `u, v, key` above.
+
+    Parameters
+    ==========
+    graph : NetworkX graph-like class
+    nbunch : (default= all nodes in graph) only report edges with these nodes
+    keys : (only for MultiGraph. default=False) report edge key in tuple
+    data : bool or string (default=False) see above
+    default : object (default=None)
+
+    Examples
+    ========
+    >>> G = nx.path_graph(4)
+    >>> EV = G.edges()
+    >>> (2, 3) in EV
+    True
+    >>> for u, v in EV:
+    ...     print((u, v))
+    (0, 1)
+    (1, 2)
+    (2, 3)
+    >>> assert EV & {(1, 2), (3, 4)} == {(1, 2)}
+
+    >>> EVdata = G.edges(data="color", default="aqua")
+    >>> G.add_edge(2, 3, color="blue")
+    >>> assert (2, 3, "blue") in EVdata
+    >>> for u, v, c in EVdata:
+    ...     print(f"({u}, {v}) has color: {c}")
+    (0, 1) has color: aqua
+    (1, 2) has color: aqua
+    (2, 3) has color: blue
+
+    >>> EVnbunch = G.edges(nbunch=2)
+    >>> assert (2, 3) in EVnbunch
+    >>> assert (0, 1) not in EVnbunch
+    >>> for u, v in EVnbunch:
+    ...     assert u == 2 or v == 2
+
+    >>> MG = nx.path_graph(4, create_using=nx.MultiGraph)
+    >>> EVmulti = MG.edges(keys=True)
+    >>> (2, 3, 0) in EVmulti
+    True
+    >>> (2, 3) in EVmulti  # 2-tuples work even when keys is True
+    True
+    >>> key = MG.add_edge(2, 3)
+    >>> for u, v, k in EVmulti:
+    ...     print((u, v, k))
+    (0, 1, 0)
+    (1, 2, 0)
+    (2, 3, 0)
+    (2, 3, 1)
+    """
+
+    __slots__ = ()
+
+    dataview = EdgeDataView
+
+    def __len__(self):
+        num_nbrs = (len(nbrs) + (n in nbrs) for n, nbrs in self._nodes_nbrs())
+        return sum(num_nbrs) // 2
+
+    def __iter__(self):
+        seen = {}
+        for n, nbrs in self._nodes_nbrs():
+            for nbr in list(nbrs):
+                if nbr not in seen:
+                    yield (n, nbr)
+            seen[n] = 1
+        del seen
+
+    def __contains__(self, e):
+        try:
+            u, v = e[:2]
+            return v in self._adjdict[u] or u in self._adjdict[v]
+        except (KeyError, ValueError):
+            return False
+
+
+class InEdgeView(OutEdgeView):
+    """A EdgeView class for inward edges of a DiGraph"""
+
+    __slots__ = ()
+
+    def __setstate__(self, state):
+        self._graph = state["_graph"]
+        self._adjdict = state["_adjdict"]
+        self._nodes_nbrs = self._adjdict.items
+
+    dataview = InEdgeDataView
+
+    def __init__(self, G):
+        self._graph = G
+        self._adjdict = G._pred if hasattr(G, "pred") else G._adj
+        self._nodes_nbrs = self._adjdict.items
+
+    def __iter__(self):
+        for n, nbrs in self._nodes_nbrs():
+            for nbr in nbrs:
+                yield (nbr, n)
+
+    def __contains__(self, e):
+        try:
+            u, v = e
+            return u in self._adjdict[v]
+        except KeyError:
+            return False
+
+    def __getitem__(self, e):
+        if isinstance(e, slice):
+            raise nx.NetworkXError(
+                f"{type(self).__name__} does not support slicing, "
+                f"try list(G.in_edges)[{e.start}:{e.stop}:{e.step}]"
+            )
+        u, v = e
+        return self._adjdict[v][u]
+
+
+class OutMultiEdgeView(OutEdgeView):
+    """A EdgeView class for outward edges of a MultiDiGraph"""
+
+    __slots__ = ()
+
+    dataview = OutMultiEdgeDataView
+
+    def __len__(self):
+        return sum(
+            len(kdict) for n, nbrs in self._nodes_nbrs() for nbr, kdict in nbrs.items()
+        )
+
+    def __iter__(self):
+        for n, nbrs in self._nodes_nbrs():
+            for nbr, kdict in nbrs.items():
+                for key in kdict:
+                    yield (n, nbr, key)
+
+    def __contains__(self, e):
+        N = len(e)
+        if N == 3:
+            u, v, k = e
+        elif N == 2:
+            u, v = e
+            k = 0
+        else:
+            raise ValueError("MultiEdge must have length 2 or 3")
+        try:
+            return k in self._adjdict[u][v]
+        except KeyError:
+            return False
+
+    def __getitem__(self, e):
+        if isinstance(e, slice):
+            raise nx.NetworkXError(
+                f"{type(self).__name__} does not support slicing, "
+                f"try list(G.edges)[{e.start}:{e.stop}:{e.step}]"
+            )
+        u, v, k = e
+        return self._adjdict[u][v][k]
+
+    def __call__(self, nbunch=None, data=False, *, default=None, keys=False):
+        if nbunch is None and data is False and keys is True:
+            return self
+        return self.dataview(self, nbunch, data, default=default, keys=keys)
+
+    def data(self, data=True, default=None, nbunch=None, keys=False):
+        if nbunch is None and data is False and keys is True:
+            return self
+        return self.dataview(self, nbunch, data, default=default, keys=keys)
+
+
+class MultiEdgeView(OutMultiEdgeView):
+    """A EdgeView class for edges of a MultiGraph"""
+
+    __slots__ = ()
+
+    dataview = MultiEdgeDataView
+
+    def __len__(self):
+        return sum(1 for e in self)
+
+    def __iter__(self):
+        seen = {}
+        for n, nbrs in self._nodes_nbrs():
+            for nbr, kd in nbrs.items():
+                if nbr not in seen:
+                    for k, dd in kd.items():
+                        yield (n, nbr, k)
+            seen[n] = 1
+        del seen
+
+
+class InMultiEdgeView(OutMultiEdgeView):
+    """A EdgeView class for inward edges of a MultiDiGraph"""
+
+    __slots__ = ()
+
+    def __setstate__(self, state):
+        self._graph = state["_graph"]
+        self._adjdict = state["_adjdict"]
+        self._nodes_nbrs = self._adjdict.items
+
+    dataview = InMultiEdgeDataView
+
+    def __init__(self, G):
+        self._graph = G
+        self._adjdict = G._pred if hasattr(G, "pred") else G._adj
+        self._nodes_nbrs = self._adjdict.items
+
+    def __iter__(self):
+        for n, nbrs in self._nodes_nbrs():
+            for nbr, kdict in nbrs.items():
+                for key in kdict:
+                    yield (nbr, n, key)
+
+    def __contains__(self, e):
+        N = len(e)
+        if N == 3:
+            u, v, k = e
+        elif N == 2:
+            u, v = e
+            k = 0
+        else:
+            raise ValueError("MultiEdge must have length 2 or 3")
+        try:
+            return k in self._adjdict[v][u]
+        except KeyError:
+            return False
+
+    def __getitem__(self, e):
+        if isinstance(e, slice):
+            raise nx.NetworkXError(
+                f"{type(self).__name__} does not support slicing, "
+                f"try list(G.in_edges)[{e.start}:{e.stop}:{e.step}]"
+            )
+        u, v, k = e
+        return self._adjdict[v][u][k]
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/__init__.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f53309d4da23a445bcce8cb7570a6de364452b5
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/__init__.py
@@ -0,0 +1,7 @@
+# graph drawing and interface to graphviz
+
+from .layout import *
+from .nx_latex import *
+from .nx_pylab import *
+from . import nx_agraph
+from . import nx_pydot
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/layout.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/layout.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a447d5d59bc240657c8a22a092b0f3798f5b3fc
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/layout.py
@@ -0,0 +1,2028 @@
+"""
+******
+Layout
+******
+
+Node positioning algorithms for graph drawing.
+
+For `random_layout()` the possible resulting shape
+is a square of side [0, scale] (default: [0, 1])
+Changing `center` shifts the layout by that amount.
+
+For the other layout routines, the extent is
+[center - scale, center + scale] (default: [-1, 1]).
+
+Warning: Most layout routines have only been tested in 2-dimensions.
+
+"""
+
+import networkx as nx
+from networkx.utils import np_random_state
+
+__all__ = [
+    "bipartite_layout",
+    "circular_layout",
+    "forceatlas2_layout",
+    "kamada_kawai_layout",
+    "random_layout",
+    "rescale_layout",
+    "rescale_layout_dict",
+    "shell_layout",
+    "spring_layout",
+    "spectral_layout",
+    "planar_layout",
+    "fruchterman_reingold_layout",
+    "spiral_layout",
+    "multipartite_layout",
+    "bfs_layout",
+    "arf_layout",
+]
+
+
+def _process_params(G, center, dim):
+    # Some boilerplate code.
+    import numpy as np
+
+    if not isinstance(G, nx.Graph):
+        empty_graph = nx.Graph()
+        empty_graph.add_nodes_from(G)
+        G = empty_graph
+
+    if center is None:
+        center = np.zeros(dim)
+    else:
+        center = np.asarray(center)
+
+    if len(center) != dim:
+        msg = "length of center coordinates must match dimension of layout"
+        raise ValueError(msg)
+
+    return G, center
+
+
+@np_random_state(3)
+def random_layout(G, center=None, dim=2, seed=None, store_pos_as=None):
+    """Position nodes uniformly at random in the unit square.
+
+    For every node, a position is generated by choosing each of dim
+    coordinates uniformly at random on the interval [0.0, 1.0).
+
+    NumPy (http://scipy.org) is required for this function.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+
+    seed : int, RandomState instance or None  optional (default=None)
+        Set the random state for deterministic node layouts.
+        If int, `seed` is the seed used by the random number generator,
+        if numpy.random.RandomState instance, `seed` is the random
+        number generator,
+        if None, the random number generator is the RandomState instance used
+        by numpy.random.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.lollipop_graph(4, 3)
+    >>> pos = nx.random_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.random_layout(G, seed=42, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([0.37454012, 0.9507143 ], dtype=float32),
+     1: array([0.7319939, 0.5986585], dtype=float32),
+     2: array([0.15601864, 0.15599452], dtype=float32),
+     3: array([0.05808361, 0.8661761 ], dtype=float32),
+     4: array([0.601115 , 0.7080726], dtype=float32),
+     5: array([0.02058449, 0.96990985], dtype=float32),
+     6: array([0.83244264, 0.21233912], dtype=float32)}
+    """
+    import numpy as np
+
+    G, center = _process_params(G, center, dim)
+    pos = seed.rand(len(G), dim) + center
+    pos = pos.astype(np.float32)
+    pos = dict(zip(G, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+    return pos
+
+
+def circular_layout(G, scale=1, center=None, dim=2, store_pos_as=None):
+    # dim=2 only
+    """Position nodes on a circle.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+        If dim>2, the remaining dimensions are set to zero
+        in the returned positions.
+        If dim<2, a ValueError is raised.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Raises
+    ------
+    ValueError
+        If dim < 2
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.circular_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.circular_layout(G, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([9.99999986e-01, 2.18556937e-08]),
+     1: array([-3.57647606e-08,  1.00000000e+00]),
+     2: array([-9.9999997e-01, -6.5567081e-08]),
+     3: array([ 1.98715071e-08, -9.99999956e-01])}
+
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    """
+    import numpy as np
+
+    if dim < 2:
+        raise ValueError("cannot handle dimensions < 2")
+
+    G, center = _process_params(G, center, dim)
+
+    paddims = max(0, (dim - 2))
+
+    if len(G) == 0:
+        pos = {}
+    elif len(G) == 1:
+        pos = {nx.utils.arbitrary_element(G): center}
+    else:
+        # Discard the extra angle since it matches 0 radians.
+        theta = np.linspace(0, 1, len(G) + 1)[:-1] * 2 * np.pi
+        theta = theta.astype(np.float32)
+        pos = np.column_stack(
+            [np.cos(theta), np.sin(theta), np.zeros((len(G), paddims))]
+        )
+        pos = rescale_layout(pos, scale=scale) + center
+        pos = dict(zip(G, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+def shell_layout(
+    G, nlist=None, rotate=None, scale=1, center=None, dim=2, store_pos_as=None
+):
+    """Position nodes in concentric circles.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    nlist : list of lists
+       List of node lists for each shell.
+
+    rotate : angle in radians (default=pi/len(nlist))
+       Angle by which to rotate the starting position of each shell
+       relative to the starting position of the previous shell.
+       To recreate behavior before v2.5 use rotate=0.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout, currently only dim=2 is supported.
+        Other dimension values result in a ValueError.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Raises
+    ------
+    ValueError
+        If dim != 2
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> shells = [[0], [1, 2, 3]]
+    >>> pos = nx.shell_layout(G, shells)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.shell_layout(G, shells, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([0., 0.]),
+     1: array([-5.00000000e-01, -4.37113883e-08]),
+     2: array([ 0.24999996, -0.43301272]),
+     3: array([0.24999981, 0.43301281])}
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    """
+    import numpy as np
+
+    if dim != 2:
+        raise ValueError("can only handle 2 dimensions")
+
+    G, center = _process_params(G, center, dim)
+
+    if len(G) == 0:
+        return {}
+    if len(G) == 1:
+        return {nx.utils.arbitrary_element(G): center}
+
+    if nlist is None:
+        # draw the whole graph in one shell
+        nlist = [list(G)]
+
+    radius_bump = scale / len(nlist)
+
+    if len(nlist[0]) == 1:
+        # single node at center
+        radius = 0.0
+    else:
+        # else start at r=1
+        radius = radius_bump
+
+    if rotate is None:
+        rotate = np.pi / len(nlist)
+    first_theta = rotate
+    npos = {}
+    for nodes in nlist:
+        # Discard the last angle (endpoint=False) since 2*pi matches 0 radians
+        theta = (
+            np.linspace(0, 2 * np.pi, len(nodes), endpoint=False, dtype=np.float32)
+            + first_theta
+        )
+        pos = radius * np.column_stack([np.cos(theta), np.sin(theta)]) + center
+        npos.update(zip(nodes, pos))
+        radius += radius_bump
+        first_theta += rotate
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, npos, store_pos_as)
+    return npos
+
+
+def bipartite_layout(
+    G,
+    nodes=None,
+    align="vertical",
+    scale=1,
+    center=None,
+    aspect_ratio=4 / 3,
+    store_pos_as=None,
+):
+    """Position nodes in two straight lines.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    nodes : collection of nodes
+        Nodes in one node set of the graph. This set will be placed on
+        left or top. If `None` (the default), a node set is chosen arbitrarily
+        if the graph if bipartite.
+
+    align : string (default='vertical')
+        The alignment of nodes. Vertical or horizontal.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    aspect_ratio : number (default=4/3):
+        The ratio of the width to the height of the layout.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node.
+
+    Raises
+    ------
+    NetworkXError
+        If ``nodes=None`` and `G` is not bipartite.
+
+    Examples
+    --------
+    >>> G = nx.complete_bipartite_graph(3, 3)
+    >>> pos = nx.bipartite_layout(G)
+
+    The ordering of the layout (i.e. which nodes appear on the left/top) can
+    be specified with the `nodes` parameter:
+
+    >>> top, bottom = nx.bipartite.sets(G)
+    >>> pos = nx.bipartite_layout(G, nodes=bottom)  # "bottom" set appears on the left
+
+    `store_pos_as` can be used to store the node positions for the computed layout
+    directly on the nodes:
+
+    >>> _ = nx.bipartite_layout(G, nodes=bottom, store_pos_as="pos")
+    >>> from pprint import pprint
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([ 1.  , -0.75]),
+     1: array([1., 0.]),
+     2: array([1.  , 0.75]),
+     3: array([-1.  , -0.75]),
+     4: array([-1.,  0.]),
+     5: array([-1.  ,  0.75])}
+
+
+    The ``bipartite_layout`` function can be used with non-bipartite graphs
+    by explicitly specifying how the layout should be partitioned with `nodes`:
+
+    >>> G = nx.complete_graph(5)  # Non-bipartite
+    >>> pos = nx.bipartite_layout(G, nodes={0, 1, 2})
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    """
+
+    import numpy as np
+
+    if align not in ("vertical", "horizontal"):
+        msg = "align must be either vertical or horizontal."
+        raise ValueError(msg)
+
+    G, center = _process_params(G, center=center, dim=2)
+    if len(G) == 0:
+        return {}
+
+    height = 1
+    width = aspect_ratio * height
+    offset = (width / 2, height / 2)
+
+    if nodes is None:
+        top, bottom = nx.bipartite.sets(G)
+        nodes = list(G)
+    else:
+        top = set(nodes)
+        bottom = set(G) - top
+        # Preserves backward-compatible node ordering in returned pos dict
+        nodes = list(top) + list(bottom)
+
+    left_xs = np.repeat(0, len(top))
+    right_xs = np.repeat(width, len(bottom))
+    left_ys = np.linspace(0, height, len(top))
+    right_ys = np.linspace(0, height, len(bottom))
+
+    top_pos = np.column_stack([left_xs, left_ys]) - offset
+    bottom_pos = np.column_stack([right_xs, right_ys]) - offset
+
+    pos = np.concatenate([top_pos, bottom_pos])
+    pos = rescale_layout(pos, scale=scale) + center
+    if align == "horizontal":
+        pos = pos[:, ::-1]  # swap x and y coords
+    pos = dict(zip(nodes, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+@np_random_state(10)
+def spring_layout(
+    G,
+    k=None,
+    pos=None,
+    fixed=None,
+    iterations=50,
+    threshold=1e-4,
+    weight="weight",
+    scale=1,
+    center=None,
+    dim=2,
+    seed=None,
+    store_pos_as=None,
+    *,
+    method="auto",
+    gravity=1.0,
+):
+    """Position nodes using Fruchterman-Reingold force-directed algorithm.
+
+    The algorithm simulates a force-directed representation of the network
+    treating edges as springs holding nodes close, while treating nodes
+    as repelling objects, sometimes called an anti-gravity force.
+    Simulation continues until the positions are close to an equilibrium.
+
+    There are some hard-coded values: minimal distance between
+    nodes (0.01) and "temperature" of 0.1 to ensure nodes don't fly away.
+    During the simulation, `k` helps determine the distance between nodes,
+    though `scale` and `center` determine the size and place after
+    rescaling occurs at the end of the simulation.
+
+    Fixing some nodes doesn't allow them to move in the simulation.
+    It also turns off the rescaling feature at the simulation's end.
+    In addition, setting `scale` to `None` turns off rescaling.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    k : float (default=None)
+        Optimal distance between nodes.  If None the distance is set to
+        1/sqrt(n) where n is the number of nodes.  Increase this value
+        to move nodes farther apart.
+
+    pos : dict or None  optional (default=None)
+        Initial positions for nodes as a dictionary with node as keys
+        and values as a coordinate list or tuple.  If None, then use
+        random initial positions.
+
+    fixed : list or None  optional (default=None)
+        Nodes to keep fixed at initial position.
+        Nodes not in ``G.nodes`` are ignored.
+        ValueError raised if `fixed` specified and `pos` not.
+
+    iterations : int  optional (default=50)
+        Maximum number of iterations taken
+
+    threshold: float optional (default = 1e-4)
+        Threshold for relative error in node position changes.
+        The iteration stops if the error is below this threshold.
+
+    weight : string or None   optional (default='weight')
+        The edge attribute that holds the numerical value used for
+        the edge weight.  Larger means a stronger attractive force.
+        If None, then all edge weights are 1.
+
+    scale : number or None (default: 1)
+        Scale factor for positions. Not used unless `fixed is None`.
+        If scale is None, no rescaling is performed.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+        Not used unless `fixed is None`.
+
+    dim : int
+        Dimension of layout.
+
+    seed : int, RandomState instance or None  optional (default=None)
+        Used only for the initial positions in the algorithm.
+        Set the random state for deterministic node layouts.
+        If int, `seed` is the seed used by the random number generator,
+        if numpy.random.RandomState instance, `seed` is the random
+        number generator,
+        if None, the random number generator is the RandomState instance used
+        by numpy.random.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    method : str  optional (default='auto')
+        The method to compute the layout.
+        If 'force', the force-directed Fruchterman-Reingold algorithm [1]_ is used.
+        If 'energy', the energy-based optimization algorithm [2]_ is used with absolute
+        values of edge weights and gravitational forces acting on each connected component.
+        If 'auto', we use 'force' if ``len(G) < 500`` and 'energy' otherwise.
+
+    gravity: float optional (default=1.0)
+        Used only for the method='energy'.
+        The positive coefficient of gravitational forces per connected component.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.spring_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.spring_layout(G, seed=123, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([-0.61520994, -1.        ]),
+     1: array([-0.21840965, -0.35501755]),
+     2: array([0.21841264, 0.35502078]),
+     3: array([0.61520696, 0.99999677])}
+
+    # The same using longer but equivalent function name
+    >>> pos = nx.fruchterman_reingold_layout(G)
+
+    References
+    ----------
+    .. [1] Fruchterman, Thomas MJ, and Edward M. Reingold.
+           "Graph drawing by force-directed placement."
+           Software: Practice and experience 21, no. 11 (1991): 1129-1164.
+           http://dx.doi.org/10.1002/spe.4380211102
+    .. [2] Hamaguchi, Hiroki, Naoki Marumo, and Akiko Takeda.
+           "Initial Placement for Fruchterman--Reingold Force Model With Coordinate Newton Direction."
+           arXiv preprint arXiv:2412.20317 (2024).
+           https://arxiv.org/abs/2412.20317
+    """
+    import numpy as np
+
+    if method not in ("auto", "force", "energy"):
+        raise ValueError("the method must be either auto, force, or energy.")
+    if method == "auto":
+        method = "force" if len(G) < 500 else "energy"
+
+    G, center = _process_params(G, center, dim)
+
+    if fixed is not None:
+        if pos is None:
+            raise ValueError("nodes are fixed without positions given")
+        for node in fixed:
+            if node not in pos:
+                raise ValueError("nodes are fixed without positions given")
+        nfixed = {node: i for i, node in enumerate(G)}
+        fixed = np.asarray([nfixed[node] for node in fixed if node in nfixed])
+
+    if pos is not None:
+        # Determine size of existing domain to adjust initial positions
+        dom_size = max(coord for pos_tup in pos.values() for coord in pos_tup)
+        if dom_size == 0:
+            dom_size = 1
+        pos_arr = seed.rand(len(G), dim) * dom_size + center
+
+        for i, n in enumerate(G):
+            if n in pos:
+                pos_arr[i] = np.asarray(pos[n])
+    else:
+        pos_arr = None
+        dom_size = 1
+
+    if len(G) == 0:
+        return {}
+    if len(G) == 1:
+        pos = {nx.utils.arbitrary_element(G.nodes()): center}
+        if store_pos_as is not None:
+            nx.set_node_attributes(G, pos, store_pos_as)
+        return pos
+
+    # Sparse matrix
+    if len(G) >= 500 or method == "energy":
+        A = nx.to_scipy_sparse_array(G, weight=weight, dtype="f")
+        if k is None and fixed is not None:
+            # We must adjust k by domain size for layouts not near 1x1
+            nnodes, _ = A.shape
+            k = dom_size / np.sqrt(nnodes)
+        pos = _sparse_fruchterman_reingold(
+            A, k, pos_arr, fixed, iterations, threshold, dim, seed, method, gravity
+        )
+    else:
+        A = nx.to_numpy_array(G, weight=weight)
+        if k is None and fixed is not None:
+            # We must adjust k by domain size for layouts not near 1x1
+            nnodes, _ = A.shape
+            k = dom_size / np.sqrt(nnodes)
+        pos = _fruchterman_reingold(
+            A, k, pos_arr, fixed, iterations, threshold, dim, seed
+        )
+    if fixed is None and scale is not None:
+        pos = rescale_layout(pos, scale=scale) + center
+    pos = dict(zip(G, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+fruchterman_reingold_layout = spring_layout
+
+
+@np_random_state(7)
+def _fruchterman_reingold(
+    A, k=None, pos=None, fixed=None, iterations=50, threshold=1e-4, dim=2, seed=None
+):
+    # Position nodes in adjacency matrix A using Fruchterman-Reingold
+    # Entry point for NetworkX graph is fruchterman_reingold_layout()
+    import numpy as np
+
+    try:
+        nnodes, _ = A.shape
+    except AttributeError as err:
+        msg = "fruchterman_reingold() takes an adjacency matrix as input"
+        raise nx.NetworkXError(msg) from err
+
+    if pos is None:
+        # random initial positions
+        pos = np.asarray(seed.rand(nnodes, dim), dtype=A.dtype)
+    else:
+        # make sure positions are of same type as matrix
+        pos = pos.astype(A.dtype)
+
+    # optimal distance between nodes
+    if k is None:
+        k = np.sqrt(1.0 / nnodes)
+    # the initial "temperature"  is about .1 of domain area (=1x1)
+    # this is the largest step allowed in the dynamics.
+    # We need to calculate this in case our fixed positions force our domain
+    # to be much bigger than 1x1
+    t = max(max(pos.T[0]) - min(pos.T[0]), max(pos.T[1]) - min(pos.T[1])) * 0.1
+    # simple cooling scheme.
+    # linearly step down by dt on each iteration so last iteration is size dt.
+    dt = t / (iterations + 1)
+    delta = np.zeros((pos.shape[0], pos.shape[0], pos.shape[1]), dtype=A.dtype)
+    # the inscrutable (but fast) version
+    # this is still O(V^2)
+    # could use multilevel methods to speed this up significantly
+    for iteration in range(iterations):
+        # matrix of difference between points
+        delta = pos[:, np.newaxis, :] - pos[np.newaxis, :, :]
+        # distance between points
+        distance = np.linalg.norm(delta, axis=-1)
+        # enforce minimum distance of 0.01
+        np.clip(distance, 0.01, None, out=distance)
+        # displacement "force"
+        displacement = np.einsum(
+            "ijk,ij->ik", delta, (k * k / distance**2 - A * distance / k)
+        )
+        # update positions
+        length = np.linalg.norm(displacement, axis=-1)
+        length = np.where(length < 0.01, 0.1, length)
+        delta_pos = np.einsum("ij,i->ij", displacement, t / length)
+        if fixed is not None:
+            # don't change positions of fixed nodes
+            delta_pos[fixed] = 0.0
+        pos += delta_pos
+        # cool temperature
+        t -= dt
+        if (np.linalg.norm(delta_pos) / nnodes) < threshold:
+            break
+    return pos
+
+
+@np_random_state(7)
+def _sparse_fruchterman_reingold(
+    A,
+    k=None,
+    pos=None,
+    fixed=None,
+    iterations=50,
+    threshold=1e-4,
+    dim=2,
+    seed=None,
+    method="energy",
+    gravity=1.0,
+):
+    # Position nodes in adjacency matrix A using Fruchterman-Reingold
+    # Entry point for NetworkX graph is fruchterman_reingold_layout()
+    # Sparse version
+    import numpy as np
+    import scipy as sp
+
+    try:
+        nnodes, _ = A.shape
+    except AttributeError as err:
+        msg = "fruchterman_reingold() takes an adjacency matrix as input"
+        raise nx.NetworkXError(msg) from err
+
+    if pos is None:
+        # random initial positions
+        pos = np.asarray(seed.rand(nnodes, dim), dtype=A.dtype)
+    else:
+        # make sure positions are of same type as matrix
+        pos = pos.astype(A.dtype)
+
+    # no fixed nodes
+    if fixed is None:
+        fixed = []
+
+    # optimal distance between nodes
+    if k is None:
+        k = np.sqrt(1.0 / nnodes)
+
+    if method == "energy":
+        return _energy_fruchterman_reingold(
+            A, nnodes, k, pos, fixed, iterations, threshold, dim, gravity
+        )
+
+    # make sure we have a LIst of Lists representation
+    try:
+        A = A.tolil()
+    except AttributeError:
+        A = (sp.sparse.coo_array(A)).tolil()
+
+    # the initial "temperature"  is about .1 of domain area (=1x1)
+    # this is the largest step allowed in the dynamics.
+    t = max(max(pos.T[0]) - min(pos.T[0]), max(pos.T[1]) - min(pos.T[1])) * 0.1
+    # simple cooling scheme.
+    # linearly step down by dt on each iteration so last iteration is size dt.
+    dt = t / (iterations + 1)
+
+    displacement = np.zeros((dim, nnodes))
+    for iteration in range(iterations):
+        displacement *= 0
+        # loop over rows
+        for i in range(A.shape[0]):
+            if i in fixed:
+                continue
+            # difference between this row's node position and all others
+            delta = (pos[i] - pos).T
+            # distance between points
+            distance = np.sqrt((delta**2).sum(axis=0))
+            # enforce minimum distance of 0.01
+            distance = np.where(distance < 0.01, 0.01, distance)
+            # the adjacency matrix row
+            Ai = A.getrowview(i).toarray()  # TODO: revisit w/ sparse 1D container
+            # displacement "force"
+            displacement[:, i] += (
+                delta * (k * k / distance**2 - Ai * distance / k)
+            ).sum(axis=1)
+        # update positions
+        length = np.sqrt((displacement**2).sum(axis=0))
+        length = np.where(length < 0.01, 0.1, length)
+        delta_pos = (displacement * t / length).T
+        pos += delta_pos
+        # cool temperature
+        t -= dt
+        if (np.linalg.norm(delta_pos) / nnodes) < threshold:
+            break
+    return pos
+
+
+def _energy_fruchterman_reingold(
+    A, nnodes, k, pos, fixed, iterations, threshold, dim, gravity
+):
+    # Entry point for NetworkX graph is fruchterman_reingold_layout()
+    # energy-based version
+    import numpy as np
+    import scipy as sp
+
+    if gravity <= 0:
+        raise ValueError(f"the gravity must be positive.")
+
+    # make sure we have a Compressed Sparse Row format
+    try:
+        A = A.tocsr()
+    except AttributeError:
+        A = sp.sparse.csr_array(A)
+
+    # Take absolute values of edge weights and symmetrize it
+    A = np.abs(A)
+    A = (A + A.T) / 2
+
+    n_components, labels = sp.sparse.csgraph.connected_components(A, directed=False)
+    bincount = np.bincount(labels)
+    batchsize = 500
+
+    def _cost_FR(x):
+        pos = x.reshape((nnodes, dim))
+        grad = np.zeros((nnodes, dim))
+        cost = 0.0
+        for l in range(0, nnodes, batchsize):
+            r = min(l + batchsize, nnodes)
+            # difference between selected node positions and all others
+            delta = pos[l:r, np.newaxis, :] - pos[np.newaxis, :, :]
+            # distance between points with a minimum distance of 1e-5
+            distance2 = np.sum(delta * delta, axis=2)
+            distance2 = np.maximum(distance2, 1e-10)
+            distance = np.sqrt(distance2)
+            # temporary variable for calculation
+            Ad = A[l:r] * distance
+            # attractive forces and repulsive forces
+            grad[l:r] = 2 * np.einsum("ij,ijk->ik", Ad / k - k**2 / distance2, delta)
+            # integrated attractive forces
+            cost += np.sum(Ad * distance2) / (3 * k)
+            # integrated repulsive forces
+            cost -= k**2 * np.sum(np.log(distance))
+        # gravitational force from the centroids of connected components to (0.5, ..., 0.5)^T
+        centers = np.zeros((n_components, dim))
+        np.add.at(centers, labels, pos)
+        delta0 = centers / bincount[:, np.newaxis] - 0.5
+        grad += gravity * delta0[labels]
+        cost += gravity * 0.5 * np.sum(bincount * np.linalg.norm(delta0, axis=1) ** 2)
+        # fix positions of fixed nodes
+        grad[fixed] = 0.0
+        return cost, grad.ravel()
+
+    # Optimization of the energy function by L-BFGS algorithm
+    options = {"maxiter": iterations, "gtol": threshold}
+    return sp.optimize.minimize(
+        _cost_FR, pos.ravel(), method="L-BFGS-B", jac=True, options=options
+    ).x.reshape((nnodes, dim))
+
+
+def kamada_kawai_layout(
+    G,
+    dist=None,
+    pos=None,
+    weight="weight",
+    scale=1,
+    center=None,
+    dim=2,
+    store_pos_as=None,
+):
+    """Position nodes using Kamada-Kawai path-length cost-function.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    dist : dict (default=None)
+        A two-level dictionary of optimal distances between nodes,
+        indexed by source and destination node.
+        If None, the distance is computed using shortest_path_length().
+
+    pos : dict or None  optional (default=None)
+        Initial positions for nodes as a dictionary with node as keys
+        and values as a coordinate list or tuple.  If None, then use
+        circular_layout() for dim >= 2 and a linear layout for dim == 1.
+
+    weight : string or None   optional (default='weight')
+        The edge attribute that holds the numerical value used for
+        the edge weight.  If None, then all edge weights are 1.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.kamada_kawai_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.kamada_kawai_layout(G, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([0.99996577, 0.99366857]),
+     1: array([0.32913544, 0.33543827]),
+     2: array([-0.33544334, -0.32910684]),
+     3: array([-0.99365787, -1.        ])}
+    """
+    import numpy as np
+
+    G, center = _process_params(G, center, dim)
+    nNodes = len(G)
+    if nNodes == 0:
+        return {}
+
+    if dist is None:
+        dist = dict(nx.shortest_path_length(G, weight=weight))
+    dist_mtx = 1e6 * np.ones((nNodes, nNodes))
+    for row, nr in enumerate(G):
+        if nr not in dist:
+            continue
+        rdist = dist[nr]
+        for col, nc in enumerate(G):
+            if nc not in rdist:
+                continue
+            dist_mtx[row][col] = rdist[nc]
+
+    if pos is None:
+        if dim >= 3:
+            pos = random_layout(G, dim=dim)
+        elif dim == 2:
+            pos = circular_layout(G, dim=dim)
+        else:
+            pos = dict(zip(G, np.linspace(0, 1, len(G))))
+    pos_arr = np.array([pos[n] for n in G])
+
+    pos = _kamada_kawai_solve(dist_mtx, pos_arr, dim)
+
+    pos = rescale_layout(pos, scale=scale) + center
+    pos = dict(zip(G, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+def _kamada_kawai_solve(dist_mtx, pos_arr, dim):
+    # Anneal node locations based on the Kamada-Kawai cost-function,
+    # using the supplied matrix of preferred inter-node distances,
+    # and starting locations.
+
+    import numpy as np
+    import scipy as sp
+
+    meanwt = 1e-3
+    costargs = (np, 1 / (dist_mtx + np.eye(dist_mtx.shape[0]) * 1e-3), meanwt, dim)
+
+    optresult = sp.optimize.minimize(
+        _kamada_kawai_costfn,
+        pos_arr.ravel(),
+        method="L-BFGS-B",
+        args=costargs,
+        jac=True,
+    )
+
+    return optresult.x.reshape((-1, dim))
+
+
+def _kamada_kawai_costfn(pos_vec, np, invdist, meanweight, dim):
+    # Cost-function and gradient for Kamada-Kawai layout algorithm
+    nNodes = invdist.shape[0]
+    pos_arr = pos_vec.reshape((nNodes, dim))
+
+    delta = pos_arr[:, np.newaxis, :] - pos_arr[np.newaxis, :, :]
+    nodesep = np.linalg.norm(delta, axis=-1)
+    direction = np.einsum("ijk,ij->ijk", delta, 1 / (nodesep + np.eye(nNodes) * 1e-3))
+
+    offset = nodesep * invdist - 1.0
+    offset[np.diag_indices(nNodes)] = 0
+
+    cost = 0.5 * np.sum(offset**2)
+    grad = np.einsum("ij,ij,ijk->ik", invdist, offset, direction) - np.einsum(
+        "ij,ij,ijk->jk", invdist, offset, direction
+    )
+
+    # Additional parabolic term to encourage mean position to be near origin:
+    sumpos = np.sum(pos_arr, axis=0)
+    cost += 0.5 * meanweight * np.sum(sumpos**2)
+    grad += meanweight * sumpos
+
+    return (cost, grad.ravel())
+
+
+def spectral_layout(G, weight="weight", scale=1, center=None, dim=2, store_pos_as=None):
+    """Position nodes using the eigenvectors of the graph Laplacian.
+
+    Using the unnormalized Laplacian, the layout shows possible clusters of
+    nodes which are an approximation of the ratio cut. If dim is the number of
+    dimensions then the positions are the entries of the dim eigenvectors
+    corresponding to the ascending eigenvalues starting from the second one.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    weight : string or None   optional (default='weight')
+        The edge attribute that holds the numerical value used for
+        the edge weight.  If None, then all edge weights are 1.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.spectral_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.spectral_layout(G, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([-1.        ,  0.76536686]),
+     1: array([-0.41421356, -0.76536686]),
+     2: array([ 0.41421356, -0.76536686]),
+     3: array([1.        , 0.76536686])}
+
+
+    Notes
+    -----
+    Directed graphs will be considered as undirected graphs when
+    positioning the nodes.
+
+    For larger graphs (>500 nodes) this will use the SciPy sparse
+    eigenvalue solver (ARPACK).
+    """
+    # handle some special cases that break the eigensolvers
+    import numpy as np
+
+    G, center = _process_params(G, center, dim)
+
+    if len(G) <= 2:
+        if len(G) == 0:
+            pos = np.array([])
+        elif len(G) == 1:
+            pos = np.array([center])
+        else:
+            pos = np.array([np.zeros(dim), np.array(center) * 2.0])
+        return dict(zip(G, pos))
+    try:
+        # Sparse matrix
+        if len(G) < 500:  # dense solver is faster for small graphs
+            raise ValueError
+        A = nx.to_scipy_sparse_array(G, weight=weight, dtype="d")
+        # Symmetrize directed graphs
+        if G.is_directed():
+            A = A + np.transpose(A)
+        pos = _sparse_spectral(A, dim)
+    except (ImportError, ValueError):
+        # Dense matrix
+        A = nx.to_numpy_array(G, weight=weight)
+        # Symmetrize directed graphs
+        if G.is_directed():
+            A += A.T
+        pos = _spectral(A, dim)
+
+    pos = rescale_layout(pos, scale=scale) + center
+    pos = dict(zip(G, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+def _spectral(A, dim=2):
+    # Input adjacency matrix A
+    # Uses dense eigenvalue solver from numpy
+    import numpy as np
+
+    try:
+        nnodes, _ = A.shape
+    except AttributeError as err:
+        msg = "spectral() takes an adjacency matrix as input"
+        raise nx.NetworkXError(msg) from err
+
+    # form Laplacian matrix where D is diagonal of degrees
+    D = np.identity(nnodes, dtype=A.dtype) * np.sum(A, axis=1)
+    L = D - A
+
+    eigenvalues, eigenvectors = np.linalg.eig(L)
+    # sort and keep smallest nonzero
+    index = np.argsort(eigenvalues)[1 : dim + 1]  # 0 index is zero eigenvalue
+    return np.real(eigenvectors[:, index])
+
+
+def _sparse_spectral(A, dim=2):
+    # Input adjacency matrix A
+    # Uses sparse eigenvalue solver from scipy
+    # Could use multilevel methods here, see Koren "On spectral graph drawing"
+    import numpy as np
+    import scipy as sp
+
+    try:
+        nnodes, _ = A.shape
+    except AttributeError as err:
+        msg = "sparse_spectral() takes an adjacency matrix as input"
+        raise nx.NetworkXError(msg) from err
+
+    # form Laplacian matrix
+    # TODO: Rm csr_array wrapper in favor of spdiags array constructor when available
+    D = sp.sparse.csr_array(sp.sparse.spdiags(A.sum(axis=1), 0, nnodes, nnodes))
+    L = D - A
+
+    k = dim + 1
+    # number of Lanczos vectors for ARPACK solver.What is the right scaling?
+    ncv = max(2 * k + 1, int(np.sqrt(nnodes)))
+    # return smallest k eigenvalues and eigenvectors
+    eigenvalues, eigenvectors = sp.sparse.linalg.eigsh(L, k, which="SM", ncv=ncv)
+    index = np.argsort(eigenvalues)[1:k]  # 0 index is zero eigenvalue
+    return np.real(eigenvectors[:, index])
+
+
+def planar_layout(G, scale=1, center=None, dim=2, store_pos_as=None):
+    """Position nodes without edge intersections.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G. If G is of type
+        nx.PlanarEmbedding, the positions are selected accordingly.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int
+        Dimension of layout.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Raises
+    ------
+    NetworkXException
+        If G is not planar
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.planar_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.planar_layout(G, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([-0.77777778, -0.33333333]),
+     1: array([ 1.        , -0.33333333]),
+     2: array([0.11111111, 0.55555556]),
+     3: array([-0.33333333,  0.11111111])}
+    """
+    import numpy as np
+
+    if dim != 2:
+        raise ValueError("can only handle 2 dimensions")
+
+    G, center = _process_params(G, center, dim)
+
+    if len(G) == 0:
+        return {}
+
+    if isinstance(G, nx.PlanarEmbedding):
+        embedding = G
+    else:
+        is_planar, embedding = nx.check_planarity(G)
+        if not is_planar:
+            raise nx.NetworkXException("G is not planar.")
+    pos = nx.combinatorial_embedding_to_pos(embedding)
+    node_list = list(embedding)
+    pos = np.vstack([pos[x] for x in node_list])
+    pos = pos.astype(np.float64)
+    pos = rescale_layout(pos, scale=scale) + center
+    pos = dict(zip(node_list, pos))
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+    return pos
+
+
+def spiral_layout(
+    G,
+    scale=1,
+    center=None,
+    dim=2,
+    resolution=0.35,
+    equidistant=False,
+    store_pos_as=None,
+):
+    """Position nodes in a spiral layout.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    dim : int, default=2
+        Dimension of layout, currently only dim=2 is supported.
+        Other dimension values result in a ValueError.
+
+    resolution : float, default=0.35
+        The compactness of the spiral layout returned.
+        Lower values result in more compressed spiral layouts.
+
+    equidistant : bool, default=False
+        If True, nodes will be positioned equidistant from each other
+        by decreasing angle further from center.
+        If False, nodes will be positioned at equal angles
+        from each other by increasing separation further from center.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node
+
+    Raises
+    ------
+    ValueError
+        If dim != 2
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.spiral_layout(G)
+    >>> nx.draw(G, pos=pos)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.spiral_layout(G, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([-0.64153279, -0.68555087]),
+     1: array([-0.03307913, -0.46344795]),
+     2: array([0.34927952, 0.14899882]),
+     3: array([0.32533239, 1.        ])}
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions.
+
+    """
+    import numpy as np
+
+    if dim != 2:
+        raise ValueError("can only handle 2 dimensions")
+
+    G, center = _process_params(G, center, dim)
+
+    if len(G) == 0:
+        return {}
+    if len(G) == 1:
+        pos = {nx.utils.arbitrary_element(G): center}
+        if store_pos_as is not None:
+            nx.set_node_attributes(G, pos, store_pos_as)
+        return pos
+
+    pos = []
+    if equidistant:
+        chord = 1
+        step = 0.5
+        theta = resolution
+        theta += chord / (step * theta)
+        for _ in range(len(G)):
+            r = step * theta
+            theta += chord / r
+            pos.append([np.cos(theta) * r, np.sin(theta) * r])
+
+    else:
+        dist = np.arange(len(G), dtype=float)
+        angle = resolution * dist
+        pos = np.transpose(dist * np.array([np.cos(angle), np.sin(angle)]))
+
+    pos = rescale_layout(np.array(pos), scale=scale) + center
+
+    pos = dict(zip(G, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+def multipartite_layout(
+    G, subset_key="subset", align="vertical", scale=1, center=None, store_pos_as=None
+):
+    """Position nodes in layers of straight lines.
+
+    Parameters
+    ----------
+    G : NetworkX graph or list of nodes
+        A position will be assigned to every node in G.
+
+    subset_key : string or dict (default='subset')
+        If a string, the key of node data in G that holds the node subset.
+        If a dict, keyed by layer number to the nodes in that layer/subset.
+
+    align : string (default='vertical')
+        The alignment of nodes. Vertical or horizontal.
+
+    scale : number (default: 1)
+        Scale factor for positions.
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.complete_multipartite_graph(28, 16, 10)
+    >>> pos = nx.multipartite_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> G = nx.complete_multipartite_graph(28, 16, 10)
+    >>> _ = nx.multipartite_layout(G, store_pos_as="pos")
+
+    or use a dict to provide the layers of the layout
+
+    >>> G = nx.Graph([(0, 1), (1, 2), (1, 3), (3, 4)])
+    >>> layers = {"a": [0], "b": [1], "c": [2, 3], "d": [4]}
+    >>> pos = nx.multipartite_layout(G, subset_key=layers)
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    Network does not need to be a complete multipartite graph. As long as nodes
+    have subset_key data, they will be placed in the corresponding layers.
+
+    """
+    import numpy as np
+
+    if align not in ("vertical", "horizontal"):
+        msg = "align must be either vertical or horizontal."
+        raise ValueError(msg)
+
+    G, center = _process_params(G, center=center, dim=2)
+    if len(G) == 0:
+        return {}
+
+    try:
+        # check if subset_key is dict-like
+        if len(G) != sum(len(nodes) for nodes in subset_key.values()):
+            raise nx.NetworkXError(
+                "all nodes must be in one subset of `subset_key` dict"
+            )
+    except AttributeError:
+        # subset_key is not a dict, hence a string
+        node_to_subset = nx.get_node_attributes(G, subset_key)
+        if len(node_to_subset) != len(G):
+            raise nx.NetworkXError(
+                f"all nodes need a subset_key attribute: {subset_key}"
+            )
+        subset_key = nx.utils.groups(node_to_subset)
+
+    # Sort by layer, if possible
+    try:
+        layers = dict(sorted(subset_key.items()))
+    except TypeError:
+        layers = subset_key
+
+    pos = None
+    nodes = []
+    width = len(layers)
+    for i, layer in enumerate(layers.values()):
+        height = len(layer)
+        xs = np.repeat(i, height)
+        ys = np.arange(0, height, dtype=float)
+        offset = ((width - 1) / 2, (height - 1) / 2)
+        layer_pos = np.column_stack([xs, ys]) - offset
+        if pos is None:
+            pos = layer_pos
+        else:
+            pos = np.concatenate([pos, layer_pos])
+        nodes.extend(layer)
+    pos = rescale_layout(pos, scale=scale) + center
+    if align == "horizontal":
+        pos = pos[:, ::-1]  # swap x and y coords
+    pos = dict(zip(nodes, pos))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+@np_random_state("seed")
+def arf_layout(
+    G,
+    pos=None,
+    scaling=1,
+    a=1.1,
+    etol=1e-6,
+    dt=1e-3,
+    max_iter=1000,
+    *,
+    seed=None,
+    store_pos_as=None,
+):
+    """Arf layout for networkx
+
+    The attractive and repulsive forces (arf) layout [1] improves the spring
+    layout in three ways. First, it prevents congestion of highly connected nodes
+    due to strong forcing between nodes. Second, it utilizes the layout space
+    more effectively by preventing large gaps that spring layout tends to create.
+    Lastly, the arf layout represents symmetries in the layout better than the
+    default spring layout.
+
+    Parameters
+    ----------
+    G : nx.Graph or nx.DiGraph
+        Networkx graph.
+    pos : dict
+        Initial  position of  the nodes.  If set  to None  a
+        random layout will be used.
+    scaling : float
+        Scales the radius of the circular layout space.
+    a : float
+        Strength of springs between connected nodes. Should be larger than 1.
+        The greater a, the clearer the separation of unconnected sub clusters.
+    etol : float
+        Gradient sum of spring forces must be larger than `etol` before successful
+        termination.
+    dt : float
+        Time step for force differential equation simulations.
+    max_iter : int
+        Max iterations before termination of the algorithm.
+    seed : int, RandomState instance or None  optional (default=None)
+        Set the random state for deterministic node layouts.
+        If int, `seed` is the seed used by the random number generator,
+        if numpy.random.RandomState instance, `seed` is the random
+        number generator,
+        if None, the random number generator is the RandomState instance used
+        by numpy.random.
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.grid_graph((5, 5))
+    >>> pos = nx.arf_layout(G)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> G = nx.grid_graph((5, 5))
+    >>> _ = nx.arf_layout(G, store_pos_as="pos")
+
+    References
+    ----------
+    .. [1] "Self-Organization Applied to Dynamic Network Layout", M. Geipel,
+            International Journal of Modern Physics C, 2007, Vol 18, No 10,
+            pp. 1537-1549.
+            https://doi.org/10.1142/S0129183107011558 https://arxiv.org/abs/0704.1748
+    """
+    import warnings
+
+    import numpy as np
+
+    if a <= 1:
+        msg = "The parameter a should be larger than 1"
+        raise ValueError(msg)
+
+    pos_tmp = nx.random_layout(G, seed=seed)
+    if pos is None:
+        pos = pos_tmp
+    else:
+        for node in G.nodes():
+            if node not in pos:
+                pos[node] = pos_tmp[node].copy()
+
+    # Initialize spring constant matrix
+    N = len(G)
+    # No nodes no computation
+    if N == 0:
+        return pos
+
+    # init force of springs
+    K = np.ones((N, N)) - np.eye(N)
+    node_order = {node: i for i, node in enumerate(G)}
+    for x, y in G.edges():
+        if x != y:
+            idx, jdx = (node_order[i] for i in (x, y))
+            K[idx, jdx] = a
+
+    # vectorize values
+    p = np.asarray(list(pos.values()))
+
+    # equation 10 in [1]
+    rho = scaling * np.sqrt(N)
+
+    # looping variables
+    error = etol + 1
+    n_iter = 0
+    while error > etol:
+        diff = p[:, np.newaxis] - p[np.newaxis]
+        A = np.linalg.norm(diff, axis=-1)[..., np.newaxis]
+        # attraction_force - repulsions force
+        # suppress nans due to division; caused by diagonal set to zero.
+        # Does not affect the computation due to nansum
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            change = K[..., np.newaxis] * diff - rho / A * diff
+        change = np.nansum(change, axis=0)
+        p += change * dt
+
+        error = np.linalg.norm(change, axis=-1).sum()
+        if n_iter > max_iter:
+            break
+        n_iter += 1
+
+    pos = dict(zip(G.nodes(), p))
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+@np_random_state("seed")
+@nx._dispatchable(edge_attrs="weight", mutates_input={"store_pos_as": 15})
+def forceatlas2_layout(
+    G,
+    pos=None,
+    *,
+    max_iter=100,
+    jitter_tolerance=1.0,
+    scaling_ratio=2.0,
+    gravity=1.0,
+    distributed_action=False,
+    strong_gravity=False,
+    node_mass=None,
+    node_size=None,
+    weight=None,
+    dissuade_hubs=False,
+    linlog=False,
+    seed=None,
+    dim=2,
+    store_pos_as=None,
+):
+    """Position nodes using the ForceAtlas2 force-directed layout algorithm.
+
+    This function applies the ForceAtlas2 layout algorithm [1]_ to a NetworkX graph,
+    positioning the nodes in a way that visually represents the structure of the graph.
+    The algorithm uses physical simulation to minimize the energy of the system,
+    resulting in a more readable layout.
+
+    Parameters
+    ----------
+    G : nx.Graph
+        A NetworkX graph to be laid out.
+    pos : dict or None, optional
+        Initial positions of the nodes. If None, random initial positions are used.
+    max_iter : int (default: 100)
+        Number of iterations for the layout optimization.
+    jitter_tolerance : float (default: 1.0)
+        Controls the tolerance for adjusting the speed of layout generation.
+    scaling_ratio : float (default: 2.0)
+        Determines the scaling of attraction and repulsion forces.
+    gravity : float (default: 1.0)
+        Determines the amount of attraction on nodes to the center. Prevents islands
+        (i.e. weakly connected or disconnected parts of the graph)
+        from drifting away.
+    distributed_action : bool (default: False)
+        Distributes the attraction force evenly among nodes.
+    strong_gravity : bool (default: False)
+        Applies a strong gravitational pull towards the center.
+    node_mass : dict or None, optional
+        Maps nodes to their masses, influencing the attraction to other nodes.
+    node_size : dict or None, optional
+        Maps nodes to their sizes, preventing crowding by creating a halo effect.
+    weight : string or None, optional (default: None)
+        The edge attribute that holds the numerical value used for
+        the edge weight. If None, then all edge weights are 1.
+    dissuade_hubs : bool (default: False)
+        Prevents the clustering of hub nodes.
+    linlog : bool (default: False)
+        Uses logarithmic attraction instead of linear.
+    seed : int, RandomState instance or None  optional (default=None)
+        Used only for the initial positions in the algorithm.
+        Set the random state for deterministic node layouts.
+        If int, `seed` is the seed used by the random number generator,
+        if numpy.random.RandomState instance, `seed` is the random
+        number generator,
+        if None, the random number generator is the RandomState instance used
+        by numpy.random.
+    dim : int (default: 2)
+        Sets the dimensions for the layout. Ignored if `pos` is provided.
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Examples
+    --------
+    >>> import networkx as nx
+    >>> G = nx.florentine_families_graph()
+    >>> pos = nx.forceatlas2_layout(G)
+    >>> nx.draw(G, pos=pos)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> pos = nx.forceatlas2_layout(G, store_pos_as="pos")
+    >>> _ = nx.forceatlas2_layout(G, store_pos_as="pos")
+
+    References
+    ----------
+    .. [1] Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014).
+           ForceAtlas2, a continuous graph layout algorithm for handy network
+           visualization designed for the Gephi software. PloS one, 9(6), e98679.
+           https://doi.org/10.1371/journal.pone.0098679
+    """
+    import numpy as np
+
+    if len(G) == 0:
+        return {}
+    # parse optional pos positions
+    if pos is None:
+        pos = nx.random_layout(G, dim=dim, seed=seed)
+        pos_arr = np.array(list(pos.values()))
+    elif len(pos) == len(G):
+        pos_arr = np.array([pos[node].copy() for node in G])
+    else:
+        # set random node pos within the initial pos values
+        pos_init = np.array(list(pos.values()))
+        max_pos = pos_init.max(axis=0)
+        min_pos = pos_init.min(axis=0)
+        dim = max_pos.size
+        pos_arr = min_pos + seed.rand(len(G), dim) * (max_pos - min_pos)
+        for idx, node in enumerate(G):
+            if node in pos:
+                pos_arr[idx] = pos[node].copy()
+
+    mass = np.zeros(len(G))
+    size = np.zeros(len(G))
+
+    # Only adjust for size when the users specifies size other than default (1)
+    adjust_sizes = False
+    if node_size is None:
+        node_size = {}
+    else:
+        adjust_sizes = True
+
+    if node_mass is None:
+        node_mass = {}
+
+    for idx, node in enumerate(G):
+        mass[idx] = node_mass.get(node, G.degree(node) + 1)
+        size[idx] = node_size.get(node, 1)
+
+    n = len(G)
+    gravities = np.zeros((n, dim))
+    attraction = np.zeros((n, dim))
+    repulsion = np.zeros((n, dim))
+    A = nx.to_numpy_array(G, weight=weight)
+
+    def estimate_factor(n, swing, traction, speed, speed_efficiency, jitter_tolerance):
+        """Computes the scaling factor for the force in the ForceAtlas2 layout algorithm.
+
+        This   helper  function   adjusts   the  speed   and
+        efficiency  of the  layout generation  based on  the
+        current state of  the system, such as  the number of
+        nodes, current swing, and traction forces.
+
+        Parameters
+        ----------
+        n : int
+            Number of nodes in the graph.
+        swing : float
+            The current swing, representing the oscillation of the nodes.
+        traction : float
+            The current traction force, representing the attraction between nodes.
+        speed : float
+            The current speed of the layout generation.
+        speed_efficiency : float
+            The efficiency of the current speed, influencing how fast the layout converges.
+        jitter_tolerance : float
+            The tolerance for jitter, affecting how much speed adjustment is allowed.
+
+        Returns
+        -------
+        tuple
+            A tuple containing the updated speed and speed efficiency.
+
+        Notes
+        -----
+        This function is a part of the ForceAtlas2 layout algorithm and is used to dynamically adjust the
+        layout parameters to achieve an optimal and stable visualization.
+
+        """
+        import numpy as np
+
+        # estimate jitter
+        opt_jitter = 0.05 * np.sqrt(n)
+        min_jitter = np.sqrt(opt_jitter)
+        max_jitter = 10
+        min_speed_efficiency = 0.05
+
+        other = min(max_jitter, opt_jitter * traction / n**2)
+        jitter = jitter_tolerance * max(min_jitter, other)
+
+        if swing / traction > 2.0:
+            if speed_efficiency > min_speed_efficiency:
+                speed_efficiency *= 0.5
+            jitter = max(jitter, jitter_tolerance)
+        if swing == 0:
+            target_speed = np.inf
+        else:
+            target_speed = jitter * speed_efficiency * traction / swing
+
+        if swing > jitter * traction:
+            if speed_efficiency > min_speed_efficiency:
+                speed_efficiency *= 0.7
+        elif speed < 1000:
+            speed_efficiency *= 1.3
+
+        max_rise = 0.5
+        speed = speed + min(target_speed - speed, max_rise * speed)
+        return speed, speed_efficiency
+
+    speed = 1
+    speed_efficiency = 1
+    swing = 1
+    traction = 1
+    for _ in range(max_iter):
+        # compute pairwise difference
+        diff = pos_arr[:, None] - pos_arr[None]
+        # compute pairwise distance
+        distance = np.linalg.norm(diff, axis=-1)
+
+        # linear attraction
+        if linlog:
+            attraction = -np.log(1 + distance) / distance
+            np.fill_diagonal(attraction, 0)
+            attraction = np.einsum("ij, ij -> ij", attraction, A)
+            attraction = np.einsum("ijk, ij -> ik", diff, attraction)
+
+        else:
+            attraction = -np.einsum("ijk, ij -> ik", diff, A)
+
+        if distributed_action:
+            attraction /= mass[:, None]
+
+        # repulsion
+        tmp = mass[:, None] @ mass[None]
+        if adjust_sizes:
+            distance += -size[:, None] - size[None]
+
+        d2 = distance**2
+        # remove self-interaction
+        np.fill_diagonal(tmp, 0)
+        np.fill_diagonal(d2, 1)
+        factor = (tmp / d2) * scaling_ratio
+        repulsion = np.einsum("ijk, ij -> ik", diff, factor)
+
+        # gravity
+        pos_centered = pos_arr - np.mean(pos_arr, axis=0)
+        if strong_gravity:
+            gravities = -gravity * mass[:, None] * pos_centered
+        else:
+            # hide warnings for divide by zero. Then change nan to 0
+            with np.errstate(divide="ignore", invalid="ignore"):
+                unit_vec = pos_centered / np.linalg.norm(pos_centered, axis=-1)[:, None]
+            unit_vec = np.nan_to_num(unit_vec, nan=0)
+            gravities = -gravity * mass[:, None] * unit_vec
+
+        # total forces
+        update = attraction + repulsion + gravities
+
+        # compute total swing and traction
+        swing += (mass * np.linalg.norm(pos_arr - update, axis=-1)).sum()
+        traction += (0.5 * mass * np.linalg.norm(pos_arr + update, axis=-1)).sum()
+
+        speed, speed_efficiency = estimate_factor(
+            n,
+            swing,
+            traction,
+            speed,
+            speed_efficiency,
+            jitter_tolerance,
+        )
+
+        # update pos
+        if adjust_sizes:
+            df = np.linalg.norm(update, axis=-1)
+            swinging = mass * df
+            factor = 0.1 * speed / (1 + np.sqrt(speed * swinging))
+            factor = np.minimum(factor * df, 10.0 * np.ones(df.shape)) / df
+        else:
+            swinging = mass * np.linalg.norm(update, axis=-1)
+            factor = speed / (1 + np.sqrt(speed * swinging))
+
+        factored_update = update * factor[:, None]
+        pos_arr += factored_update
+        if abs(factored_update).sum() < 1e-10:
+            break
+
+    pos = dict(zip(G, pos_arr))
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
+
+
+def rescale_layout(pos, scale=1):
+    """Returns scaled position array to (-scale, scale) in all axes.
+
+    The function acts on NumPy arrays which hold position information.
+    Each position is one row of the array. The dimension of the space
+    equals the number of columns. Each coordinate in one column.
+
+    To rescale, the mean (center) is subtracted from each axis separately.
+    Then all values are scaled so that the largest magnitude value
+    from all axes equals `scale` (thus, the aspect ratio is preserved).
+    The resulting NumPy Array is returned (order of rows unchanged).
+
+    Parameters
+    ----------
+    pos : numpy array
+        positions to be scaled. Each row is a position.
+
+    scale : number (default: 1)
+        The size of the resulting extent in all directions.
+
+    attribute : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute named `attribute` which can be accessed with
+        `G.nodes[...][attribute]`. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : numpy array
+        scaled positions. Each row is a position.
+
+    See Also
+    --------
+    rescale_layout_dict
+    """
+    import numpy as np
+
+    # Find max length over all dimensions
+    pos -= pos.mean(axis=0)
+    lim = np.abs(pos).max()  # max coordinate for all axes
+    # rescale to (-scale, scale) in all directions, preserves aspect
+    if lim > 0:
+        pos *= scale / lim
+    return pos
+
+
+def rescale_layout_dict(pos, scale=1):
+    """Return a dictionary of scaled positions keyed by node
+
+    Parameters
+    ----------
+    pos : A dictionary of positions keyed by node
+
+    scale : number (default: 1)
+        The size of the resulting extent in all directions.
+
+    Returns
+    -------
+    pos : A dictionary of positions keyed by node
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> pos = {0: np.array((0, 0)), 1: np.array((1, 1)), 2: np.array((0.5, 0.5))}
+    >>> nx.rescale_layout_dict(pos)
+    {0: array([-1., -1.]), 1: array([1., 1.]), 2: array([0., 0.])}
+
+    >>> pos = {0: np.array((0, 0)), 1: np.array((-1, 1)), 2: np.array((-0.5, 0.5))}
+    >>> nx.rescale_layout_dict(pos, scale=2)
+    {0: array([ 2., -2.]), 1: array([-2.,  2.]), 2: array([0., 0.])}
+
+    See Also
+    --------
+    rescale_layout
+    """
+    import numpy as np
+
+    if not pos:  # empty_graph
+        return {}
+    pos_v = np.array(list(pos.values()))
+    pos_v = rescale_layout(pos_v, scale=scale)
+    return dict(zip(pos, pos_v))
+
+
+def bfs_layout(G, start, *, align="vertical", scale=1, center=None, store_pos_as=None):
+    """Position nodes according to breadth-first search algorithm.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A position will be assigned to every node in G.
+
+    start : node in `G`
+        Starting node for bfs
+
+    center : array-like or None
+        Coordinate pair around which to center the layout.
+
+    store_pos_as : str, default None
+        If non-None, the position of each node will be stored on the graph as
+        an attribute with this string as its name, which can be accessed with
+        ``G.nodes[...][store_pos_as]``. The function still returns the dictionary.
+
+    Returns
+    -------
+    pos : dict
+        A dictionary of positions keyed by node.
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> G = nx.path_graph(4)
+    >>> pos = nx.bfs_layout(G, 0)
+    >>> # suppress the returned dict and store on the graph directly
+    >>> _ = nx.bfs_layout(G, 0, store_pos_as="pos")
+    >>> pprint(nx.get_node_attributes(G, "pos"))
+    {0: array([-1.,  0.]),
+     1: array([-0.33333333,  0.        ]),
+     2: array([0.33333333, 0.        ]),
+     3: array([1., 0.])}
+
+
+
+    Notes
+    -----
+    This algorithm currently only works in two dimensions and does not
+    try to minimize edge crossings.
+
+    """
+    G, center = _process_params(G, center, 2)
+
+    # Compute layers with BFS
+    layers = dict(enumerate(nx.bfs_layers(G, start)))
+
+    if len(G) != sum(len(nodes) for nodes in layers.values()):
+        raise nx.NetworkXError(
+            "bfs_layout didn't include all nodes. Perhaps use input graph:\n"
+            "        G.subgraph(nx.node_connected_component(G, start))"
+        )
+
+    # Compute node positions with multipartite_layout
+    pos = multipartite_layout(
+        G, subset_key=layers, align=align, scale=scale, center=center
+    )
+
+    if store_pos_as is not None:
+        nx.set_node_attributes(G, pos, store_pos_as)
+
+    return pos
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/nx_agraph.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/nx_agraph.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f1a7beb4c6a948a30b9e91594a4819260903f99
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/nx_agraph.py
@@ -0,0 +1,464 @@
+"""
+***************
+Graphviz AGraph
+***************
+
+Interface to pygraphviz AGraph class.
+
+Examples
+--------
+>>> G = nx.complete_graph(5)
+>>> A = nx.nx_agraph.to_agraph(G)
+>>> H = nx.nx_agraph.from_agraph(A)
+
+See Also
+--------
+ - Pygraphviz: http://pygraphviz.github.io/
+ - Graphviz:      https://www.graphviz.org
+ - DOT Language:  http://www.graphviz.org/doc/info/lang.html
+"""
+
+import tempfile
+
+import networkx as nx
+
+__all__ = [
+    "from_agraph",
+    "to_agraph",
+    "write_dot",
+    "read_dot",
+    "graphviz_layout",
+    "pygraphviz_layout",
+    "view_pygraphviz",
+]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_agraph(A, create_using=None):
+    """Returns a NetworkX Graph or DiGraph from a PyGraphviz graph.
+
+    Parameters
+    ----------
+    A : PyGraphviz AGraph
+      A graph created with PyGraphviz
+
+    create_using : NetworkX graph constructor, optional (default=None)
+       Graph type to create. If graph instance, then cleared before populated.
+       If `None`, then the appropriate Graph type is inferred from `A`.
+
+    Examples
+    --------
+    >>> K5 = nx.complete_graph(5)
+    >>> A = nx.nx_agraph.to_agraph(K5)
+    >>> G = nx.nx_agraph.from_agraph(A)
+
+    Notes
+    -----
+    The Graph G will have a dictionary G.graph_attr containing
+    the default graphviz attributes for graphs, nodes and edges.
+
+    Default node attributes will be in the dictionary G.node_attr
+    which is keyed by node.
+
+    Edge attributes will be returned as edge data in G.  With
+    edge_attr=False the edge data will be the Graphviz edge weight
+    attribute or the value 1 if no edge weight attribute is found.
+
+    """
+    if create_using is None:
+        if A.is_directed():
+            if A.is_strict():
+                create_using = nx.DiGraph
+            else:
+                create_using = nx.MultiDiGraph
+        else:
+            if A.is_strict():
+                create_using = nx.Graph
+            else:
+                create_using = nx.MultiGraph
+
+    # assign defaults
+    N = nx.empty_graph(0, create_using)
+    if A.name is not None:
+        N.name = A.name
+
+    # add graph attributes
+    N.graph.update(A.graph_attr)
+
+    # add nodes, attributes to N.node_attr
+    for n in A.nodes():
+        str_attr = {str(k): v for k, v in n.attr.items()}
+        N.add_node(str(n), **str_attr)
+
+    # add edges, assign edge data as dictionary of attributes
+    for e in A.edges():
+        u, v = str(e[0]), str(e[1])
+        attr = dict(e.attr)
+        str_attr = {str(k): v for k, v in attr.items()}
+        if not N.is_multigraph():
+            if e.name is not None:
+                str_attr["key"] = e.name
+            N.add_edge(u, v, **str_attr)
+        else:
+            N.add_edge(u, v, key=e.name, **str_attr)
+
+    # add default attributes for graph, nodes, and edges
+    # hang them on N.graph_attr
+    N.graph["graph"] = dict(A.graph_attr)
+    N.graph["node"] = dict(A.node_attr)
+    N.graph["edge"] = dict(A.edge_attr)
+    return N
+
+
+def to_agraph(N):
+    """Returns a pygraphviz graph from a NetworkX graph N.
+
+    Parameters
+    ----------
+    N : NetworkX graph
+      A graph created with NetworkX
+
+    Examples
+    --------
+    >>> K5 = nx.complete_graph(5)
+    >>> A = nx.nx_agraph.to_agraph(K5)
+
+    Notes
+    -----
+    If N has an dict N.graph_attr an attempt will be made first
+    to copy properties attached to the graph (see from_agraph)
+    and then updated with the calling arguments if any.
+
+    """
+    try:
+        import pygraphviz
+    except ImportError as err:
+        raise ImportError("requires pygraphviz http://pygraphviz.github.io/") from err
+    directed = N.is_directed()
+    strict = nx.number_of_selfloops(N) == 0 and not N.is_multigraph()
+
+    A = pygraphviz.AGraph(name=N.name, strict=strict, directed=directed)
+
+    # default graph attributes
+    A.graph_attr.update(N.graph.get("graph", {}))
+    A.node_attr.update(N.graph.get("node", {}))
+    A.edge_attr.update(N.graph.get("edge", {}))
+
+    A.graph_attr.update(
+        (k, v) for k, v in N.graph.items() if k not in ("graph", "node", "edge")
+    )
+
+    # add nodes
+    for n, nodedata in N.nodes(data=True):
+        A.add_node(n)
+        # Add node data
+        a = A.get_node(n)
+        for key, val in nodedata.items():
+            if key == "pos":
+                a.attr["pos"] = f"{val[0]},{val[1]}!"
+            else:
+                a.attr[key] = str(val)
+
+    # loop over edges
+    if N.is_multigraph():
+        for u, v, key, edgedata in N.edges(data=True, keys=True):
+            str_edgedata = {k: str(v) for k, v in edgedata.items() if k != "key"}
+            A.add_edge(u, v, key=str(key))
+            # Add edge data
+            a = A.get_edge(u, v)
+            a.attr.update(str_edgedata)
+
+    else:
+        for u, v, edgedata in N.edges(data=True):
+            str_edgedata = {k: str(v) for k, v in edgedata.items()}
+            A.add_edge(u, v)
+            # Add edge data
+            a = A.get_edge(u, v)
+            a.attr.update(str_edgedata)
+
+    return A
+
+
+def write_dot(G, path):
+    """Write NetworkX graph G to Graphviz dot format on path.
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    path : filename
+       Filename or file handle to write
+
+    Notes
+    -----
+    To use a specific graph layout, call ``A.layout`` prior to `write_dot`.
+    Note that some graphviz layouts are not guaranteed to be deterministic,
+    see https://gitlab.com/graphviz/graphviz/-/issues/1767 for more info.
+    """
+    A = to_agraph(G)
+    A.write(path)
+    A.clear()
+    return
+
+
+@nx._dispatchable(name="agraph_read_dot", graphs=None, returns_graph=True)
+def read_dot(path):
+    """Returns a NetworkX graph from a dot file on path.
+
+    Parameters
+    ----------
+    path : file or string
+       File name or file handle to read.
+    """
+    try:
+        import pygraphviz
+    except ImportError as err:
+        raise ImportError(
+            "read_dot() requires pygraphviz http://pygraphviz.github.io/"
+        ) from err
+    A = pygraphviz.AGraph(file=path)
+    gr = from_agraph(A)
+    A.clear()
+    return gr
+
+
+def graphviz_layout(G, prog="neato", root=None, args=""):
+    """Create node positions for G using Graphviz.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+      A graph created with NetworkX
+    prog : string
+      Name of Graphviz layout program
+    root : string, optional
+      Root node for twopi layout
+    args : string, optional
+      Extra arguments to Graphviz layout program
+
+    Returns
+    -------
+    Dictionary of x, y, positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.petersen_graph()
+    >>> pos = nx.nx_agraph.graphviz_layout(G)
+    >>> pos = nx.nx_agraph.graphviz_layout(G, prog="dot")
+
+    Notes
+    -----
+    This is a wrapper for pygraphviz_layout.
+
+    Note that some graphviz layouts are not guaranteed to be deterministic,
+    see https://gitlab.com/graphviz/graphviz/-/issues/1767 for more info.
+    """
+    return pygraphviz_layout(G, prog=prog, root=root, args=args)
+
+
+def pygraphviz_layout(G, prog="neato", root=None, args=""):
+    """Create node positions for G using Graphviz.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+      A graph created with NetworkX
+    prog : string
+      Name of Graphviz layout program
+    root : string, optional
+      Root node for twopi layout
+    args : string, optional
+      Extra arguments to Graphviz layout program
+
+    Returns
+    -------
+    node_pos : dict
+      Dictionary of x, y, positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.petersen_graph()
+    >>> pos = nx.nx_agraph.graphviz_layout(G)
+    >>> pos = nx.nx_agraph.graphviz_layout(G, prog="dot")
+
+    Notes
+    -----
+    If you use complex node objects, they may have the same string
+    representation and GraphViz could treat them as the same node.
+    The layout may assign both nodes a single location. See Issue #1568
+    If this occurs in your case, consider relabeling the nodes just
+    for the layout computation using something similar to::
+
+        >>> H = nx.convert_node_labels_to_integers(G, label_attribute="node_label")
+        >>> H_layout = nx.nx_agraph.pygraphviz_layout(H, prog="dot")
+        >>> G_layout = {H.nodes[n]["node_label"]: p for n, p in H_layout.items()}
+
+    Note that some graphviz layouts are not guaranteed to be deterministic,
+    see https://gitlab.com/graphviz/graphviz/-/issues/1767 for more info.
+    """
+    try:
+        import pygraphviz
+    except ImportError as err:
+        raise ImportError("requires pygraphviz http://pygraphviz.github.io/") from err
+    if root is not None:
+        args += f"-Groot={root}"
+    A = to_agraph(G)
+    A.layout(prog=prog, args=args)
+    node_pos = {}
+    for n in G:
+        node = pygraphviz.Node(A, n)
+        try:
+            xs = node.attr["pos"].split(",")
+            node_pos[n] = tuple(float(x) for x in xs)
+        except:
+            print("no position for node", n)
+            node_pos[n] = (0.0, 0.0)
+    return node_pos
+
+
+@nx.utils.open_file(5, "w+b")
+def view_pygraphviz(
+    G, edgelabel=None, prog="dot", args="", suffix="", path=None, show=True
+):
+    """Views the graph G using the specified layout algorithm.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The machine to draw.
+    edgelabel : str, callable, None
+        If a string, then it specifies the edge attribute to be displayed
+        on the edge labels. If a callable, then it is called for each
+        edge and it should return the string to be displayed on the edges.
+        The function signature of `edgelabel` should be edgelabel(data),
+        where `data` is the edge attribute dictionary.
+    prog : string
+        Name of Graphviz layout program.
+    args : str
+        Additional arguments to pass to the Graphviz layout program.
+    suffix : str
+        If `filename` is None, we save to a temporary file.  The value of
+        `suffix` will appear at the tail end of the temporary filename.
+    path : str, None
+        The filename used to save the image.  If None, save to a temporary
+        file.  File formats are the same as those from pygraphviz.agraph.draw.
+        Filenames ending in .gz or .bz2 will be compressed.
+    show : bool, default = True
+        Whether to display the graph with :mod:`PIL.Image.show`,
+        default is `True`. If `False`, the rendered graph is still available
+        at `path`.
+
+    Returns
+    -------
+    path : str
+        The filename of the generated image.
+    A : PyGraphviz graph
+        The PyGraphviz graph instance used to generate the image.
+
+    Notes
+    -----
+    If this function is called in succession too quickly, sometimes the
+    image is not displayed. So you might consider time.sleep(.5) between
+    calls if you experience problems.
+
+    Note that some graphviz layouts are not guaranteed to be deterministic,
+    see https://gitlab.com/graphviz/graphviz/-/issues/1767 for more info.
+
+    """
+    if not len(G):
+        raise nx.NetworkXException("An empty graph cannot be drawn.")
+
+    # If we are providing default values for graphviz, these must be set
+    # before any nodes or edges are added to the PyGraphviz graph object.
+    # The reason for this is that default values only affect incoming objects.
+    # If you change the default values after the objects have been added,
+    # then they inherit no value and are set only if explicitly set.
+
+    # to_agraph() uses these values.
+    attrs = ["edge", "node", "graph"]
+    for attr in attrs:
+        if attr not in G.graph:
+            G.graph[attr] = {}
+
+    # These are the default values.
+    edge_attrs = {"fontsize": "10"}
+    node_attrs = {
+        "style": "filled",
+        "fillcolor": "#0000FF40",
+        "height": "0.75",
+        "width": "0.75",
+        "shape": "circle",
+    }
+    graph_attrs = {}
+
+    def update_attrs(which, attrs):
+        # Update graph attributes. Return list of those which were added.
+        added = []
+        for k, v in attrs.items():
+            if k not in G.graph[which]:
+                G.graph[which][k] = v
+                added.append(k)
+
+    def clean_attrs(which, added):
+        # Remove added attributes
+        for attr in added:
+            del G.graph[which][attr]
+        if not G.graph[which]:
+            del G.graph[which]
+
+    # Update all default values
+    update_attrs("edge", edge_attrs)
+    update_attrs("node", node_attrs)
+    update_attrs("graph", graph_attrs)
+
+    # Convert to agraph, so we inherit default values
+    A = to_agraph(G)
+
+    # Remove the default values we added to the original graph.
+    clean_attrs("edge", edge_attrs)
+    clean_attrs("node", node_attrs)
+    clean_attrs("graph", graph_attrs)
+
+    # If the user passed in an edgelabel, we update the labels for all edges.
+    if edgelabel is not None:
+        if not callable(edgelabel):
+
+            def func(data):
+                return "".join(["  ", str(data[edgelabel]), "  "])
+
+        else:
+            func = edgelabel
+
+        # update all the edge labels
+        if G.is_multigraph():
+            for u, v, key, data in G.edges(keys=True, data=True):
+                # PyGraphviz doesn't convert the key to a string. See #339
+                edge = A.get_edge(u, v, str(key))
+                edge.attr["label"] = str(func(data))
+        else:
+            for u, v, data in G.edges(data=True):
+                edge = A.get_edge(u, v)
+                edge.attr["label"] = str(func(data))
+
+    if path is None:
+        ext = "png"
+        if suffix:
+            suffix = f"_{suffix}.{ext}"
+        else:
+            suffix = f".{ext}"
+        path = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
+    else:
+        # Assume the decorator worked and it is a file-object.
+        pass
+
+    # Write graph to file
+    A.draw(path=path, format=None, prog=prog, args=args)
+    path.close()
+
+    # Show graph in a new window (depends on platform configuration)
+    if show:
+        from PIL import Image
+
+        Image.open(path.name).show()
+
+    return path.name, A
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/nx_latex.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/nx_latex.py
new file mode 100644
index 0000000000000000000000000000000000000000..677def8e75144afe5d73fbdea9f5643222a4ea01
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/nx_latex.py
@@ -0,0 +1,570 @@
+r"""
+*****
+LaTeX
+*****
+
+Export NetworkX graphs in LaTeX format using the TikZ library within TeX/LaTeX.
+Usually, you will want the drawing to appear in a figure environment so
+you use ``to_latex(G, caption="A caption")``. If you want the raw
+drawing commands without a figure environment use :func:`to_latex_raw`.
+And if you want to write to a file instead of just returning the latex
+code as a string, use ``write_latex(G, "filename.tex", caption="A caption")``.
+
+To construct a figure with subfigures for each graph to be shown, provide
+``to_latex`` or ``write_latex`` a list of graphs, a list of subcaptions,
+and a number of rows of subfigures inside the figure.
+
+To be able to refer to the figures or subfigures in latex using ``\\ref``,
+the keyword ``latex_label`` is available for figures and `sub_labels` for
+a list of labels, one for each subfigure.
+
+We intend to eventually provide an interface to the TikZ Graph
+features which include e.g. layout algorithms.
+
+Let us know via github what you'd like to see available, or better yet
+give us some code to do it, or even better make a github pull request
+to add the feature.
+
+The TikZ approach
+=================
+Drawing options can be stored on the graph as node/edge attributes, or
+can be provided as dicts keyed by node/edge to a string of the options
+for that node/edge. Similarly a label can be shown for each node/edge
+by specifying the labels as graph node/edge attributes or by providing
+a dict keyed by node/edge to the text to be written for that node/edge.
+
+Options for the tikzpicture environment (e.g. "[scale=2]") can be provided
+via a keyword argument. Similarly default node and edge options can be
+provided through keywords arguments. The default node options are applied
+to the single TikZ "path" that draws all nodes (and no edges). The default edge
+options are applied to a TikZ "scope" which contains a path for each edge.
+
+Examples
+========
+>>> G = nx.path_graph(3)
+>>> nx.write_latex(G, "just_my_figure.tex", as_document=True)
+>>> nx.write_latex(G, "my_figure.tex", caption="A path graph", latex_label="fig1")
+>>> latex_code = nx.to_latex(G)  # a string rather than a file
+
+You can change many features of the nodes and edges.
+
+>>> G = nx.path_graph(4, create_using=nx.DiGraph)
+>>> pos = {n: (n, n) for n in G}  # nodes set on a line
+
+>>> G.nodes[0]["style"] = "blue"
+>>> G.nodes[2]["style"] = "line width=3,draw"
+>>> G.nodes[3]["label"] = "Stop"
+>>> G.edges[(0, 1)]["label"] = "1st Step"
+>>> G.edges[(0, 1)]["label_opts"] = "near start"
+>>> G.edges[(1, 2)]["style"] = "line width=3"
+>>> G.edges[(1, 2)]["label"] = "2nd Step"
+>>> G.edges[(2, 3)]["style"] = "green"
+>>> G.edges[(2, 3)]["label"] = "3rd Step"
+>>> G.edges[(2, 3)]["label_opts"] = "near end"
+
+>>> nx.write_latex(G, "latex_graph.tex", pos=pos, as_document=True)
+
+Then compile the LaTeX using something like ``pdflatex latex_graph.tex``
+and view the pdf file created: ``latex_graph.pdf``.
+
+If you want **subfigures** each containing one graph, you can input a list of graphs.
+
+>>> H1 = nx.path_graph(4)
+>>> H2 = nx.complete_graph(4)
+>>> H3 = nx.path_graph(8)
+>>> H4 = nx.complete_graph(8)
+>>> graphs = [H1, H2, H3, H4]
+>>> caps = ["Path 4", "Complete graph 4", "Path 8", "Complete graph 8"]
+>>> lbls = ["fig2a", "fig2b", "fig2c", "fig2d"]
+>>> nx.write_latex(graphs, "subfigs.tex", n_rows=2, sub_captions=caps, sub_labels=lbls)
+>>> latex_code = nx.to_latex(graphs, n_rows=2, sub_captions=caps, sub_labels=lbls)
+
+>>> node_color = {0: "red", 1: "orange", 2: "blue", 3: "gray!90"}
+>>> edge_width = {e: "line width=1.5" for e in H3.edges}
+>>> pos = nx.circular_layout(H3)
+>>> latex_code = nx.to_latex(H3, pos, node_options=node_color, edge_options=edge_width)
+>>> print(latex_code)
+\documentclass{report}
+\usepackage{tikz}
+\usepackage{subcaption}
+<BLANKLINE>
+\begin{document}
+\begin{figure}
+  \begin{tikzpicture}
+      \draw
+        (1.0, 0.0) node[red] (0){0}
+        (0.707, 0.707) node[orange] (1){1}
+        (-0.0, 1.0) node[blue] (2){2}
+        (-0.707, 0.707) node[gray!90] (3){3}
+        (-1.0, -0.0) node (4){4}
+        (-0.707, -0.707) node (5){5}
+        (0.0, -1.0) node (6){6}
+        (0.707, -0.707) node (7){7};
+      \begin{scope}[-]
+        \draw[line width=1.5] (0) to (1);
+        \draw[line width=1.5] (1) to (2);
+        \draw[line width=1.5] (2) to (3);
+        \draw[line width=1.5] (3) to (4);
+        \draw[line width=1.5] (4) to (5);
+        \draw[line width=1.5] (5) to (6);
+        \draw[line width=1.5] (6) to (7);
+      \end{scope}
+    \end{tikzpicture}
+\end{figure}
+\end{document}
+
+Notes
+-----
+If you want to change the preamble/postamble of the figure/document/subfigure
+environment, use the keyword arguments: `figure_wrapper`, `document_wrapper`,
+`subfigure_wrapper`. The default values are stored in private variables
+e.g. ``nx.nx_layout._DOCUMENT_WRAPPER``
+
+References
+----------
+TikZ:          https://tikz.dev/
+
+TikZ options details:   https://tikz.dev/tikz-actions
+"""
+
+import networkx as nx
+
+__all__ = [
+    "to_latex_raw",
+    "to_latex",
+    "write_latex",
+]
+
+
+@nx.utils.not_implemented_for("multigraph")
+def to_latex_raw(
+    G,
+    pos="pos",
+    tikz_options="",
+    default_node_options="",
+    node_options="node_options",
+    node_label="label",
+    default_edge_options="",
+    edge_options="edge_options",
+    edge_label="label",
+    edge_label_options="edge_label_options",
+):
+    """Return a string of the LaTeX/TikZ code to draw `G`
+
+    This function produces just the code for the tikzpicture
+    without any enclosing environment.
+
+    Parameters
+    ==========
+    G : NetworkX graph
+        The NetworkX graph to be drawn
+    pos : string or dict (default "pos")
+        The name of the node attribute on `G` that holds the position of each node.
+        Positions can be sequences of length 2 with numbers for (x,y) coordinates.
+        They can also be strings to denote positions in TikZ style, such as (x, y)
+        or (angle:radius).
+        If a dict, it should be keyed by node to a position.
+        If an empty dict, a circular layout is computed by TikZ.
+    tikz_options : string
+        The tikzpicture options description defining the options for the picture.
+        Often large scale options like `[scale=2]`.
+    default_node_options : string
+        The draw options for a path of nodes. Individual node options override these.
+    node_options : string or dict
+        The name of the node attribute on `G` that holds the options for each node.
+        Or a dict keyed by node to a string holding the options for that node.
+    node_label : string or dict
+        The name of the node attribute on `G` that holds the node label (text)
+        displayed for each node. If the attribute is "" or not present, the node
+        itself is drawn as a string. LaTeX processing such as ``"$A_1$"`` is allowed.
+        Or a dict keyed by node to a string holding the label for that node.
+    default_edge_options : string
+        The options for the scope drawing all edges. The default is "[-]" for
+        undirected graphs and "[->]" for directed graphs.
+    edge_options : string or dict
+        The name of the edge attribute on `G` that holds the options for each edge.
+        If the edge is a self-loop and ``"loop" not in edge_options`` the option
+        "loop," is added to the options for the self-loop edge. Hence you can
+        use "[loop above]" explicitly, but the default is "[loop]".
+        Or a dict keyed by edge to a string holding the options for that edge.
+    edge_label : string or dict
+        The name of the edge attribute on `G` that holds the edge label (text)
+        displayed for each edge. If the attribute is "" or not present, no edge
+        label is drawn.
+        Or a dict keyed by edge to a string holding the label for that edge.
+    edge_label_options : string or dict
+        The name of the edge attribute on `G` that holds the label options for
+        each edge. For example, "[sloped,above,blue]". The default is no options.
+        Or a dict keyed by edge to a string holding the label options for that edge.
+
+    Returns
+    =======
+    latex_code : string
+       The text string which draws the desired graph(s) when compiled by LaTeX.
+
+    See Also
+    ========
+    to_latex
+    write_latex
+    """
+    i4 = "\n    "
+    i8 = "\n        "
+
+    # set up position dict
+    # TODO allow pos to be None and use a nice TikZ default
+    if not isinstance(pos, dict):
+        pos = nx.get_node_attributes(G, pos)
+    if not pos:
+        # circular layout with radius 2
+        pos = {n: f"({round(360.0 * i / len(G), 3)}:2)" for i, n in enumerate(G)}
+    for node in G:
+        if node not in pos:
+            raise nx.NetworkXError(f"node {node} has no specified pos {pos}")
+        posnode = pos[node]
+        if not isinstance(posnode, str):
+            try:
+                posx, posy = posnode
+                pos[node] = f"({round(posx, 3)}, {round(posy, 3)})"
+            except (TypeError, ValueError):
+                msg = f"position pos[{node}] is not 2-tuple or a string: {posnode}"
+                raise nx.NetworkXError(msg)
+
+    # set up all the dicts
+    if not isinstance(node_options, dict):
+        node_options = nx.get_node_attributes(G, node_options)
+    if not isinstance(node_label, dict):
+        node_label = nx.get_node_attributes(G, node_label)
+    if not isinstance(edge_options, dict):
+        edge_options = nx.get_edge_attributes(G, edge_options)
+    if not isinstance(edge_label, dict):
+        edge_label = nx.get_edge_attributes(G, edge_label)
+    if not isinstance(edge_label_options, dict):
+        edge_label_options = nx.get_edge_attributes(G, edge_label_options)
+
+    # process default options (add brackets or not)
+    topts = "" if tikz_options == "" else f"[{tikz_options.strip('[]')}]"
+    defn = "" if default_node_options == "" else f"[{default_node_options.strip('[]')}]"
+    linestyle = f"{'->' if G.is_directed() else '-'}"
+    if default_edge_options == "":
+        defe = "[" + linestyle + "]"
+    elif "-" in default_edge_options:
+        defe = default_edge_options
+    else:
+        defe = f"[{linestyle},{default_edge_options.strip('[]')}]"
+
+    # Construct the string line by line
+    result = "  \\begin{tikzpicture}" + topts
+    result += i4 + "  \\draw" + defn
+    # load the nodes
+    for n in G:
+        # node options goes inside square brackets
+        nopts = f"[{node_options[n].strip('[]')}]" if n in node_options else ""
+        # node text goes inside curly brackets {}
+        ntext = f"{{{node_label[n]}}}" if n in node_label else f"{{{n}}}"
+
+        result += i8 + f"{pos[n]} node{nopts} ({n}){ntext}"
+    result += ";\n"
+
+    # load the edges
+    result += "      \\begin{scope}" + defe
+    for edge in G.edges:
+        u, v = edge[:2]
+        e_opts = f"{edge_options[edge]}".strip("[]") if edge in edge_options else ""
+        # add loop options for selfloops if not present
+        if u == v and "loop" not in e_opts:
+            e_opts = "loop," + e_opts
+        e_opts = f"[{e_opts}]" if e_opts != "" else ""
+        # TODO -- handle bending of multiedges
+
+        els = edge_label_options[edge] if edge in edge_label_options else ""
+        # edge label options goes inside square brackets []
+        els = f"[{els.strip('[]')}]"
+        # edge text is drawn using the TikZ node command inside curly brackets {}
+        e_label = f" node{els} {{{edge_label[edge]}}}" if edge in edge_label else ""
+
+        result += i8 + f"\\draw{e_opts} ({u}) to{e_label} ({v});"
+
+    result += "\n      \\end{scope}\n    \\end{tikzpicture}\n"
+    return result
+
+
+_DOC_WRAPPER_TIKZ = r"""\documentclass{{report}}
+\usepackage{{tikz}}
+\usepackage{{subcaption}}
+
+\begin{{document}}
+{content}
+\end{{document}}"""
+
+
+_FIG_WRAPPER = r"""\begin{{figure}}
+{content}{caption}{label}
+\end{{figure}}"""
+
+
+_SUBFIG_WRAPPER = r"""  \begin{{subfigure}}{{{size}\textwidth}}
+{content}{caption}{label}
+  \end{{subfigure}}"""
+
+
+def to_latex(
+    Gbunch,
+    pos="pos",
+    tikz_options="",
+    default_node_options="",
+    node_options="node_options",
+    node_label="node_label",
+    default_edge_options="",
+    edge_options="edge_options",
+    edge_label="edge_label",
+    edge_label_options="edge_label_options",
+    caption="",
+    latex_label="",
+    sub_captions=None,
+    sub_labels=None,
+    n_rows=1,
+    as_document=True,
+    document_wrapper=_DOC_WRAPPER_TIKZ,
+    figure_wrapper=_FIG_WRAPPER,
+    subfigure_wrapper=_SUBFIG_WRAPPER,
+):
+    """Return latex code to draw the graph(s) in `Gbunch`
+
+    The TikZ drawing utility in LaTeX is used to draw the graph(s).
+    If `Gbunch` is a graph, it is drawn in a figure environment.
+    If `Gbunch` is an iterable of graphs, each is drawn in a subfigure environment
+    within a single figure environment.
+
+    If `as_document` is True, the figure is wrapped inside a document environment
+    so that the resulting string is ready to be compiled by LaTeX. Otherwise,
+    the string is ready for inclusion in a larger tex document using ``\\include``
+    or ``\\input`` statements.
+
+    Parameters
+    ==========
+    Gbunch : NetworkX graph or iterable of NetworkX graphs
+        The NetworkX graph to be drawn or an iterable of graphs
+        to be drawn inside subfigures of a single figure.
+    pos : string or list of strings
+        The name of the node attribute on `G` that holds the position of each node.
+        Positions can be sequences of length 2 with numbers for (x,y) coordinates.
+        They can also be strings to denote positions in TikZ style, such as (x, y)
+        or (angle:radius).
+        If a dict, it should be keyed by node to a position.
+        If an empty dict, a circular layout is computed by TikZ.
+        If you are drawing many graphs in subfigures, use a list of position dicts.
+    tikz_options : string
+        The tikzpicture options description defining the options for the picture.
+        Often large scale options like `[scale=2]`.
+    default_node_options : string
+        The draw options for a path of nodes. Individual node options override these.
+    node_options : string or dict
+        The name of the node attribute on `G` that holds the options for each node.
+        Or a dict keyed by node to a string holding the options for that node.
+    node_label : string or dict
+        The name of the node attribute on `G` that holds the node label (text)
+        displayed for each node. If the attribute is "" or not present, the node
+        itself is drawn as a string. LaTeX processing such as ``"$A_1$"`` is allowed.
+        Or a dict keyed by node to a string holding the label for that node.
+    default_edge_options : string
+        The options for the scope drawing all edges. The default is "[-]" for
+        undirected graphs and "[->]" for directed graphs.
+    edge_options : string or dict
+        The name of the edge attribute on `G` that holds the options for each edge.
+        If the edge is a self-loop and ``"loop" not in edge_options`` the option
+        "loop," is added to the options for the self-loop edge. Hence you can
+        use "[loop above]" explicitly, but the default is "[loop]".
+        Or a dict keyed by edge to a string holding the options for that edge.
+    edge_label : string or dict
+        The name of the edge attribute on `G` that holds the edge label (text)
+        displayed for each edge. If the attribute is "" or not present, no edge
+        label is drawn.
+        Or a dict keyed by edge to a string holding the label for that edge.
+    edge_label_options : string or dict
+        The name of the edge attribute on `G` that holds the label options for
+        each edge. For example, "[sloped,above,blue]". The default is no options.
+        Or a dict keyed by edge to a string holding the label options for that edge.
+    caption : string
+        The caption string for the figure environment
+    latex_label : string
+        The latex label used for the figure for easy referral from the main text
+    sub_captions : list of strings
+        The sub_caption string for each subfigure in the figure
+    sub_latex_labels : list of strings
+        The latex label for each subfigure in the figure
+    n_rows : int
+        The number of rows of subfigures to arrange for multiple graphs
+    as_document : bool
+        Whether to wrap the latex code in a document environment for compiling
+    document_wrapper : formatted text string with variable ``content``.
+        This text is called to evaluate the content embedded in a document
+        environment with a preamble setting up TikZ.
+    figure_wrapper : formatted text string
+        This text is evaluated with variables ``content``, ``caption`` and ``label``.
+        It wraps the content and if a caption is provided, adds the latex code for
+        that caption, and if a label is provided, adds the latex code for a label.
+    subfigure_wrapper : formatted text string
+        This text evaluate variables ``size``, ``content``, ``caption`` and ``label``.
+        It wraps the content and if a caption is provided, adds the latex code for
+        that caption, and if a label is provided, adds the latex code for a label.
+        The size is the vertical size of each row of subfigures as a fraction.
+
+    Returns
+    =======
+    latex_code : string
+        The text string which draws the desired graph(s) when compiled by LaTeX.
+
+    See Also
+    ========
+    write_latex
+    to_latex_raw
+    """
+    if hasattr(Gbunch, "adj"):
+        raw = to_latex_raw(
+            Gbunch,
+            pos,
+            tikz_options,
+            default_node_options,
+            node_options,
+            node_label,
+            default_edge_options,
+            edge_options,
+            edge_label,
+            edge_label_options,
+        )
+    else:  # iterator of graphs
+        sbf = subfigure_wrapper
+        size = 1 / n_rows
+
+        N = len(Gbunch)
+        if isinstance(pos, str | dict):
+            pos = [pos] * N
+        if sub_captions is None:
+            sub_captions = [""] * N
+        if sub_labels is None:
+            sub_labels = [""] * N
+        if not (len(Gbunch) == len(pos) == len(sub_captions) == len(sub_labels)):
+            raise nx.NetworkXError(
+                "length of Gbunch, sub_captions and sub_figures must agree"
+            )
+
+        raw = ""
+        for G, pos, subcap, sublbl in zip(Gbunch, pos, sub_captions, sub_labels):
+            subraw = to_latex_raw(
+                G,
+                pos,
+                tikz_options,
+                default_node_options,
+                node_options,
+                node_label,
+                default_edge_options,
+                edge_options,
+                edge_label,
+                edge_label_options,
+            )
+            cap = f"    \\caption{{{subcap}}}" if subcap else ""
+            lbl = f"\\label{{{sublbl}}}" if sublbl else ""
+            raw += sbf.format(size=size, content=subraw, caption=cap, label=lbl)
+            raw += "\n"
+
+    # put raw latex code into a figure environment and optionally into a document
+    raw = raw[:-1]
+    cap = f"\n  \\caption{{{caption}}}" if caption else ""
+    lbl = f"\\label{{{latex_label}}}" if latex_label else ""
+    fig = figure_wrapper.format(content=raw, caption=cap, label=lbl)
+    if as_document:
+        return document_wrapper.format(content=fig)
+    return fig
+
+
+@nx.utils.open_file(1, mode="w")
+def write_latex(Gbunch, path, **options):
+    """Write the latex code to draw the graph(s) onto `path`.
+
+    This convenience function creates the latex drawing code as a string
+    and writes that to a file ready to be compiled when `as_document` is True
+    or ready to be ``import`` ed or ``include`` ed into your main LaTeX document.
+
+    The `path` argument can be a string filename or a file handle to write to.
+
+    Parameters
+    ----------
+    Gbunch : NetworkX graph or iterable of NetworkX graphs
+        If Gbunch is a graph, it is drawn in a figure environment.
+        If Gbunch is an iterable of graphs, each is drawn in a subfigure
+        environment within a single figure environment.
+    path : string or file
+        Filename or file handle to write to.
+        Filenames ending in .gz or .bz2 will be compressed.
+    options : dict
+        By default, TikZ is used with options: (others are ignored)::
+
+            pos : string or dict or list
+                The name of the node attribute on `G` that holds the position of each node.
+                Positions can be sequences of length 2 with numbers for (x,y) coordinates.
+                They can also be strings to denote positions in TikZ style, such as (x, y)
+                or (angle:radius).
+                If a dict, it should be keyed by node to a position.
+                If an empty dict, a circular layout is computed by TikZ.
+                If you are drawing many graphs in subfigures, use a list of position dicts.
+            tikz_options : string
+                The tikzpicture options description defining the options for the picture.
+                Often large scale options like `[scale=2]`.
+            default_node_options : string
+                The draw options for a path of nodes. Individual node options override these.
+            node_options : string or dict
+                The name of the node attribute on `G` that holds the options for each node.
+                Or a dict keyed by node to a string holding the options for that node.
+            node_label : string or dict
+                The name of the node attribute on `G` that holds the node label (text)
+                displayed for each node. If the attribute is "" or not present, the node
+                itself is drawn as a string. LaTeX processing such as ``"$A_1$"`` is allowed.
+                Or a dict keyed by node to a string holding the label for that node.
+            default_edge_options : string
+                The options for the scope drawing all edges. The default is "[-]" for
+                undirected graphs and "[->]" for directed graphs.
+            edge_options : string or dict
+                The name of the edge attribute on `G` that holds the options for each edge.
+                If the edge is a self-loop and ``"loop" not in edge_options`` the option
+                "loop," is added to the options for the self-loop edge. Hence you can
+                use "[loop above]" explicitly, but the default is "[loop]".
+                Or a dict keyed by edge to a string holding the options for that edge.
+            edge_label : string or dict
+                The name of the edge attribute on `G` that holds the edge label (text)
+                displayed for each edge. If the attribute is "" or not present, no edge
+                label is drawn.
+                Or a dict keyed by edge to a string holding the label for that edge.
+            edge_label_options : string or dict
+                The name of the edge attribute on `G` that holds the label options for
+                each edge. For example, "[sloped,above,blue]". The default is no options.
+                Or a dict keyed by edge to a string holding the label options for that edge.
+            caption : string
+                The caption string for the figure environment
+            latex_label : string
+                The latex label used for the figure for easy referral from the main text
+            sub_captions : list of strings
+                The sub_caption string for each subfigure in the figure
+            sub_latex_labels : list of strings
+                The latex label for each subfigure in the figure
+            n_rows : int
+                The number of rows of subfigures to arrange for multiple graphs
+            as_document : bool
+                Whether to wrap the latex code in a document environment for compiling
+            document_wrapper : formatted text string with variable ``content``.
+                This text is called to evaluate the content embedded in a document
+                environment with a preamble setting up the TikZ syntax.
+            figure_wrapper : formatted text string
+                This text is evaluated with variables ``content``, ``caption`` and ``label``.
+                It wraps the content and if a caption is provided, adds the latex code for
+                that caption, and if a label is provided, adds the latex code for a label.
+            subfigure_wrapper : formatted text string
+                This text evaluate variables ``size``, ``content``, ``caption`` and ``label``.
+                It wraps the content and if a caption is provided, adds the latex code for
+                that caption, and if a label is provided, adds the latex code for a label.
+                The size is the vertical size of each row of subfigures as a fraction.
+
+    See Also
+    ========
+    to_latex
+    """
+    path.write(to_latex(Gbunch, **options))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/nx_pydot.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/nx_pydot.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fe5ceec97c5c3c85e5e64e05b4c02eb83978c3a
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/nx_pydot.py
@@ -0,0 +1,361 @@
+"""
+*****
+Pydot
+*****
+
+Import and export NetworkX graphs in Graphviz dot format using pydot.
+
+Either this module or nx_agraph can be used to interface with graphviz.
+
+Examples
+--------
+>>> G = nx.complete_graph(5)
+>>> PG = nx.nx_pydot.to_pydot(G)
+>>> H = nx.nx_pydot.from_pydot(PG)
+
+See Also
+--------
+ - pydot:         https://github.com/erocarrera/pydot
+ - Graphviz:      https://www.graphviz.org
+ - DOT Language:  http://www.graphviz.org/doc/info/lang.html
+"""
+
+from locale import getpreferredencoding
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = [
+    "write_dot",
+    "read_dot",
+    "graphviz_layout",
+    "pydot_layout",
+    "to_pydot",
+    "from_pydot",
+]
+
+
+@open_file(1, mode="w")
+def write_dot(G, path):
+    """Write NetworkX graph G to Graphviz dot format on path.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    path : string or file
+       Filename or file handle for data output.
+       Filenames ending in .gz or .bz2 will be compressed.
+    """
+    P = to_pydot(G)
+    path.write(P.to_string())
+    return
+
+
+@open_file(0, mode="r")
+@nx._dispatchable(name="pydot_read_dot", graphs=None, returns_graph=True)
+def read_dot(path):
+    """Returns a NetworkX :class:`MultiGraph` or :class:`MultiDiGraph` from the
+    dot file with the passed path.
+
+    If this file contains multiple graphs, only the first such graph is
+    returned. All graphs _except_ the first are silently ignored.
+
+    Parameters
+    ----------
+    path : str or file
+        Filename or file handle to read.
+        Filenames ending in .gz or .bz2 will be decompressed.
+
+    Returns
+    -------
+    G : MultiGraph or MultiDiGraph
+        A :class:`MultiGraph` or :class:`MultiDiGraph`.
+
+    Notes
+    -----
+    Use `G = nx.Graph(nx.nx_pydot.read_dot(path))` to return a :class:`Graph` instead of a
+    :class:`MultiGraph`.
+    """
+    import pydot
+
+    data = path.read()
+
+    # List of one or more "pydot.Dot" instances deserialized from this file.
+    P_list = pydot.graph_from_dot_data(data)
+
+    # Convert only the first such instance into a NetworkX graph.
+    return from_pydot(P_list[0])
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_pydot(P):
+    """Returns a NetworkX graph from a Pydot graph.
+
+    Parameters
+    ----------
+    P : Pydot graph
+      A graph created with Pydot
+
+    Returns
+    -------
+    G : NetworkX multigraph
+        A MultiGraph or MultiDiGraph.
+
+    Examples
+    --------
+    >>> K5 = nx.complete_graph(5)
+    >>> A = nx.nx_pydot.to_pydot(K5)
+    >>> G = nx.nx_pydot.from_pydot(A)  # return MultiGraph
+
+    # make a Graph instead of MultiGraph
+    >>> G = nx.Graph(nx.nx_pydot.from_pydot(A))
+
+    """
+    # NOTE: Pydot v3 expects a dummy argument whereas Pydot v4 doesn't
+    # Remove the try-except when Pydot v4 becomes the minimum supported version
+    try:
+        strict = P.get_strict()
+    except TypeError:
+        strict = P.get_strict(None)  # pydot bug: get_strict() shouldn't take argument
+    multiedges = not strict
+
+    if P.get_type() == "graph":  # undirected
+        if multiedges:
+            N = nx.MultiGraph()
+        else:
+            N = nx.Graph()
+    else:
+        if multiedges:
+            N = nx.MultiDiGraph()
+        else:
+            N = nx.DiGraph()
+
+    # assign defaults
+    name = P.get_name().strip('"')
+    if name != "":
+        N.name = name
+
+    # add nodes, attributes to N.node_attr
+    for p in P.get_node_list():
+        n = p.get_name().strip('"')
+        if n in ("node", "graph", "edge"):
+            continue
+        N.add_node(n, **p.get_attributes())
+
+    # add edges
+    for e in P.get_edge_list():
+        u = e.get_source()
+        v = e.get_destination()
+        attr = e.get_attributes()
+        s = []
+        d = []
+
+        if isinstance(u, str):
+            s.append(u.strip('"'))
+        else:
+            for unodes in u["nodes"]:
+                s.append(unodes.strip('"'))
+
+        if isinstance(v, str):
+            d.append(v.strip('"'))
+        else:
+            for vnodes in v["nodes"]:
+                d.append(vnodes.strip('"'))
+
+        for source_node in s:
+            for destination_node in d:
+                N.add_edge(source_node, destination_node, **attr)
+
+    # add default attributes for graph, nodes, edges
+    pattr = P.get_attributes()
+    if pattr:
+        N.graph["graph"] = pattr
+    try:
+        N.graph["node"] = P.get_node_defaults()[0]
+    except (IndexError, TypeError):
+        pass  # N.graph['node']={}
+    try:
+        N.graph["edge"] = P.get_edge_defaults()[0]
+    except (IndexError, TypeError):
+        pass  # N.graph['edge']={}
+    return N
+
+
+def to_pydot(N):
+    """Returns a pydot graph from a NetworkX graph N.
+
+    Parameters
+    ----------
+    N : NetworkX graph
+      A graph created with NetworkX
+
+    Examples
+    --------
+    >>> K5 = nx.complete_graph(5)
+    >>> P = nx.nx_pydot.to_pydot(K5)
+
+    Notes
+    -----
+
+    """
+    import pydot
+
+    # set Graphviz graph type
+    if N.is_directed():
+        graph_type = "digraph"
+    else:
+        graph_type = "graph"
+    strict = nx.number_of_selfloops(N) == 0 and not N.is_multigraph()
+
+    name = N.name
+    graph_defaults = N.graph.get("graph", {})
+    if name == "":
+        P = pydot.Dot("", graph_type=graph_type, strict=strict, **graph_defaults)
+    else:
+        P = pydot.Dot(
+            f'"{name}"', graph_type=graph_type, strict=strict, **graph_defaults
+        )
+    try:
+        P.set_node_defaults(**N.graph["node"])
+    except KeyError:
+        pass
+    try:
+        P.set_edge_defaults(**N.graph["edge"])
+    except KeyError:
+        pass
+
+    for n, nodedata in N.nodes(data=True):
+        str_nodedata = {str(k): str(v) for k, v in nodedata.items()}
+        n = str(n)
+        p = pydot.Node(n, **str_nodedata)
+        P.add_node(p)
+
+    if N.is_multigraph():
+        for u, v, key, edgedata in N.edges(data=True, keys=True):
+            str_edgedata = {str(k): str(v) for k, v in edgedata.items() if k != "key"}
+            u, v = str(u), str(v)
+            edge = pydot.Edge(u, v, key=str(key), **str_edgedata)
+            P.add_edge(edge)
+
+    else:
+        for u, v, edgedata in N.edges(data=True):
+            str_edgedata = {str(k): str(v) for k, v in edgedata.items()}
+            u, v = str(u), str(v)
+            edge = pydot.Edge(u, v, **str_edgedata)
+            P.add_edge(edge)
+    return P
+
+
+def graphviz_layout(G, prog="neato", root=None):
+    """Create node positions using Pydot and Graphviz.
+
+    Returns a dictionary of positions keyed by node.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        The graph for which the layout is computed.
+    prog : string (default: 'neato')
+        The name of the GraphViz program to use for layout.
+        Options depend on GraphViz version but may include:
+        'dot', 'twopi', 'fdp', 'sfdp', 'circo'
+    root : Node from G or None (default: None)
+        The node of G from which to start some layout algorithms.
+
+    Returns
+    -------
+      Dictionary of (x, y) positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(4)
+    >>> pos = nx.nx_pydot.graphviz_layout(G)
+    >>> pos = nx.nx_pydot.graphviz_layout(G, prog="dot")
+
+    Notes
+    -----
+    This is a wrapper for pydot_layout.
+    """
+    return pydot_layout(G=G, prog=prog, root=root)
+
+
+def pydot_layout(G, prog="neato", root=None):
+    """Create node positions using :mod:`pydot` and Graphviz.
+
+    Parameters
+    ----------
+    G : Graph
+        NetworkX graph to be laid out.
+    prog : string  (default: 'neato')
+        Name of the GraphViz command to use for layout.
+        Options depend on GraphViz version but may include:
+        'dot', 'twopi', 'fdp', 'sfdp', 'circo'
+    root : Node from G or None (default: None)
+        The node of G from which to start some layout algorithms.
+
+    Returns
+    -------
+    dict
+        Dictionary of positions keyed by node.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(4)
+    >>> pos = nx.nx_pydot.pydot_layout(G)
+    >>> pos = nx.nx_pydot.pydot_layout(G, prog="dot")
+
+    Notes
+    -----
+    If you use complex node objects, they may have the same string
+    representation and GraphViz could treat them as the same node.
+    The layout may assign both nodes a single location. See Issue #1568
+    If this occurs in your case, consider relabeling the nodes just
+    for the layout computation using something similar to::
+
+        H = nx.convert_node_labels_to_integers(G, label_attribute="node_label")
+        H_layout = nx.nx_pydot.pydot_layout(H, prog="dot")
+        G_layout = {H.nodes[n]["node_label"]: p for n, p in H_layout.items()}
+
+    """
+    import pydot
+
+    P = to_pydot(G)
+    if root is not None:
+        P.set("root", str(root))
+
+    # List of low-level bytes comprising a string in the dot language converted
+    # from the passed graph with the passed external GraphViz command.
+    D_bytes = P.create_dot(prog=prog)
+
+    # Unique string decoded from these bytes with the preferred locale encoding
+    D = str(D_bytes, encoding=getpreferredencoding())
+
+    if D == "":  # no data returned
+        print(f"Graphviz layout with {prog} failed")
+        print()
+        print("To debug what happened try:")
+        print("P = nx.nx_pydot.to_pydot(G)")
+        print('P.write_dot("file.dot")')
+        print(f"And then run {prog} on file.dot")
+        return
+
+    # List of one or more "pydot.Dot" instances deserialized from this string.
+    Q_list = pydot.graph_from_dot_data(D)
+    assert len(Q_list) == 1
+
+    # The first and only such instance, as guaranteed by the above assertion.
+    Q = Q_list[0]
+
+    node_pos = {}
+    for n in G.nodes():
+        str_n = str(n)
+        node = Q.get_node(pydot.quote_id_if_necessary(str_n))
+
+        if isinstance(node, list):
+            node = node[0]
+        pos = node.get_pos()[1:-1]  # strip leading and trailing double quotes
+        if pos is not None:
+            xx, yy = pos.split(",")
+            node_pos[n] = (float(xx), float(yy))
+    return node_pos
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/nx_pylab.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/nx_pylab.py
new file mode 100644
index 0000000000000000000000000000000000000000..778d6e8e19a03052592358897005118629f5afab
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/drawing/nx_pylab.py
@@ -0,0 +1,3021 @@
+"""
+**********
+Matplotlib
+**********
+
+Draw networks with matplotlib.
+
+Examples
+--------
+>>> G = nx.complete_graph(5)
+>>> nx.draw(G)
+
+See Also
+--------
+ - :doc:`matplotlib <matplotlib:index>`
+ - :func:`matplotlib.pyplot.scatter`
+ - :obj:`matplotlib.patches.FancyArrowPatch`
+"""
+
+import collections
+import itertools
+from numbers import Number
+
+import networkx as nx
+
+__all__ = [
+    "display",
+    "apply_matplotlib_colors",
+    "draw",
+    "draw_networkx",
+    "draw_networkx_nodes",
+    "draw_networkx_edges",
+    "draw_networkx_labels",
+    "draw_networkx_edge_labels",
+    "draw_bipartite",
+    "draw_circular",
+    "draw_kamada_kawai",
+    "draw_random",
+    "draw_spectral",
+    "draw_spring",
+    "draw_planar",
+    "draw_shell",
+    "draw_forceatlas2",
+]
+
+
+def apply_matplotlib_colors(
+    G, src_attr, dest_attr, map, vmin=None, vmax=None, nodes=True
+):
+    """
+    Apply colors from a matplotlib colormap to a graph.
+
+    Reads values from the `src_attr` and use a matplotlib colormap
+    to produce a color. Write the color to `dest_attr`.
+
+    Parameters
+    ----------
+    G : nx.Graph
+        The graph to read and compute colors for.
+
+    src_attr : str or other attribute name
+        The name of the attribute to read from the graph.
+
+    dest_attr : str or other attribute name
+        The name of the attribute to write to on the graph.
+
+    map : matplotlib.colormap
+        The matplotlib colormap to use.
+
+    vmin : float, default None
+        The minimum value for scaling the colormap. If `None`, find the
+        minimum value of `src_attr`.
+
+    vmax : float, default None
+        The maximum value for scaling the colormap. If `None`, find the
+        maximum value of `src_attr`.
+
+    nodes : bool, default True
+        Whether the attribute names are edge attributes or node attributes.
+    """
+    import matplotlib as mpl
+
+    if nodes:
+        type_iter = G.nodes()
+    elif G.is_multigraph():
+        type_iter = G.edges(keys=True)
+    else:
+        type_iter = G.edges()
+
+    if vmin is None or vmax is None:
+        vals = [type_iter[a][src_attr] for a in type_iter]
+        if vmin is None:
+            vmin = min(vals)
+        if vmax is None:
+            vmax = max(vals)
+
+    mapper = mpl.cm.ScalarMappable(cmap=map)
+    mapper.set_clim(vmin, vmax)
+
+    def do_map(x):
+        # Cast numpy scalars to float
+        return tuple(float(x) for x in mapper.to_rgba(x))
+
+    if nodes:
+        nx.set_node_attributes(
+            G, {n: do_map(G.nodes[n][src_attr]) for n in G.nodes()}, dest_attr
+        )
+    else:
+        nx.set_edge_attributes(
+            G, {e: do_map(G.edges[e][src_attr]) for e in type_iter}, dest_attr
+        )
+
+
+def display(
+    G,
+    canvas=None,
+    **kwargs,
+):
+    """Draw the graph G.
+
+    Draw the graph as a collection of nodes connected by edges.
+    The exact details of what the graph looks like are controled by the below
+    attributes. All nodes and nodes at the end of visible edges must have a
+    position set, but nearly all other node and edge attributes are options and
+    nodes or edges missing the attribute will use the default listed below. A more
+    complete discription of each parameter is given below this summary.
+
+    .. list-table:: Default Visualization Attributes
+        :widths: 25 25 50
+        :header-rows: 1
+
+        * - Parameter
+          - Default Attribute
+          - Default Value
+        * - pos
+          - `"pos"`
+          - If there is not position, a layout will be calculated with `nx.spring_layout`.
+        * - node_visible
+          - `"visible"`
+          - True
+        * - node_color
+          - `"color"`
+          - #1f78b4
+        * - node_size
+          - `"size"`
+          - 300
+        * - node_label
+          - `"label"`
+          - Dict describing the node label. Defaults create a black text with
+            the node name as the label. The dict respects these keys and defaults:
+
+            * size : 12
+            * color : black
+            * family : sans serif
+            * weight : normal
+            * alpha : 1.0
+            * h_align : center
+            * v_align : center
+            * bbox : Dict describing a `matplotlib.patches.FancyBboxPatch`.
+              Default is None.
+
+        * - node_shape
+          - `"shape"`
+          - "o"
+        * - node_alpha
+          - `"alpha"`
+          - 1.0
+        * - node_border_width
+          - `"border_width"`
+          - 1.0
+        * - node_border_color
+          - `"border_color"`
+          - Matching node_color
+        * - edge_visible
+          - `"visible"`
+          - True
+        * - edge_width
+          - `"width"`
+          - 1.0
+        * - edge_color
+          - `"color"`
+          - Black (#000000)
+        * - edge_label
+          - `"label"`
+          - Dict describing the edge label. Defaults create black text with a
+            white bounding box. The dictionary respects these keys and defaults:
+
+            * size : 12
+            * color : black
+            * family : sans serif
+            * weight : normal
+            * alpha : 1.0
+            * bbox : Dict describing a `matplotlib.patches.FancyBboxPatch`.
+              Default {"boxstyle": "round", "ec": (1.0, 1.0, 1.0), "fc": (1.0, 1.0, 1.0)}
+            * h_align : "center"
+            * v_align : "center"
+            * pos : 0.5
+            * rotate : True
+
+        * - edge_style
+          - `"style"`
+          - "-"
+        * - edge_alpha
+          - `"alpha"`
+          - 1.0
+        * - arrowstyle
+          - `"arrowstyle"`
+          - ``"-|>"`` if `G` is directed else ``"-"``
+        * - arrowsize
+          - `"arrowsize"`
+          - 10 if `G` is directed else 0
+        * - edge_curvature
+          - `"curvature"`
+          - arc3
+        * - edge_source_margin
+          - `"source_margin"`
+          - 0
+        * - edge_target_margin
+          - `"target_margin"`
+          - 0
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    canvas : Matplotlib Axes object, optional
+        Draw the graph in specified Matplotlib axes
+
+    pos : string or function, default "pos"
+        A string naming the node attribute storing the position of nodes as a tuple.
+        Or a function to be called with input `G` which returns the layout as a dict keyed
+        by node to position tuple like the NetworkX layout functions.
+        If no nodes in the graph has the attribute, a spring layout is calculated.
+
+    node_visible : string or bool, default visible
+        A string naming the node attribute which stores if a node should be drawn.
+        If `True`, all nodes will be visible while if `False` no nodes will be visible.
+        If incomplete, nodes missing this attribute will be shown by default.
+
+    node_color : string, default "color"
+        A string naming the node attribute which stores the color of each node.
+        Visible nodes without this attribute will use '#1f78b4' as a default.
+
+    node_size : string or number, default "size"
+        A string naming the node attribute which stores the size of each node.
+        Visible nodes without this attribute will use a default size of 300.
+
+    node_label : string or bool, default "label"
+        A string naming the node attribute which stores the label of each node.
+        The attribute value can be a string, False (no label for that node),
+        True (the node is the label) or a dict keyed by node to the label.
+
+        If a dict is specified, these keys are read to further control the label:
+
+        * label : The text of the label; default: name of the node
+        * size : Font size of the label; default: 12
+        * color : Font color of the label; default: black
+        * family : Font family of the label; default: "sans-serif"
+        * weight : Font weight of the label; default: "normal"
+        * alpha : Alpha value of the label; default: 1.0
+        * h_align : The horizontal alignment of the label.
+            one of "left", "center", "right"; default: "center"
+        * v_align : The vertical alignment of the label.
+            one of "top", "center", "bottom"; default: "center"
+        * bbox : A dict of parameters for `matplotlib.patches.FancyBboxPatch`.
+
+        Visible nodes without this attribute will be treated as if the value was True.
+
+    node_shape : string, default "shape"
+        A string naming the node attribute which stores the label of each node.
+        The values of this attribute are expected to be one of the matplotlib shapes,
+        one of 'so^>v<dph8'. Visible nodes without this attribute will use 'o'.
+
+    node_alpha : string, default "alpha"
+        A string naming the node attribute which stores the alpha of each node.
+        The values of this attribute are expected to be floats between 0.0 and 1.0.
+        Visible nodes without this attribute will be treated as if the value was 1.0.
+
+    node_border_width : string, default "border_width"
+        A string naming the node attribute storing the width of the border of the node.
+        The values of this attribute are expected to be numeric. Visible nodes without
+        this attribute will use the assumed default of 1.0.
+
+    node_border_color : string, default "border_color"
+        A string naming the node attribute which storing the color of the border of the node.
+        Visible nodes missing this attribute will use the final node_color value.
+
+    edge_visible : string or bool, default "visible"
+        A string nameing the edge attribute which stores if an edge should be drawn.
+        If `True`, all edges will be drawn while if `False` no edges will be visible.
+        If incomplete, edges missing this attribute will be shown by default. Values
+        of this attribute are expected to be booleans.
+
+    edge_width : string or int, default "width"
+        A string nameing the edge attribute which stores the width of each edge.
+        Visible edges without this attribute will use a default width of 1.0.
+
+    edge_color : string or color, default "color"
+        A string nameing the edge attribute which stores of color of each edge.
+        Visible edges without this attribute will be drawn black. Each color can be
+        a string or rgb (or rgba) tuple of floats from 0.0 to 1.0.
+
+    edge_label : string, default "label"
+        A string naming the edge attribute which stores the label of each edge.
+        The values of this attribute can be a string, number or False or None. In
+        the latter two cases, no edge label is displayed.
+
+        If a dict is specified, these keys are read to further control the label:
+
+        * label : The text of the label, or the name of an edge attribute holding the label.
+        * size : Font size of the label; default: 12
+        * color : Font color of the label; default: black
+        * family : Font family of the label; default: "sans-serif"
+        * weight : Font weight of the label; default: "normal"
+        * alpha : Alpha value of the label; default: 1.0
+        * h_align : The horizontal alignment of the label.
+            one of "left", "center", "right"; default: "center"
+        * v_align : The vertical alignment of the label.
+            one of "top", "center", "bottom"; default: "center"
+        * bbox : A dict of parameters for `matplotlib.patches.FancyBboxPatch`.
+        * rotate : Whether to rotate labels to lie parallel to the edge, default: True.
+        * pos : A float showing how far along the edge to put the label; default: 0.5.
+
+    edge_style : string, default "style"
+        A string naming the edge attribute which stores the style of each edge.
+        Visible edges without this attribute will be drawn solid. Values of this
+        attribute can be line styles, e.g. '-', '--', '-.' or ':' or words like 'solid'
+        or 'dashed'. If no edge in the graph has this attribute and it is a non-default
+        value, assume that it describes the edge style for all edges in the graph.
+
+    edge_alpha : string or float, default "alpha"
+        A string naming the edge attribute which stores the alpha value of each edge.
+        Visible edges without this attribute will use an alpha value of 1.0.
+
+    arrowstyle : string, default "arrow"
+        A string naming the edge attribute which stores the type of arrowhead to use for
+        each edge. Visible edges without this attribute use ``"-"`` for undirected graphs
+        and ``"-|>"`` for directed graphs.
+
+        See `matplotlib.patches.ArrowStyle` for more options
+
+    arrowsize : string or int, default "arrow_size"
+        A string naming the edge attribute which stores the size of the arrowhead for each
+        edge. Visible edges without this attribute will use a default value of 10.
+
+    edge_curvature : string, default "curvature"
+       A string naming the edge attribute storing the curvature and connection style
+       of each edge. Visible edges without this attribute will use "arc3" as a default
+       value, resulting an a straight line between the two nodes. Curvature can be given
+       as 'arc3,rad=0.2' to specify both the style and radius of curvature.
+
+       Please see `matplotlib.patches.ConnectionStyle` and
+       `matplotlib.patches.FancyArrowPatch` for more information.
+
+    edge_source_margin : string or int, default "source_margin"
+        A string naming the edge attribute which stores the minimum margin (gap) between
+        the source node and the start of the edge. Visible edges without this attribute
+        will use a default value of 0.
+
+    edge_target_margin : string or int, default "target_margin"
+        A string naming the edge attribute which stores the minimumm margin (gap) between
+        the target node and the end of the edge. Visible edges without this attribute
+        will use a default value of 0.
+
+    hide_ticks : bool, default True
+        Weather to remove the ticks from the axes of the matplotlib object.
+
+    Raises
+    ------
+    NetworkXError
+        If a node or edge is missing a required parameter such as `pos` or
+        if `display` receives an argument not listed above.
+
+    ValueError
+        If a node or edge has an invalid color format, i.e. not a color string,
+        rgb tuple or rgba tuple.
+
+    Returns
+    -------
+    The input graph. This is potentially useful for dispatching visualization
+    functions.
+    """
+    from collections import Counter
+
+    import matplotlib as mpl
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    defaults = {
+        "node_pos": None,
+        "node_visible": True,
+        "node_color": "#1f78b4",
+        "node_size": 300,
+        "node_label": {
+            "size": 12,
+            "color": "#000000",
+            "family": "sans-serif",
+            "weight": "normal",
+            "alpha": 1.0,
+            "h_align": "center",
+            "v_align": "center",
+            "bbox": None,
+        },
+        "node_shape": "o",
+        "node_alpha": 1.0,
+        "node_border_width": 1.0,
+        "node_border_color": "face",
+        "edge_visible": True,
+        "edge_width": 1.0,
+        "edge_color": "#000000",
+        "edge_label": {
+            "size": 12,
+            "color": "#000000",
+            "family": "sans-serif",
+            "weight": "normal",
+            "alpha": 1.0,
+            "bbox": {"boxstyle": "round", "ec": (1.0, 1.0, 1.0), "fc": (1.0, 1.0, 1.0)},
+            "h_align": "center",
+            "v_align": "center",
+            "pos": 0.5,
+            "rotate": True,
+        },
+        "edge_style": "-",
+        "edge_alpha": 1.0,
+        "edge_arrowstyle": "-|>" if G.is_directed() else "-",
+        "edge_arrowsize": 10 if G.is_directed() else 0,
+        "edge_curvature": "arc3",
+        "edge_source_margin": 0,
+        "edge_target_margin": 0,
+        "hide_ticks": True,
+    }
+
+    # Check arguments
+    for kwarg in kwargs:
+        if kwarg not in defaults:
+            raise nx.NetworkXError(
+                f"Unrecongized visualization keyword argument: {kwarg}"
+            )
+
+    if canvas is None:
+        canvas = plt.gca()
+
+    if kwargs.get("hide_ticks", defaults["hide_ticks"]):
+        canvas.tick_params(
+            axis="both",
+            which="both",
+            bottom=False,
+            left=False,
+            labelbottom=False,
+            labelleft=False,
+        )
+
+    ### Helper methods and classes
+
+    def node_property_sequence(seq, attr):
+        """Return a list of attribute values for `seq`, using a default if needed"""
+
+        # All node attribute parameters start with "node_"
+        param_name = f"node_{attr}"
+        default = defaults[param_name]
+        attr = kwargs.get(param_name, attr)
+
+        if default is None:
+            # raise instead of using non-existant default value
+            for n in seq:
+                if attr not in node_subgraph.nodes[n]:
+                    raise nx.NetworkXError(f"Attribute '{attr}' missing for node {n}")
+
+        # If `attr` is not a graph attr and was explicitly passed as an argument
+        # it must be a user-default value. Allow attr=None to tell draw to skip
+        # attributes which are on the graph
+        if (
+            attr is not None
+            and nx.get_node_attributes(node_subgraph, attr) == {}
+            and any(attr == v for k, v in kwargs.items() if "node" in k)
+        ):
+            return [attr for _ in seq]
+
+        return [node_subgraph.nodes[n].get(attr, default) for n in seq]
+
+    def compute_colors(color, alpha):
+        if isinstance(color, str):
+            rgba = mpl.colors.colorConverter.to_rgba(color)
+            # Using a non-default alpha value overrides any alpha value in the color
+            if alpha != defaults["node_alpha"]:
+                return (rgba[0], rgba[1], rgba[2], alpha)
+            return rgba
+
+        if isinstance(color, tuple) and len(color) == 3:
+            return (color[0], color[1], color[2], alpha)
+
+        if isinstance(color, tuple) and len(color) == 4:
+            return color
+
+        raise ValueError(f"Invalid format for color: {color}")
+
+    # Find which edges can be plotted as a line collection
+    #
+    # Non-default values for these attributes require fancy arrow patches:
+    # - any arrow style (including the default -|> for directed graphs)
+    # - arrow size (by extension of style)
+    # - connection style
+    # - min_source_margin
+    # - min_target_margin
+
+    def collection_compatible(e):
+        return (
+            get_edge_attr(e, "arrowstyle") == "-"
+            and get_edge_attr(e, "curvature") == "arc3"
+            and get_edge_attr(e, "source_margin") == 0
+            and get_edge_attr(e, "target_margin") == 0
+            # Self-loops will use fancy arrow patches
+            and e[0] != e[1]
+        )
+
+    def edge_property_sequence(seq, attr):
+        """Return a list of attribute values for `seq`, using a default if needed"""
+
+        param_name = f"edge_{attr}"
+        default = defaults[param_name]
+        attr = kwargs.get(param_name, attr)
+
+        if default is None:
+            # raise instead of using non-existant default value
+            for e in seq:
+                if attr not in edge_subgraph.edges[e]:
+                    raise nx.NetworkXError(f"Attribute '{attr}' missing for edge {e}")
+
+        if (
+            attr is not None
+            and nx.get_edge_attributes(edge_subgraph, attr) == {}
+            and any(attr == v for k, v in kwargs.items() if "edge" in k)
+        ):
+            return [attr for _ in seq]
+
+        return [edge_subgraph.edges[e].get(attr, default) for e in seq]
+
+    def get_edge_attr(e, attr):
+        """Return the final edge attribute value, using default if not None"""
+
+        param_name = f"edge_{attr}"
+        default = defaults[param_name]
+        attr = kwargs.get(param_name, attr)
+
+        if default is None and attr not in edge_subgraph.edges[e]:
+            raise nx.NetworkXError(f"Attribute '{attr}' missing from edge {e}")
+
+        if (
+            attr is not None
+            and nx.get_edge_attributes(edge_subgraph, attr) == {}
+            and attr in kwargs.values()
+        ):
+            return attr
+
+        return edge_subgraph.edges[e].get(attr, default)
+
+    def get_node_attr(n, attr, use_edge_subgraph=True):
+        """Return the final node attribute value, using default if not None"""
+        subgraph = edge_subgraph if use_edge_subgraph else node_subgraph
+
+        param_name = f"node_{attr}"
+        default = defaults[param_name]
+        attr = kwargs.get(param_name, attr)
+
+        if default is None and attr not in subgraph.nodes[n]:
+            raise nx.NetworkXError(f"Attribute '{attr}' missing from node {n}")
+
+        if (
+            attr is not None
+            and nx.get_node_attributes(subgraph, attr) == {}
+            and attr in kwargs.values()
+        ):
+            return attr
+
+        return subgraph.nodes[n].get(attr, default)
+
+    # Taken from ConnectionStyleFactory
+    def self_loop(edge_index, node_size):
+        def self_loop_connection(posA, posB, *args, **kwargs):
+            if not np.all(posA == posB):
+                raise nx.NetworkXError(
+                    "`self_loop` connection style method"
+                    "is only to be used for self-loops"
+                )
+            # this is called with _screen space_ values
+            # so convert back to data space
+            data_loc = canvas.transData.inverted().transform(posA)
+            # Scale self loop based on the size of the base node
+            # Size of nodes are given in points ** 2 and each point is 1/72 of an inch
+            v_shift = np.sqrt(node_size) / 72
+            h_shift = v_shift * 0.5
+            # put the top of the loop first so arrow is not hidden by node
+            path = np.asarray(
+                [
+                    # 1
+                    [0, v_shift],
+                    # 4 4 4
+                    [h_shift, v_shift],
+                    [h_shift, 0],
+                    [0, 0],
+                    # 4 4 4
+                    [-h_shift, 0],
+                    [-h_shift, v_shift],
+                    [0, v_shift],
+                ]
+            )
+            # Rotate self loop 90 deg. if more than 1
+            # This will allow for maximum of 4 visible self loops
+            if edge_index % 4:
+                x, y = path.T
+                for _ in range(edge_index % 4):
+                    x, y = y, -x
+                path = np.array([x, y]).T
+            return mpl.path.Path(
+                canvas.transData.transform(data_loc + path), [1, 4, 4, 4, 4, 4, 4]
+            )
+
+        return self_loop_connection
+
+    def to_marker_edge(size, marker):
+        if marker in "s^>v<d":
+            return np.sqrt(2 * size) / 2
+        else:
+            return np.sqrt(size) / 2
+
+    def build_fancy_arrow(e):
+        source_margin = to_marker_edge(
+            get_node_attr(e[0], "size"),
+            get_node_attr(e[0], "shape"),
+        )
+        source_margin = max(
+            source_margin,
+            get_edge_attr(e, "source_margin"),
+        )
+
+        target_margin = to_marker_edge(
+            get_node_attr(e[1], "size"),
+            get_node_attr(e[1], "shape"),
+        )
+        target_margin = max(
+            target_margin,
+            get_edge_attr(e, "target_margin"),
+        )
+        return mpl.patches.FancyArrowPatch(
+            edge_subgraph.nodes[e[0]][pos],
+            edge_subgraph.nodes[e[1]][pos],
+            arrowstyle=get_edge_attr(e, "arrowstyle"),
+            connectionstyle=(
+                get_edge_attr(e, "curvature")
+                if e[0] != e[1]
+                else self_loop(
+                    0 if len(e) == 2 else e[2] % 4,
+                    get_node_attr(e[0], "size"),
+                )
+            ),
+            color=get_edge_attr(e, "color"),
+            linestyle=get_edge_attr(e, "style"),
+            linewidth=get_edge_attr(e, "width"),
+            mutation_scale=get_edge_attr(e, "arrowsize"),
+            shrinkA=source_margin,
+            shrinkB=source_margin,
+            zorder=1,
+        )
+
+    class CurvedArrowText(mpl.text.Text):
+        def __init__(
+            self,
+            arrow,
+            *args,
+            label_pos=0.5,
+            labels_horizontal=False,
+            ax=None,
+            **kwargs,
+        ):
+            # Bind to FancyArrowPatch
+            self.arrow = arrow
+            # how far along the text should be on the curve,
+            # 0 is at start, 1 is at end etc.
+            self.label_pos = label_pos
+            self.labels_horizontal = labels_horizontal
+            if ax is None:
+                ax = plt.gca()
+            self.ax = ax
+            self.x, self.y, self.angle = self._update_text_pos_angle(arrow)
+
+            # Create text object
+            super().__init__(self.x, self.y, *args, rotation=self.angle, **kwargs)
+            # Bind to axis
+            self.ax.add_artist(self)
+
+        def _get_arrow_path_disp(self, arrow):
+            """
+            This is part of FancyArrowPatch._get_path_in_displaycoord
+            It omits the second part of the method where path is converted
+                to polygon based on width
+            The transform is taken from ax, not the object, as the object
+                has not been added yet, and doesn't have transform
+            """
+            dpi_cor = arrow._dpi_cor
+            # trans_data = arrow.get_transform()
+            trans_data = self.ax.transData
+            if arrow._posA_posB is not None:
+                posA = arrow._convert_xy_units(arrow._posA_posB[0])
+                posB = arrow._convert_xy_units(arrow._posA_posB[1])
+                (posA, posB) = trans_data.transform((posA, posB))
+                _path = arrow.get_connectionstyle()(
+                    posA,
+                    posB,
+                    patchA=arrow.patchA,
+                    patchB=arrow.patchB,
+                    shrinkA=arrow.shrinkA * dpi_cor,
+                    shrinkB=arrow.shrinkB * dpi_cor,
+                )
+            else:
+                _path = trans_data.transform_path(arrow._path_original)
+            # Return is in display coordinates
+            return _path
+
+        def _update_text_pos_angle(self, arrow):
+            # Fractional label position
+            path_disp = self._get_arrow_path_disp(arrow)
+            (x1, y1), (cx, cy), (x2, y2) = path_disp.vertices
+            # Text position at a proportion t along the line in display coords
+            # default is 0.5 so text appears at the halfway point
+            t = self.label_pos
+            tt = 1 - t
+            x = tt**2 * x1 + 2 * t * tt * cx + t**2 * x2
+            y = tt**2 * y1 + 2 * t * tt * cy + t**2 * y2
+            if self.labels_horizontal:
+                # Horizontal text labels
+                angle = 0
+            else:
+                # Labels parallel to curve
+                change_x = 2 * tt * (cx - x1) + 2 * t * (x2 - cx)
+                change_y = 2 * tt * (cy - y1) + 2 * t * (y2 - cy)
+                angle = (np.arctan2(change_y, change_x) / (2 * np.pi)) * 360
+                # Text is "right way up"
+                if angle > 90:
+                    angle -= 180
+                if angle < -90:
+                    angle += 180
+            (x, y) = self.ax.transData.inverted().transform((x, y))
+            return x, y, angle
+
+        def draw(self, renderer):
+            # recalculate the text position and angle
+            self.x, self.y, self.angle = self._update_text_pos_angle(self.arrow)
+            self.set_position((self.x, self.y))
+            self.set_rotation(self.angle)
+            # redraw text
+            super().draw(renderer)
+
+    ### Draw the nodes first
+    node_visible = kwargs.get("node_visible", "visible")
+    if isinstance(node_visible, bool):
+        if node_visible:
+            visible_nodes = G.nodes()
+        else:
+            visible_nodes = []
+    else:
+        visible_nodes = [
+            n for n, v in nx.get_node_attributes(G, node_visible, True).items() if v
+        ]
+
+    node_subgraph = G.subgraph(visible_nodes)
+
+    # Ignore the default dict value since that's for default values to use, not
+    # default attribute name
+    pos = kwargs.get("node_pos", "pos")
+
+    default_display_pos_attr = "display's position attribute name"
+    if callable(pos):
+        nx.set_node_attributes(
+            node_subgraph, pos(node_subgraph), default_display_pos_attr
+        )
+        pos = default_display_pos_attr
+        kwargs["node_pos"] = default_display_pos_attr
+    elif nx.get_node_attributes(G, pos) == {}:
+        nx.set_node_attributes(
+            node_subgraph, nx.spring_layout(node_subgraph), default_display_pos_attr
+        )
+        pos = default_display_pos_attr
+        kwargs["node_pos"] = default_display_pos_attr
+
+    # Each shape requires a new scatter object since they can't have different
+    # shapes.
+    if len(visible_nodes) > 0:
+        node_shape = kwargs.get("node_shape", "shape")
+        for shape in Counter(
+            nx.get_node_attributes(
+                node_subgraph, node_shape, defaults["node_shape"]
+            ).values()
+        ):
+            # Filter position just on this shape.
+            nodes_with_shape = [
+                n
+                for n, s in node_subgraph.nodes(data=node_shape)
+                if s == shape or (s is None and shape == defaults["node_shape"])
+            ]
+            # There are two property sequences to create before hand.
+            # 1. position, since it is used for x and y parameters to scatter
+            # 2. edgecolor, since the spaeical 'face' parameter value can only be
+            #    be passed in as the sole string, not part of a list of strings.
+            position = np.asarray(node_property_sequence(nodes_with_shape, "pos"))
+            color = np.asarray(
+                [
+                    compute_colors(c, a)
+                    for c, a in zip(
+                        node_property_sequence(nodes_with_shape, "color"),
+                        node_property_sequence(nodes_with_shape, "alpha"),
+                    )
+                ]
+            )
+            border_color = np.asarray(
+                [
+                    (
+                        c
+                        if (
+                            c := get_node_attr(
+                                n,
+                                "border_color",
+                                False,
+                            )
+                        )
+                        != "face"
+                        else color[i]
+                    )
+                    for i, n in enumerate(nodes_with_shape)
+                ]
+            )
+            canvas.scatter(
+                position[:, 0],
+                position[:, 1],
+                s=node_property_sequence(nodes_with_shape, "size"),
+                c=color,
+                marker=shape,
+                linewidths=node_property_sequence(nodes_with_shape, "border_width"),
+                edgecolors=border_color,
+                zorder=2,
+            )
+
+    ### Draw node labels
+    node_label = kwargs.get("node_label", "label")
+    # Plot labels if node_label is not None and not False
+    if node_label is not None and node_label is not False:
+        default_dict = {}
+        if isinstance(node_label, dict):
+            default_dict = node_label
+            node_label = None
+
+        for n, lbl in node_subgraph.nodes(data=node_label):
+            if lbl is False:
+                continue
+
+            # We work with label dicts down here...
+            if not isinstance(lbl, dict):
+                lbl = {"label": lbl if lbl is not None else n}
+
+            lbl_text = lbl.get("label", n)
+            if not isinstance(lbl_text, str):
+                lbl_text = str(lbl_text)
+
+            lbl.update(default_dict)
+            x, y = node_subgraph.nodes[n][pos]
+            canvas.text(
+                x,
+                y,
+                lbl_text,
+                size=lbl.get("size", defaults["node_label"]["size"]),
+                color=lbl.get("color", defaults["node_label"]["color"]),
+                family=lbl.get("family", defaults["node_label"]["family"]),
+                weight=lbl.get("weight", defaults["node_label"]["weight"]),
+                horizontalalignment=lbl.get(
+                    "h_align", defaults["node_label"]["h_align"]
+                ),
+                verticalalignment=lbl.get("v_align", defaults["node_label"]["v_align"]),
+                transform=canvas.transData,
+                bbox=lbl.get("bbox", defaults["node_label"]["bbox"]),
+            )
+
+    ### Draw edges
+
+    edge_visible = kwargs.get("edge_visible", "visible")
+    if isinstance(edge_visible, bool):
+        if edge_visible:
+            visible_edges = G.edges()
+        else:
+            visible_edges = []
+    else:
+        visible_edges = [
+            e for e, v in nx.get_edge_attributes(G, edge_visible, True).items() if v
+        ]
+
+    edge_subgraph = G.edge_subgraph(visible_edges)
+    print(nx.get_node_attributes(node_subgraph, pos))
+    nx.set_node_attributes(
+        edge_subgraph, nx.get_node_attributes(node_subgraph, pos), name=pos
+    )
+
+    collection_edges = (
+        [e for e in edge_subgraph.edges(keys=True) if collection_compatible(e)]
+        if edge_subgraph.is_multigraph()
+        else [e for e in edge_subgraph.edges() if collection_compatible(e)]
+    )
+    non_collection_edges = (
+        [e for e in edge_subgraph.edges(keys=True) if not collection_compatible(e)]
+        if edge_subgraph.is_multigraph()
+        else [e for e in edge_subgraph.edges() if not collection_compatible(e)]
+    )
+    edge_position = np.asarray(
+        [
+            (
+                get_node_attr(u, "pos", use_edge_subgraph=True),
+                get_node_attr(v, "pos", use_edge_subgraph=True),
+            )
+            for u, v, *_ in collection_edges
+        ]
+    )
+
+    # Only plot a line collection if needed
+    if len(collection_edges) > 0:
+        edge_collection = mpl.collections.LineCollection(
+            edge_position,
+            colors=edge_property_sequence(collection_edges, "color"),
+            linewidths=edge_property_sequence(collection_edges, "width"),
+            linestyle=edge_property_sequence(collection_edges, "style"),
+            alpha=edge_property_sequence(collection_edges, "alpha"),
+            antialiaseds=(1,),
+            zorder=1,
+        )
+        canvas.add_collection(edge_collection)
+
+    fancy_arrows = {}
+    if len(non_collection_edges) > 0:
+        for e in non_collection_edges:
+            # Cache results for use in edge labels
+            fancy_arrows[e] = build_fancy_arrow(e)
+            canvas.add_patch(fancy_arrows[e])
+
+    ### Draw edge labels
+    edge_label = kwargs.get("edge_label", "label")
+    default_dict = {}
+    if isinstance(edge_label, dict):
+        default_dict = edge_label
+        # Restore the default label attribute key of 'label'
+        edge_label = "label"
+
+    # Handle multigraphs
+    edge_label_data = (
+        edge_subgraph.edges(data=edge_label, keys=True)
+        if edge_subgraph.is_multigraph()
+        else edge_subgraph.edges(data=edge_label)
+    )
+    if edge_label is not None and edge_label is not False:
+        for *e, lbl in edge_label_data:
+            e = tuple(e)
+            # I'm not sure how I want to handle None here... For now it means no label
+            if lbl is False or lbl is None:
+                continue
+
+            if not isinstance(lbl, dict):
+                lbl = {"label": lbl}
+
+            lbl.update(default_dict)
+            lbl_text = lbl.get("label")
+            if not isinstance(lbl_text, str):
+                lbl_text = str(lbl_text)
+
+            # In the old code, every non-self-loop is placed via a fancy arrow patch
+            # Only compute a new fancy arrow if needed by caching the results from
+            # edge placement.
+            try:
+                arrow = fancy_arrows[e]
+            except KeyError:
+                arrow = build_fancy_arrow(e)
+
+            if e[0] == e[1]:
+                # Taken directly from draw_networkx_edge_labels
+                connectionstyle_obj = arrow.get_connectionstyle()
+                posA = canvas.transData.transform(edge_subgraph.nodes[e[0]][pos])
+                path_disp = connectionstyle_obj(posA, posA)
+                path_data = canvas.transData.inverted().transform_path(path_disp)
+                x, y = path_data.vertices[0]
+                canvas.text(
+                    x,
+                    y,
+                    lbl_text,
+                    size=lbl.get("size", defaults["edge_label"]["size"]),
+                    color=lbl.get("color", defaults["edge_label"]["color"]),
+                    family=lbl.get("family", defaults["edge_label"]["family"]),
+                    weight=lbl.get("weight", defaults["edge_label"]["weight"]),
+                    alpha=lbl.get("alpha", defaults["edge_label"]["alpha"]),
+                    horizontalalignment=lbl.get(
+                        "h_align", defaults["edge_label"]["h_align"]
+                    ),
+                    verticalalignment=lbl.get(
+                        "v_align", defaults["edge_label"]["v_align"]
+                    ),
+                    rotation=0,
+                    transform=canvas.transData,
+                    bbox=lbl.get("bbox", defaults["edge_label"]["bbox"]),
+                    zorder=1,
+                )
+                continue
+
+            CurvedArrowText(
+                arrow,
+                lbl_text,
+                size=lbl.get("size", defaults["edge_label"]["size"]),
+                color=lbl.get("color", defaults["edge_label"]["color"]),
+                family=lbl.get("family", defaults["edge_label"]["family"]),
+                weight=lbl.get("weight", defaults["edge_label"]["weight"]),
+                alpha=lbl.get("alpha", defaults["edge_label"]["alpha"]),
+                bbox=lbl.get("bbox", defaults["edge_label"]["bbox"]),
+                horizontalalignment=lbl.get(
+                    "h_align", defaults["edge_label"]["h_align"]
+                ),
+                verticalalignment=lbl.get("v_align", defaults["edge_label"]["v_align"]),
+                label_pos=lbl.get("pos", defaults["edge_label"]["pos"]),
+                labels_horizontal=lbl.get("rotate", defaults["edge_label"]["rotate"]),
+                transform=canvas.transData,
+                zorder=1,
+                ax=canvas,
+            )
+
+    # If we had to add an attribute, remove it here
+    if pos == default_display_pos_attr:
+        nx.remove_node_attributes(G, default_display_pos_attr)
+
+    return G
+
+
+def draw(G, pos=None, ax=None, **kwds):
+    """Draw the graph G with Matplotlib.
+
+    Draw the graph as a simple representation with no node
+    labels or edge labels and using the full Matplotlib figure area
+    and no axis labels by default.  See draw_networkx() for more
+    full-featured drawing that allows title, axis labels etc.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    pos : dictionary, optional
+        A dictionary with nodes as keys and positions as values.
+        If not specified a spring layout positioning will be computed.
+        See :py:mod:`networkx.drawing.layout` for functions that
+        compute node positions.
+
+    ax : Matplotlib Axes object, optional
+        Draw the graph in specified Matplotlib axes.
+
+    kwds : optional keywords
+        See networkx.draw_networkx() for a description of optional keywords.
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> nx.draw(G)
+    >>> nx.draw(G, pos=nx.spring_layout(G))  # use spring layout
+
+    See Also
+    --------
+    draw_networkx
+    draw_networkx_nodes
+    draw_networkx_edges
+    draw_networkx_labels
+    draw_networkx_edge_labels
+
+    Notes
+    -----
+    This function has the same name as pylab.draw and pyplot.draw
+    so beware when using `from networkx import *`
+
+    since you might overwrite the pylab.draw function.
+
+    With pyplot use
+
+    >>> import matplotlib.pyplot as plt
+    >>> G = nx.dodecahedral_graph()
+    >>> nx.draw(G)  # networkx draw()
+    >>> plt.draw()  # pyplot draw()
+
+    Also see the NetworkX drawing examples at
+    https://networkx.org/documentation/latest/auto_examples/index.html
+    """
+
+    import matplotlib.pyplot as plt
+
+    if ax is None:
+        cf = plt.gcf()
+    else:
+        cf = ax.get_figure()
+    cf.set_facecolor("w")
+    if ax is None:
+        if cf.axes:
+            ax = cf.gca()
+        else:
+            ax = cf.add_axes((0, 0, 1, 1))
+
+    if "with_labels" not in kwds:
+        kwds["with_labels"] = "labels" in kwds
+
+    draw_networkx(G, pos=pos, ax=ax, **kwds)
+    ax.set_axis_off()
+    plt.draw_if_interactive()
+    return
+
+
+def draw_networkx(G, pos=None, arrows=None, with_labels=True, **kwds):
+    r"""Draw the graph G using Matplotlib.
+
+    Draw the graph with Matplotlib with options for node positions,
+    labeling, titles, and many other drawing features.
+    See draw() for simple drawing without labels or axes.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    pos : dictionary, optional
+        A dictionary with nodes as keys and positions as values.
+        If not specified a spring layout positioning will be computed.
+        See :py:mod:`networkx.drawing.layout` for functions that
+        compute node positions.
+
+    arrows : bool or None, optional (default=None)
+        If `None`, directed graphs draw arrowheads with
+        `~matplotlib.patches.FancyArrowPatch`, while undirected graphs draw edges
+        via `~matplotlib.collections.LineCollection` for speed.
+        If `True`, draw arrowheads with FancyArrowPatches (bendable and stylish).
+        If `False`, draw edges using LineCollection (linear and fast).
+        For directed graphs, if True draw arrowheads.
+        Note: Arrows will be the same color as edges.
+
+    arrowstyle : str (default='-\|>' for directed graphs)
+        For directed graphs, choose the style of the arrowsheads.
+        For undirected graphs default to '-'
+
+        See `matplotlib.patches.ArrowStyle` for more options.
+
+    arrowsize : int or list (default=10)
+        For directed graphs, choose the size of the arrow head's length and
+        width. A list of values can be passed in to assign a different size for arrow head's length and width.
+        See `matplotlib.patches.FancyArrowPatch` for attribute `mutation_scale`
+        for more info.
+
+    with_labels :  bool (default=True)
+        Set to True to draw labels on the nodes.
+
+    ax : Matplotlib Axes object, optional
+        Draw the graph in the specified Matplotlib axes.
+
+    nodelist : list (default=list(G))
+        Draw only specified nodes
+
+    edgelist : list (default=list(G.edges()))
+        Draw only specified edges
+
+    node_size : scalar or array (default=300)
+        Size of nodes.  If an array is specified it must be the
+        same length as nodelist.
+
+    node_color : color or array of colors (default='#1f78b4')
+        Node color. Can be a single color or a sequence of colors with the same
+        length as nodelist. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1. If numeric values are specified they will be
+        mapped to colors using the cmap and vmin,vmax parameters. See
+        matplotlib.scatter for more details.
+
+    node_shape :  string (default='o')
+        The shape of the node.  Specification is as matplotlib.scatter
+        marker, one of 'so^>v<dph8'.
+
+    alpha : float or None (default=None)
+        The node and edge transparency
+
+    cmap : Matplotlib colormap, optional
+        Colormap for mapping intensities of nodes
+
+    vmin,vmax : float, optional
+        Minimum and maximum for node colormap scaling
+
+    linewidths : scalar or sequence (default=1.0)
+        Line width of symbol border
+
+    width : float or array of floats (default=1.0)
+        Line width of edges
+
+    edge_color : color or array of colors (default='k')
+        Edge color. Can be a single color or a sequence of colors with the same
+        length as edgelist. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1. If numeric values are specified they will be
+        mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.
+
+    edge_cmap : Matplotlib colormap, optional
+        Colormap for mapping intensities of edges
+
+    edge_vmin,edge_vmax : floats, optional
+        Minimum and maximum for edge colormap scaling
+
+    style : string (default=solid line)
+        Edge line style e.g.: '-', '--', '-.', ':'
+        or words like 'solid' or 'dashed'.
+        (See `matplotlib.patches.FancyArrowPatch`: `linestyle`)
+
+    labels : dictionary (default=None)
+        Node labels in a dictionary of text labels keyed by node
+
+    font_size : int (default=12 for nodes, 10 for edges)
+        Font size for text labels
+
+    font_color : color (default='k' black)
+        Font color string. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1.
+
+    font_weight : string (default='normal')
+        Font weight
+
+    font_family : string (default='sans-serif')
+        Font family
+
+    label : string, optional
+        Label for graph legend
+
+    hide_ticks : bool, optional
+        Hide ticks of axes. When `True` (the default), ticks and ticklabels
+        are removed from the axes. To set ticks and tick labels to the pyplot default,
+        use ``hide_ticks=False``.
+
+    kwds : optional keywords
+        See networkx.draw_networkx_nodes(), networkx.draw_networkx_edges(), and
+        networkx.draw_networkx_labels() for a description of optional keywords.
+
+    Notes
+    -----
+    For directed graphs, arrows  are drawn at the head end.  Arrows can be
+    turned off with keyword arrows=False.
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> nx.draw(G)
+    >>> nx.draw(G, pos=nx.spring_layout(G))  # use spring layout
+
+    >>> import matplotlib.pyplot as plt
+    >>> limits = plt.axis("off")  # turn off axis
+
+    Also see the NetworkX drawing examples at
+    https://networkx.org/documentation/latest/auto_examples/index.html
+
+    See Also
+    --------
+    draw
+    draw_networkx_nodes
+    draw_networkx_edges
+    draw_networkx_labels
+    draw_networkx_edge_labels
+    """
+    from inspect import signature
+
+    import matplotlib.pyplot as plt
+
+    # Get all valid keywords by inspecting the signatures of draw_networkx_nodes,
+    # draw_networkx_edges, draw_networkx_labels
+
+    valid_node_kwds = signature(draw_networkx_nodes).parameters.keys()
+    valid_edge_kwds = signature(draw_networkx_edges).parameters.keys()
+    valid_label_kwds = signature(draw_networkx_labels).parameters.keys()
+
+    # Create a set with all valid keywords across the three functions and
+    # remove the arguments of this function (draw_networkx)
+    valid_kwds = (valid_node_kwds | valid_edge_kwds | valid_label_kwds) - {
+        "G",
+        "pos",
+        "arrows",
+        "with_labels",
+    }
+
+    if any(k not in valid_kwds for k in kwds):
+        invalid_args = ", ".join([k for k in kwds if k not in valid_kwds])
+        raise ValueError(f"Received invalid argument(s): {invalid_args}")
+
+    node_kwds = {k: v for k, v in kwds.items() if k in valid_node_kwds}
+    edge_kwds = {k: v for k, v in kwds.items() if k in valid_edge_kwds}
+    label_kwds = {k: v for k, v in kwds.items() if k in valid_label_kwds}
+
+    if pos is None:
+        pos = nx.drawing.spring_layout(G)  # default to spring layout
+
+    draw_networkx_nodes(G, pos, **node_kwds)
+    draw_networkx_edges(G, pos, arrows=arrows, **edge_kwds)
+    if with_labels:
+        draw_networkx_labels(G, pos, **label_kwds)
+    plt.draw_if_interactive()
+
+
+def draw_networkx_nodes(
+    G,
+    pos,
+    nodelist=None,
+    node_size=300,
+    node_color="#1f78b4",
+    node_shape="o",
+    alpha=None,
+    cmap=None,
+    vmin=None,
+    vmax=None,
+    ax=None,
+    linewidths=None,
+    edgecolors=None,
+    label=None,
+    margins=None,
+    hide_ticks=True,
+):
+    """Draw the nodes of the graph G.
+
+    This draws only the nodes of the graph G.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    pos : dictionary
+        A dictionary with nodes as keys and positions as values.
+        Positions should be sequences of length 2.
+
+    ax : Matplotlib Axes object, optional
+        Draw the graph in the specified Matplotlib axes.
+
+    nodelist : list (default list(G))
+        Draw only specified nodes
+
+    node_size : scalar or array (default=300)
+        Size of nodes.  If an array it must be the same length as nodelist.
+
+    node_color : color or array of colors (default='#1f78b4')
+        Node color. Can be a single color or a sequence of colors with the same
+        length as nodelist. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1. If numeric values are specified they will be
+        mapped to colors using the cmap and vmin,vmax parameters. See
+        matplotlib.scatter for more details.
+
+    node_shape :  string (default='o')
+        The shape of the node.  Specification is as matplotlib.scatter
+        marker, one of 'so^>v<dph8'.
+
+    alpha : float or array of floats (default=None)
+        The node transparency.  This can be a single alpha value,
+        in which case it will be applied to all the nodes of color. Otherwise,
+        if it is an array, the elements of alpha will be applied to the colors
+        in order (cycling through alpha multiple times if necessary).
+
+    cmap : Matplotlib colormap (default=None)
+        Colormap for mapping intensities of nodes
+
+    vmin,vmax : floats or None (default=None)
+        Minimum and maximum for node colormap scaling
+
+    linewidths : [None | scalar | sequence] (default=1.0)
+        Line width of symbol border
+
+    edgecolors : [None | scalar | sequence] (default = node_color)
+        Colors of node borders. Can be a single color or a sequence of colors with the
+        same length as nodelist. Color can be string or rgb (or rgba) tuple of floats
+        from 0-1. If numeric values are specified they will be mapped to colors
+        using the cmap and vmin,vmax parameters. See `~matplotlib.pyplot.scatter` for more details.
+
+    label : [None | string]
+        Label for legend
+
+    margins : float or 2-tuple, optional
+        Sets the padding for axis autoscaling. Increase margin to prevent
+        clipping for nodes that are near the edges of an image. Values should
+        be in the range ``[0, 1]``. See :meth:`matplotlib.axes.Axes.margins`
+        for details. The default is `None`, which uses the Matplotlib default.
+
+    hide_ticks : bool, optional
+        Hide ticks of axes. When `True` (the default), ticks and ticklabels
+        are removed from the axes. To set ticks and tick labels to the pyplot default,
+        use ``hide_ticks=False``.
+
+    Returns
+    -------
+    matplotlib.collections.PathCollection
+        `PathCollection` of the nodes.
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> nodes = nx.draw_networkx_nodes(G, pos=nx.spring_layout(G))
+
+    Also see the NetworkX drawing examples at
+    https://networkx.org/documentation/latest/auto_examples/index.html
+
+    See Also
+    --------
+    draw
+    draw_networkx
+    draw_networkx_edges
+    draw_networkx_labels
+    draw_networkx_edge_labels
+    """
+    from collections.abc import Iterable
+
+    import matplotlib as mpl
+    import matplotlib.collections  # call as mpl.collections
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    if ax is None:
+        ax = plt.gca()
+
+    if nodelist is None:
+        nodelist = list(G)
+
+    if len(nodelist) == 0:  # empty nodelist, no drawing
+        return mpl.collections.PathCollection(None)
+
+    try:
+        xy = np.asarray([pos[v] for v in nodelist])
+    except KeyError as err:
+        raise nx.NetworkXError(f"Node {err} has no position.") from err
+
+    if isinstance(alpha, Iterable):
+        node_color = apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)
+        alpha = None
+
+    if not isinstance(node_shape, np.ndarray) and not isinstance(node_shape, list):
+        node_shape = np.array([node_shape for _ in range(len(nodelist))])
+
+    for shape in np.unique(node_shape):
+        node_collection = ax.scatter(
+            xy[node_shape == shape, 0],
+            xy[node_shape == shape, 1],
+            s=node_size,
+            c=node_color,
+            marker=shape,
+            cmap=cmap,
+            vmin=vmin,
+            vmax=vmax,
+            alpha=alpha,
+            linewidths=linewidths,
+            edgecolors=edgecolors,
+            label=label,
+        )
+    if hide_ticks:
+        ax.tick_params(
+            axis="both",
+            which="both",
+            bottom=False,
+            left=False,
+            labelbottom=False,
+            labelleft=False,
+        )
+
+    if margins is not None:
+        if isinstance(margins, Iterable):
+            ax.margins(*margins)
+        else:
+            ax.margins(margins)
+
+    node_collection.set_zorder(2)
+    return node_collection
+
+
+class FancyArrowFactory:
+    """Draw arrows with `matplotlib.patches.FancyarrowPatch`"""
+
+    class ConnectionStyleFactory:
+        def __init__(self, connectionstyles, selfloop_height, ax=None):
+            import matplotlib as mpl
+            import matplotlib.path  # call as mpl.path
+            import numpy as np
+
+            self.ax = ax
+            self.mpl = mpl
+            self.np = np
+            self.base_connection_styles = [
+                mpl.patches.ConnectionStyle(cs) for cs in connectionstyles
+            ]
+            self.n = len(self.base_connection_styles)
+            self.selfloop_height = selfloop_height
+
+        def curved(self, edge_index):
+            return self.base_connection_styles[edge_index % self.n]
+
+        def self_loop(self, edge_index):
+            def self_loop_connection(posA, posB, *args, **kwargs):
+                if not self.np.all(posA == posB):
+                    raise nx.NetworkXError(
+                        "`self_loop` connection style method"
+                        "is only to be used for self-loops"
+                    )
+                # this is called with _screen space_ values
+                # so convert back to data space
+                data_loc = self.ax.transData.inverted().transform(posA)
+                v_shift = 0.1 * self.selfloop_height
+                h_shift = v_shift * 0.5
+                # put the top of the loop first so arrow is not hidden by node
+                path = self.np.asarray(
+                    [
+                        # 1
+                        [0, v_shift],
+                        # 4 4 4
+                        [h_shift, v_shift],
+                        [h_shift, 0],
+                        [0, 0],
+                        # 4 4 4
+                        [-h_shift, 0],
+                        [-h_shift, v_shift],
+                        [0, v_shift],
+                    ]
+                )
+                # Rotate self loop 90 deg. if more than 1
+                # This will allow for maximum of 4 visible self loops
+                if edge_index % 4:
+                    x, y = path.T
+                    for _ in range(edge_index % 4):
+                        x, y = y, -x
+                    path = self.np.array([x, y]).T
+                return self.mpl.path.Path(
+                    self.ax.transData.transform(data_loc + path), [1, 4, 4, 4, 4, 4, 4]
+                )
+
+            return self_loop_connection
+
+    def __init__(
+        self,
+        edge_pos,
+        edgelist,
+        nodelist,
+        edge_indices,
+        node_size,
+        selfloop_height,
+        connectionstyle="arc3",
+        node_shape="o",
+        arrowstyle="-",
+        arrowsize=10,
+        edge_color="k",
+        alpha=None,
+        linewidth=1.0,
+        style="solid",
+        min_source_margin=0,
+        min_target_margin=0,
+        ax=None,
+    ):
+        import matplotlib as mpl
+        import matplotlib.patches  # call as mpl.patches
+        import matplotlib.pyplot as plt
+        import numpy as np
+
+        if isinstance(connectionstyle, str):
+            connectionstyle = [connectionstyle]
+        elif np.iterable(connectionstyle):
+            connectionstyle = list(connectionstyle)
+        else:
+            msg = "ConnectionStyleFactory arg `connectionstyle` must be str or iterable"
+            raise nx.NetworkXError(msg)
+        self.ax = ax
+        self.mpl = mpl
+        self.np = np
+        self.edge_pos = edge_pos
+        self.edgelist = edgelist
+        self.nodelist = nodelist
+        self.node_shape = node_shape
+        self.min_source_margin = min_source_margin
+        self.min_target_margin = min_target_margin
+        self.edge_indices = edge_indices
+        self.node_size = node_size
+        self.connectionstyle_factory = self.ConnectionStyleFactory(
+            connectionstyle, selfloop_height, ax
+        )
+        self.arrowstyle = arrowstyle
+        self.arrowsize = arrowsize
+        self.arrow_colors = mpl.colors.colorConverter.to_rgba_array(edge_color, alpha)
+        self.linewidth = linewidth
+        self.style = style
+        if isinstance(arrowsize, list) and len(arrowsize) != len(edge_pos):
+            raise ValueError("arrowsize should have the same length as edgelist")
+
+    def __call__(self, i):
+        (x1, y1), (x2, y2) = self.edge_pos[i]
+        shrink_source = 0  # space from source to tail
+        shrink_target = 0  # space from  head to target
+        if (
+            self.np.iterable(self.min_source_margin)
+            and not isinstance(self.min_source_margin, str)
+            and not isinstance(self.min_source_margin, tuple)
+        ):
+            min_source_margin = self.min_source_margin[i]
+        else:
+            min_source_margin = self.min_source_margin
+
+        if (
+            self.np.iterable(self.min_target_margin)
+            and not isinstance(self.min_target_margin, str)
+            and not isinstance(self.min_target_margin, tuple)
+        ):
+            min_target_margin = self.min_target_margin[i]
+        else:
+            min_target_margin = self.min_target_margin
+
+        if self.np.iterable(self.node_size):  # many node sizes
+            source, target = self.edgelist[i][:2]
+            source_node_size = self.node_size[self.nodelist.index(source)]
+            target_node_size = self.node_size[self.nodelist.index(target)]
+            shrink_source = self.to_marker_edge(source_node_size, self.node_shape)
+            shrink_target = self.to_marker_edge(target_node_size, self.node_shape)
+        else:
+            shrink_source = self.to_marker_edge(self.node_size, self.node_shape)
+            shrink_target = shrink_source
+        shrink_source = max(shrink_source, min_source_margin)
+        shrink_target = max(shrink_target, min_target_margin)
+
+        # scale factor of arrow head
+        if isinstance(self.arrowsize, list):
+            mutation_scale = self.arrowsize[i]
+        else:
+            mutation_scale = self.arrowsize
+
+        if len(self.arrow_colors) > i:
+            arrow_color = self.arrow_colors[i]
+        elif len(self.arrow_colors) == 1:
+            arrow_color = self.arrow_colors[0]
+        else:  # Cycle through colors
+            arrow_color = self.arrow_colors[i % len(self.arrow_colors)]
+
+        if self.np.iterable(self.linewidth):
+            if len(self.linewidth) > i:
+                linewidth = self.linewidth[i]
+            else:
+                linewidth = self.linewidth[i % len(self.linewidth)]
+        else:
+            linewidth = self.linewidth
+
+        if (
+            self.np.iterable(self.style)
+            and not isinstance(self.style, str)
+            and not isinstance(self.style, tuple)
+        ):
+            if len(self.style) > i:
+                linestyle = self.style[i]
+            else:  # Cycle through styles
+                linestyle = self.style[i % len(self.style)]
+        else:
+            linestyle = self.style
+
+        if x1 == x2 and y1 == y2:
+            connectionstyle = self.connectionstyle_factory.self_loop(
+                self.edge_indices[i]
+            )
+        else:
+            connectionstyle = self.connectionstyle_factory.curved(self.edge_indices[i])
+
+        if (
+            self.np.iterable(self.arrowstyle)
+            and not isinstance(self.arrowstyle, str)
+            and not isinstance(self.arrowstyle, tuple)
+        ):
+            arrowstyle = self.arrowstyle[i]
+        else:
+            arrowstyle = self.arrowstyle
+
+        return self.mpl.patches.FancyArrowPatch(
+            (x1, y1),
+            (x2, y2),
+            arrowstyle=arrowstyle,
+            shrinkA=shrink_source,
+            shrinkB=shrink_target,
+            mutation_scale=mutation_scale,
+            color=arrow_color,
+            linewidth=linewidth,
+            connectionstyle=connectionstyle,
+            linestyle=linestyle,
+            zorder=1,  # arrows go behind nodes
+        )
+
+    def to_marker_edge(self, marker_size, marker):
+        if marker in "s^>v<d":  # `large` markers need extra space
+            return self.np.sqrt(2 * marker_size) / 2
+        else:
+            return self.np.sqrt(marker_size) / 2
+
+
+def draw_networkx_edges(
+    G,
+    pos,
+    edgelist=None,
+    width=1.0,
+    edge_color="k",
+    style="solid",
+    alpha=None,
+    arrowstyle=None,
+    arrowsize=10,
+    edge_cmap=None,
+    edge_vmin=None,
+    edge_vmax=None,
+    ax=None,
+    arrows=None,
+    label=None,
+    node_size=300,
+    nodelist=None,
+    node_shape="o",
+    connectionstyle="arc3",
+    min_source_margin=0,
+    min_target_margin=0,
+    hide_ticks=True,
+):
+    r"""Draw the edges of the graph G.
+
+    This draws only the edges of the graph G.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    pos : dictionary
+        A dictionary with nodes as keys and positions as values.
+        Positions should be sequences of length 2.
+
+    edgelist : collection of edge tuples (default=G.edges())
+        Draw only specified edges
+
+    width : float or array of floats (default=1.0)
+        Line width of edges
+
+    edge_color : color or array of colors (default='k')
+        Edge color. Can be a single color or a sequence of colors with the same
+        length as edgelist. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1. If numeric values are specified they will be
+        mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.
+
+    style : string or array of strings (default='solid')
+        Edge line style e.g.: '-', '--', '-.', ':'
+        or words like 'solid' or 'dashed'.
+        Can be a single style or a sequence of styles with the same
+        length as the edge list.
+        If less styles than edges are given the styles will cycle.
+        If more styles than edges are given the styles will be used sequentially
+        and not be exhausted.
+        Also, `(offset, onoffseq)` tuples can be used as style instead of a strings.
+        (See `matplotlib.patches.FancyArrowPatch`: `linestyle`)
+
+    alpha : float or array of floats (default=None)
+        The edge transparency.  This can be a single alpha value,
+        in which case it will be applied to all specified edges. Otherwise,
+        if it is an array, the elements of alpha will be applied to the colors
+        in order (cycling through alpha multiple times if necessary).
+
+    edge_cmap : Matplotlib colormap, optional
+        Colormap for mapping intensities of edges
+
+    edge_vmin,edge_vmax : floats, optional
+        Minimum and maximum for edge colormap scaling
+
+    ax : Matplotlib Axes object, optional
+        Draw the graph in the specified Matplotlib axes.
+
+    arrows : bool or None, optional (default=None)
+        If `None`, directed graphs draw arrowheads with
+        `~matplotlib.patches.FancyArrowPatch`, while undirected graphs draw edges
+        via `~matplotlib.collections.LineCollection` for speed.
+        If `True`, draw arrowheads with FancyArrowPatches (bendable and stylish).
+        If `False`, draw edges using LineCollection (linear and fast).
+
+        Note: Arrowheads will be the same color as edges.
+
+    arrowstyle : str or list of strs (default='-\|>' for directed graphs)
+        For directed graphs and `arrows==True` defaults to '-\|>',
+        For undirected graphs default to '-'.
+
+        See `matplotlib.patches.ArrowStyle` for more options.
+
+    arrowsize : int or list of ints(default=10)
+        For directed graphs, choose the size of the arrow head's length and
+        width. See `matplotlib.patches.FancyArrowPatch` for attribute
+        `mutation_scale` for more info.
+
+    connectionstyle : string or iterable of strings (default="arc3")
+        Pass the connectionstyle parameter to create curved arc of rounding
+        radius rad. For example, connectionstyle='arc3,rad=0.2'.
+        See `matplotlib.patches.ConnectionStyle` and
+        `matplotlib.patches.FancyArrowPatch` for more info.
+        If Iterable, index indicates i'th edge key of MultiGraph
+
+    node_size : scalar or array (default=300)
+        Size of nodes. Though the nodes are not drawn with this function, the
+        node size is used in determining edge positioning.
+
+    nodelist : list, optional (default=G.nodes())
+       This provides the node order for the `node_size` array (if it is an array).
+
+    node_shape :  string (default='o')
+        The marker used for nodes, used in determining edge positioning.
+        Specification is as a `matplotlib.markers` marker, e.g. one of 'so^>v<dph8'.
+
+    label : None or string
+        Label for legend
+
+    min_source_margin : int or list of ints (default=0)
+        The minimum margin (gap) at the beginning of the edge at the source.
+
+    min_target_margin : int or list of ints (default=0)
+        The minimum margin (gap) at the end of the edge at the target.
+
+    hide_ticks : bool, optional
+        Hide ticks of axes. When `True` (the default), ticks and ticklabels
+        are removed from the axes. To set ticks and tick labels to the pyplot default,
+        use ``hide_ticks=False``.
+
+    Returns
+    -------
+     matplotlib.collections.LineCollection or a list of matplotlib.patches.FancyArrowPatch
+        If ``arrows=True``, a list of FancyArrowPatches is returned.
+        If ``arrows=False``, a LineCollection is returned.
+        If ``arrows=None`` (the default), then a LineCollection is returned if
+        `G` is undirected, otherwise returns a list of FancyArrowPatches.
+
+    Notes
+    -----
+    For directed graphs, arrows are drawn at the head end.  Arrows can be
+    turned off with keyword arrows=False or by passing an arrowstyle without
+    an arrow on the end.
+
+    Be sure to include `node_size` as a keyword argument; arrows are
+    drawn considering the size of nodes.
+
+    Self-loops are always drawn with `~matplotlib.patches.FancyArrowPatch`
+    regardless of the value of `arrows` or whether `G` is directed.
+    When ``arrows=False`` or ``arrows=None`` and `G` is undirected, the
+    FancyArrowPatches corresponding to the self-loops are not explicitly
+    returned. They should instead be accessed via the ``Axes.patches``
+    attribute (see examples).
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
+
+    >>> G = nx.DiGraph()
+    >>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])
+    >>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
+    >>> alphas = [0.3, 0.4, 0.5]
+    >>> for i, arc in enumerate(arcs):  # change alpha values of arcs
+    ...     arc.set_alpha(alphas[i])
+
+    The FancyArrowPatches corresponding to self-loops are not always
+    returned, but can always be accessed via the ``patches`` attribute of the
+    `matplotlib.Axes` object.
+
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots()
+    >>> G = nx.Graph([(0, 1), (0, 0)])  # Self-loop at node 0
+    >>> edge_collection = nx.draw_networkx_edges(G, pos=nx.circular_layout(G), ax=ax)
+    >>> self_loop_fap = ax.patches[0]
+
+    Also see the NetworkX drawing examples at
+    https://networkx.org/documentation/latest/auto_examples/index.html
+
+    See Also
+    --------
+    draw
+    draw_networkx
+    draw_networkx_nodes
+    draw_networkx_labels
+    draw_networkx_edge_labels
+
+    """
+    import warnings
+
+    import matplotlib as mpl
+    import matplotlib.collections  # call as mpl.collections
+    import matplotlib.colors  # call as mpl.colors
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    # The default behavior is to use LineCollection to draw edges for
+    # undirected graphs (for performance reasons) and use FancyArrowPatches
+    # for directed graphs.
+    # The `arrows` keyword can be used to override the default behavior
+    if arrows is None:
+        use_linecollection = not (G.is_directed() or G.is_multigraph())
+    else:
+        if not isinstance(arrows, bool):
+            raise TypeError("Argument `arrows` must be of type bool or None")
+        use_linecollection = not arrows
+
+    if isinstance(connectionstyle, str):
+        connectionstyle = [connectionstyle]
+    elif np.iterable(connectionstyle):
+        connectionstyle = list(connectionstyle)
+    else:
+        msg = "draw_networkx_edges arg `connectionstyle` must be str or iterable"
+        raise nx.NetworkXError(msg)
+
+    # Some kwargs only apply to FancyArrowPatches. Warn users when they use
+    # non-default values for these kwargs when LineCollection is being used
+    # instead of silently ignoring the specified option
+    if use_linecollection:
+        msg = (
+            "\n\nThe {0} keyword argument is not applicable when drawing edges\n"
+            "with LineCollection.\n\n"
+            "To make this warning go away, either specify `arrows=True` to\n"
+            "force FancyArrowPatches or use the default values.\n"
+            "Note that using FancyArrowPatches may be slow for large graphs.\n"
+        )
+        if arrowstyle is not None:
+            warnings.warn(msg.format("arrowstyle"), category=UserWarning, stacklevel=2)
+        if arrowsize != 10:
+            warnings.warn(msg.format("arrowsize"), category=UserWarning, stacklevel=2)
+        if min_source_margin != 0:
+            warnings.warn(
+                msg.format("min_source_margin"), category=UserWarning, stacklevel=2
+            )
+        if min_target_margin != 0:
+            warnings.warn(
+                msg.format("min_target_margin"), category=UserWarning, stacklevel=2
+            )
+        if any(cs != "arc3" for cs in connectionstyle):
+            warnings.warn(
+                msg.format("connectionstyle"), category=UserWarning, stacklevel=2
+            )
+
+    # NOTE: Arrowstyle modification must occur after the warnings section
+    if arrowstyle is None:
+        arrowstyle = "-|>" if G.is_directed() else "-"
+
+    if ax is None:
+        ax = plt.gca()
+
+    if edgelist is None:
+        edgelist = list(G.edges)  # (u, v, k) for multigraph (u, v) otherwise
+
+    if len(edgelist):
+        if G.is_multigraph():
+            key_count = collections.defaultdict(lambda: itertools.count(0))
+            edge_indices = [next(key_count[tuple(e[:2])]) for e in edgelist]
+        else:
+            edge_indices = [0] * len(edgelist)
+    else:  # no edges!
+        return []
+
+    if nodelist is None:
+        nodelist = list(G.nodes())
+
+    # FancyArrowPatch handles color=None different from LineCollection
+    if edge_color is None:
+        edge_color = "k"
+
+    # set edge positions
+    edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
+
+    # Check if edge_color is an array of floats and map to edge_cmap.
+    # This is the only case handled differently from matplotlib
+    if (
+        np.iterable(edge_color)
+        and (len(edge_color) == len(edge_pos))
+        and np.all([isinstance(c, Number) for c in edge_color])
+    ):
+        if edge_cmap is not None:
+            assert isinstance(edge_cmap, mpl.colors.Colormap)
+        else:
+            edge_cmap = plt.get_cmap()
+        if edge_vmin is None:
+            edge_vmin = min(edge_color)
+        if edge_vmax is None:
+            edge_vmax = max(edge_color)
+        color_normal = mpl.colors.Normalize(vmin=edge_vmin, vmax=edge_vmax)
+        edge_color = [edge_cmap(color_normal(e)) for e in edge_color]
+
+    # compute initial view
+    minx = np.amin(np.ravel(edge_pos[:, :, 0]))
+    maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
+    miny = np.amin(np.ravel(edge_pos[:, :, 1]))
+    maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
+    w = maxx - minx
+    h = maxy - miny
+
+    # Self-loops are scaled by view extent, except in cases the extent
+    # is 0, e.g. for a single node. In this case, fall back to scaling
+    # by the maximum node size
+    selfloop_height = h if h != 0 else 0.005 * np.array(node_size).max()
+    fancy_arrow_factory = FancyArrowFactory(
+        edge_pos,
+        edgelist,
+        nodelist,
+        edge_indices,
+        node_size,
+        selfloop_height,
+        connectionstyle,
+        node_shape,
+        arrowstyle,
+        arrowsize,
+        edge_color,
+        alpha,
+        width,
+        style,
+        min_source_margin,
+        min_target_margin,
+        ax=ax,
+    )
+
+    # Draw the edges
+    if use_linecollection:
+        edge_collection = mpl.collections.LineCollection(
+            edge_pos,
+            colors=edge_color,
+            linewidths=width,
+            antialiaseds=(1,),
+            linestyle=style,
+            alpha=alpha,
+        )
+        edge_collection.set_cmap(edge_cmap)
+        edge_collection.set_clim(edge_vmin, edge_vmax)
+        edge_collection.set_zorder(1)  # edges go behind nodes
+        edge_collection.set_label(label)
+        ax.add_collection(edge_collection)
+        edge_viz_obj = edge_collection
+
+        # Make sure selfloop edges are also drawn
+        # ---------------------------------------
+        selfloops_to_draw = [loop for loop in nx.selfloop_edges(G) if loop in edgelist]
+        if selfloops_to_draw:
+            edgelist_tuple = list(map(tuple, edgelist))
+            arrow_collection = []
+            for loop in selfloops_to_draw:
+                i = edgelist_tuple.index(loop)
+                arrow = fancy_arrow_factory(i)
+                arrow_collection.append(arrow)
+                ax.add_patch(arrow)
+    else:
+        edge_viz_obj = []
+        for i in range(len(edgelist)):
+            arrow = fancy_arrow_factory(i)
+            ax.add_patch(arrow)
+            edge_viz_obj.append(arrow)
+
+    # update view after drawing
+    padx, pady = 0.05 * w, 0.05 * h
+    corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
+    ax.update_datalim(corners)
+    ax.autoscale_view()
+
+    if hide_ticks:
+        ax.tick_params(
+            axis="both",
+            which="both",
+            bottom=False,
+            left=False,
+            labelbottom=False,
+            labelleft=False,
+        )
+
+    return edge_viz_obj
+
+
+def draw_networkx_labels(
+    G,
+    pos,
+    labels=None,
+    font_size=12,
+    font_color="k",
+    font_family="sans-serif",
+    font_weight="normal",
+    alpha=None,
+    bbox=None,
+    horizontalalignment="center",
+    verticalalignment="center",
+    ax=None,
+    clip_on=True,
+    hide_ticks=True,
+):
+    """Draw node labels on the graph G.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    pos : dictionary
+        A dictionary with nodes as keys and positions as values.
+        Positions should be sequences of length 2.
+
+    labels : dictionary (default={n: n for n in G})
+        Node labels in a dictionary of text labels keyed by node.
+        Node-keys in labels should appear as keys in `pos`.
+        If needed use: `{n:lab for n,lab in labels.items() if n in pos}`
+
+    font_size : int or dictionary of nodes to ints (default=12)
+        Font size for text labels.
+
+    font_color : color or dictionary of nodes to colors (default='k' black)
+        Font color string. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1.
+
+    font_weight : string or dictionary of nodes to strings (default='normal')
+        Font weight.
+
+    font_family : string or dictionary of nodes to strings (default='sans-serif')
+        Font family.
+
+    alpha : float or None or dictionary of nodes to floats (default=None)
+        The text transparency.
+
+    bbox : Matplotlib bbox, (default is Matplotlib's ax.text default)
+        Specify text box properties (e.g. shape, color etc.) for node labels.
+
+    horizontalalignment : string or array of strings (default='center')
+        Horizontal alignment {'center', 'right', 'left'}. If an array is
+        specified it must be the same length as `nodelist`.
+
+    verticalalignment : string (default='center')
+        Vertical alignment {'center', 'top', 'bottom', 'baseline', 'center_baseline'}.
+        If an array is specified it must be the same length as `nodelist`.
+
+    ax : Matplotlib Axes object, optional
+        Draw the graph in the specified Matplotlib axes.
+
+    clip_on : bool (default=True)
+        Turn on clipping of node labels at axis boundaries
+
+    hide_ticks : bool, optional
+        Hide ticks of axes. When `True` (the default), ticks and ticklabels
+        are removed from the axes. To set ticks and tick labels to the pyplot default,
+        use ``hide_ticks=False``.
+
+    Returns
+    -------
+    dict
+        `dict` of labels keyed on the nodes
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> labels = nx.draw_networkx_labels(G, pos=nx.spring_layout(G))
+
+    Also see the NetworkX drawing examples at
+    https://networkx.org/documentation/latest/auto_examples/index.html
+
+    See Also
+    --------
+    draw
+    draw_networkx
+    draw_networkx_nodes
+    draw_networkx_edges
+    draw_networkx_edge_labels
+    """
+    import matplotlib.pyplot as plt
+
+    if ax is None:
+        ax = plt.gca()
+
+    if labels is None:
+        labels = {n: n for n in G.nodes()}
+
+    individual_params = set()
+
+    def check_individual_params(p_value, p_name):
+        if isinstance(p_value, dict):
+            if len(p_value) != len(labels):
+                raise ValueError(f"{p_name} must have the same length as labels.")
+            individual_params.add(p_name)
+
+    def get_param_value(node, p_value, p_name):
+        if p_name in individual_params:
+            return p_value[node]
+        return p_value
+
+    check_individual_params(font_size, "font_size")
+    check_individual_params(font_color, "font_color")
+    check_individual_params(font_weight, "font_weight")
+    check_individual_params(font_family, "font_family")
+    check_individual_params(alpha, "alpha")
+
+    text_items = {}  # there is no text collection so we'll fake one
+    for n, label in labels.items():
+        (x, y) = pos[n]
+        if not isinstance(label, str):
+            label = str(label)  # this makes "1" and 1 labeled the same
+        t = ax.text(
+            x,
+            y,
+            label,
+            size=get_param_value(n, font_size, "font_size"),
+            color=get_param_value(n, font_color, "font_color"),
+            family=get_param_value(n, font_family, "font_family"),
+            weight=get_param_value(n, font_weight, "font_weight"),
+            alpha=get_param_value(n, alpha, "alpha"),
+            horizontalalignment=horizontalalignment,
+            verticalalignment=verticalalignment,
+            transform=ax.transData,
+            bbox=bbox,
+            clip_on=clip_on,
+        )
+        text_items[n] = t
+
+    if hide_ticks:
+        ax.tick_params(
+            axis="both",
+            which="both",
+            bottom=False,
+            left=False,
+            labelbottom=False,
+            labelleft=False,
+        )
+
+    return text_items
+
+
+def draw_networkx_edge_labels(
+    G,
+    pos,
+    edge_labels=None,
+    label_pos=0.5,
+    font_size=10,
+    font_color="k",
+    font_family="sans-serif",
+    font_weight="normal",
+    alpha=None,
+    bbox=None,
+    horizontalalignment="center",
+    verticalalignment="center",
+    ax=None,
+    rotate=True,
+    clip_on=True,
+    node_size=300,
+    nodelist=None,
+    connectionstyle="arc3",
+    hide_ticks=True,
+):
+    """Draw edge labels.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    pos : dictionary
+        A dictionary with nodes as keys and positions as values.
+        Positions should be sequences of length 2.
+
+    edge_labels : dictionary (default=None)
+        Edge labels in a dictionary of labels keyed by edge two-tuple.
+        Only labels for the keys in the dictionary are drawn.
+
+    label_pos : float (default=0.5)
+        Position of edge label along edge (0=head, 0.5=center, 1=tail)
+
+    font_size : int (default=10)
+        Font size for text labels
+
+    font_color : color (default='k' black)
+        Font color string. Color can be string or rgb (or rgba) tuple of
+        floats from 0-1.
+
+    font_weight : string (default='normal')
+        Font weight
+
+    font_family : string (default='sans-serif')
+        Font family
+
+    alpha : float or None (default=None)
+        The text transparency
+
+    bbox : Matplotlib bbox, optional
+        Specify text box properties (e.g. shape, color etc.) for edge labels.
+        Default is {boxstyle='round', ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0)}.
+
+    horizontalalignment : string (default='center')
+        Horizontal alignment {'center', 'right', 'left'}
+
+    verticalalignment : string (default='center')
+        Vertical alignment {'center', 'top', 'bottom', 'baseline', 'center_baseline'}
+
+    ax : Matplotlib Axes object, optional
+        Draw the graph in the specified Matplotlib axes.
+
+    rotate : bool (default=True)
+        Rotate edge labels to lie parallel to edges
+
+    clip_on : bool (default=True)
+        Turn on clipping of edge labels at axis boundaries
+
+    node_size : scalar or array (default=300)
+        Size of nodes.  If an array it must be the same length as nodelist.
+
+    nodelist : list, optional (default=G.nodes())
+       This provides the node order for the `node_size` array (if it is an array).
+
+    connectionstyle : string or iterable of strings (default="arc3")
+        Pass the connectionstyle parameter to create curved arc of rounding
+        radius rad. For example, connectionstyle='arc3,rad=0.2'.
+        See `matplotlib.patches.ConnectionStyle` and
+        `matplotlib.patches.FancyArrowPatch` for more info.
+        If Iterable, index indicates i'th edge key of MultiGraph
+
+    hide_ticks : bool, optional
+        Hide ticks of axes. When `True` (the default), ticks and ticklabels
+        are removed from the axes. To set ticks and tick labels to the pyplot default,
+        use ``hide_ticks=False``.
+
+    Returns
+    -------
+    dict
+        `dict` of labels keyed by edge
+
+    Examples
+    --------
+    >>> G = nx.dodecahedral_graph()
+    >>> edge_labels = nx.draw_networkx_edge_labels(G, pos=nx.spring_layout(G))
+
+    Also see the NetworkX drawing examples at
+    https://networkx.org/documentation/latest/auto_examples/index.html
+
+    See Also
+    --------
+    draw
+    draw_networkx
+    draw_networkx_nodes
+    draw_networkx_edges
+    draw_networkx_labels
+    """
+    import matplotlib as mpl
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    class CurvedArrowText(mpl.text.Text):
+        def __init__(
+            self,
+            arrow,
+            *args,
+            label_pos=0.5,
+            labels_horizontal=False,
+            ax=None,
+            **kwargs,
+        ):
+            # Bind to FancyArrowPatch
+            self.arrow = arrow
+            # how far along the text should be on the curve,
+            # 0 is at start, 1 is at end etc.
+            self.label_pos = label_pos
+            self.labels_horizontal = labels_horizontal
+            if ax is None:
+                ax = plt.gca()
+            self.ax = ax
+            self.x, self.y, self.angle = self._update_text_pos_angle(arrow)
+
+            # Create text object
+            super().__init__(self.x, self.y, *args, rotation=self.angle, **kwargs)
+            # Bind to axis
+            self.ax.add_artist(self)
+
+        def _get_arrow_path_disp(self, arrow):
+            """
+            This is part of FancyArrowPatch._get_path_in_displaycoord
+            It omits the second part of the method where path is converted
+                to polygon based on width
+            The transform is taken from ax, not the object, as the object
+                has not been added yet, and doesn't have transform
+            """
+            dpi_cor = arrow._dpi_cor
+            trans_data = self.ax.transData
+            if arrow._posA_posB is None:
+                raise ValueError(
+                    "Can only draw labels for fancy arrows with "
+                    "posA and posB inputs, not custom path"
+                )
+            posA = arrow._convert_xy_units(arrow._posA_posB[0])
+            posB = arrow._convert_xy_units(arrow._posA_posB[1])
+            (posA, posB) = trans_data.transform((posA, posB))
+            _path = arrow.get_connectionstyle()(
+                posA,
+                posB,
+                patchA=arrow.patchA,
+                patchB=arrow.patchB,
+                shrinkA=arrow.shrinkA * dpi_cor,
+                shrinkB=arrow.shrinkB * dpi_cor,
+            )
+            # Return is in display coordinates
+            return _path
+
+        def _update_text_pos_angle(self, arrow):
+            # Fractional label position
+            # Text position at a proportion t along the line in display coords
+            # default is 0.5 so text appears at the halfway point
+            t = self.label_pos
+            tt = 1 - t
+            path_disp = self._get_arrow_path_disp(arrow)
+            is_bar_style = isinstance(
+                arrow.get_connectionstyle(), mpl.patches.ConnectionStyle.Bar
+            )
+            # 1. Calculate x and y
+            if is_bar_style:
+                # Bar Connection Style - straight line
+                _, (cx1, cy1), (cx2, cy2), _ = path_disp.vertices
+                x = cx1 * tt + cx2 * t
+                y = cy1 * tt + cy2 * t
+            else:
+                # Arc or Angle type Connection Styles - Bezier curve
+                (x1, y1), (cx, cy), (x2, y2) = path_disp.vertices
+                x = tt**2 * x1 + 2 * t * tt * cx + t**2 * x2
+                y = tt**2 * y1 + 2 * t * tt * cy + t**2 * y2
+            # 2. Calculate Angle
+            if self.labels_horizontal:
+                # Horizontal text labels
+                angle = 0
+            else:
+                # Labels parallel to curve
+                if is_bar_style:
+                    change_x = (cx2 - cx1) / 2
+                    change_y = (cy2 - cy1) / 2
+                else:
+                    change_x = 2 * tt * (cx - x1) + 2 * t * (x2 - cx)
+                    change_y = 2 * tt * (cy - y1) + 2 * t * (y2 - cy)
+                angle = np.arctan2(change_y, change_x) / (2 * np.pi) * 360
+                # Text is "right way up"
+                if angle > 90:
+                    angle -= 180
+                elif angle < -90:
+                    angle += 180
+            (x, y) = self.ax.transData.inverted().transform((x, y))
+            return x, y, angle
+
+        def draw(self, renderer):
+            # recalculate the text position and angle
+            self.x, self.y, self.angle = self._update_text_pos_angle(self.arrow)
+            self.set_position((self.x, self.y))
+            self.set_rotation(self.angle)
+            # redraw text
+            super().draw(renderer)
+
+    # use default box of white with white border
+    if bbox is None:
+        bbox = {"boxstyle": "round", "ec": (1.0, 1.0, 1.0), "fc": (1.0, 1.0, 1.0)}
+
+    if isinstance(connectionstyle, str):
+        connectionstyle = [connectionstyle]
+    elif np.iterable(connectionstyle):
+        connectionstyle = list(connectionstyle)
+    else:
+        raise nx.NetworkXError(
+            "draw_networkx_edges arg `connectionstyle` must be"
+            "string or iterable of strings"
+        )
+
+    if ax is None:
+        ax = plt.gca()
+
+    if edge_labels is None:
+        kwds = {"keys": True} if G.is_multigraph() else {}
+        edge_labels = {tuple(edge): d for *edge, d in G.edges(data=True, **kwds)}
+    # NOTHING TO PLOT
+    if not edge_labels:
+        return {}
+    edgelist, labels = zip(*edge_labels.items())
+
+    if nodelist is None:
+        nodelist = list(G.nodes())
+
+    # set edge positions
+    edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
+
+    if G.is_multigraph():
+        key_count = collections.defaultdict(lambda: itertools.count(0))
+        edge_indices = [next(key_count[tuple(e[:2])]) for e in edgelist]
+    else:
+        edge_indices = [0] * len(edgelist)
+
+    # Used to determine self loop mid-point
+    # Note, that this will not be accurate,
+    #   if not drawing edge_labels for all edges drawn
+    h = 0
+    if edge_labels:
+        miny = np.amin(np.ravel(edge_pos[:, :, 1]))
+        maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
+        h = maxy - miny
+    selfloop_height = h if h != 0 else 0.005 * np.array(node_size).max()
+    fancy_arrow_factory = FancyArrowFactory(
+        edge_pos,
+        edgelist,
+        nodelist,
+        edge_indices,
+        node_size,
+        selfloop_height,
+        connectionstyle,
+        ax=ax,
+    )
+
+    individual_params = {}
+
+    def check_individual_params(p_value, p_name):
+        # TODO should this be list or array (as in a numpy array)?
+        if isinstance(p_value, list):
+            if len(p_value) != len(edgelist):
+                raise ValueError(f"{p_name} must have the same length as edgelist.")
+            individual_params[p_name] = p_value.iter()
+
+    # Don't need to pass in an edge because these are lists, not dicts
+    def get_param_value(p_value, p_name):
+        if p_name in individual_params:
+            return next(individual_params[p_name])
+        return p_value
+
+    check_individual_params(font_size, "font_size")
+    check_individual_params(font_color, "font_color")
+    check_individual_params(font_weight, "font_weight")
+    check_individual_params(alpha, "alpha")
+    check_individual_params(horizontalalignment, "horizontalalignment")
+    check_individual_params(verticalalignment, "verticalalignment")
+    check_individual_params(rotate, "rotate")
+    check_individual_params(label_pos, "label_pos")
+
+    text_items = {}
+    for i, (edge, label) in enumerate(zip(edgelist, labels)):
+        if not isinstance(label, str):
+            label = str(label)  # this makes "1" and 1 labeled the same
+
+        n1, n2 = edge[:2]
+        arrow = fancy_arrow_factory(i)
+        if n1 == n2:
+            connectionstyle_obj = arrow.get_connectionstyle()
+            posA = ax.transData.transform(pos[n1])
+            path_disp = connectionstyle_obj(posA, posA)
+            path_data = ax.transData.inverted().transform_path(path_disp)
+            x, y = path_data.vertices[0]
+            text_items[edge] = ax.text(
+                x,
+                y,
+                label,
+                size=get_param_value(font_size, "font_size"),
+                color=get_param_value(font_color, "font_color"),
+                family=get_param_value(font_family, "font_family"),
+                weight=get_param_value(font_weight, "font_weight"),
+                alpha=get_param_value(alpha, "alpha"),
+                horizontalalignment=get_param_value(
+                    horizontalalignment, "horizontalalignment"
+                ),
+                verticalalignment=get_param_value(
+                    verticalalignment, "verticalalignment"
+                ),
+                rotation=0,
+                transform=ax.transData,
+                bbox=bbox,
+                zorder=1,
+                clip_on=clip_on,
+            )
+        else:
+            text_items[edge] = CurvedArrowText(
+                arrow,
+                label,
+                size=get_param_value(font_size, "font_size"),
+                color=get_param_value(font_color, "font_color"),
+                family=get_param_value(font_family, "font_family"),
+                weight=get_param_value(font_weight, "font_weight"),
+                alpha=get_param_value(alpha, "alpha"),
+                horizontalalignment=get_param_value(
+                    horizontalalignment, "horizontalalignment"
+                ),
+                verticalalignment=get_param_value(
+                    verticalalignment, "verticalalignment"
+                ),
+                transform=ax.transData,
+                bbox=bbox,
+                zorder=1,
+                clip_on=clip_on,
+                label_pos=get_param_value(label_pos, "label_pos"),
+                labels_horizontal=not get_param_value(rotate, "rotate"),
+                ax=ax,
+            )
+
+    if hide_ticks:
+        ax.tick_params(
+            axis="both",
+            which="both",
+            bottom=False,
+            left=False,
+            labelbottom=False,
+            labelleft=False,
+        )
+
+    return text_items
+
+
+def draw_bipartite(G, **kwargs):
+    """Draw the graph `G` with a bipartite layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.bipartite_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Raises
+    ------
+    NetworkXError :
+        If `G` is not bipartite.
+
+    Notes
+    -----
+    The layout is computed each time this function is called. For
+    repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.bipartite_layout` directly and reuse the result::
+
+        >>> G = nx.complete_bipartite_graph(3, 3)
+        >>> pos = nx.bipartite_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.complete_bipartite_graph(2, 5)
+    >>> nx.draw_bipartite(G)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.bipartite_layout`
+    """
+    draw(G, pos=nx.bipartite_layout(G), **kwargs)
+
+
+def draw_circular(G, **kwargs):
+    """Draw the graph `G` with a circular layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.circular_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Notes
+    -----
+    The layout is computed each time this function is called. For
+    repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.circular_layout` directly and reuse the result::
+
+        >>> G = nx.complete_graph(5)
+        >>> pos = nx.circular_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.draw_circular(G)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.circular_layout`
+    """
+    draw(G, pos=nx.circular_layout(G), **kwargs)
+
+
+def draw_kamada_kawai(G, **kwargs):
+    """Draw the graph `G` with a Kamada-Kawai force-directed layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.kamada_kawai_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Notes
+    -----
+    The layout is computed each time this function is called.
+    For repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.kamada_kawai_layout` directly and reuse the
+    result::
+
+        >>> G = nx.complete_graph(5)
+        >>> pos = nx.kamada_kawai_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.draw_kamada_kawai(G)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.kamada_kawai_layout`
+    """
+    draw(G, pos=nx.kamada_kawai_layout(G), **kwargs)
+
+
+def draw_random(G, **kwargs):
+    """Draw the graph `G` with a random layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.random_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Notes
+    -----
+    The layout is computed each time this function is called.
+    For repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.random_layout` directly and reuse the result::
+
+        >>> G = nx.complete_graph(5)
+        >>> pos = nx.random_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.lollipop_graph(4, 3)
+    >>> nx.draw_random(G)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.random_layout`
+    """
+    draw(G, pos=nx.random_layout(G), **kwargs)
+
+
+def draw_spectral(G, **kwargs):
+    """Draw the graph `G` with a spectral 2D layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.spectral_layout(G), **kwargs)
+
+    For more information about how node positions are determined, see
+    `~networkx.drawing.layout.spectral_layout`.
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Notes
+    -----
+    The layout is computed each time this function is called.
+    For repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.spectral_layout` directly and reuse the result::
+
+        >>> G = nx.complete_graph(5)
+        >>> pos = nx.spectral_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.draw_spectral(G)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.spectral_layout`
+    """
+    draw(G, pos=nx.spectral_layout(G), **kwargs)
+
+
+def draw_spring(G, **kwargs):
+    """Draw the graph `G` with a spring layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.spring_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Notes
+    -----
+    `~networkx.drawing.layout.spring_layout` is also the default layout for
+    `draw`, so this function is equivalent to `draw`.
+
+    The layout is computed each time this function is called.
+    For repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.spring_layout` directly and reuse the result::
+
+        >>> G = nx.complete_graph(5)
+        >>> pos = nx.spring_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.path_graph(20)
+    >>> nx.draw_spring(G)
+
+    See Also
+    --------
+    draw
+    :func:`~networkx.drawing.layout.spring_layout`
+    """
+    draw(G, pos=nx.spring_layout(G), **kwargs)
+
+
+def draw_shell(G, nlist=None, **kwargs):
+    """Draw networkx graph `G` with shell layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.shell_layout(G, nlist=nlist), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A networkx graph
+
+    nlist : list of list of nodes, optional
+        A list containing lists of nodes representing the shells.
+        Default is `None`, meaning all nodes are in a single shell.
+        See `~networkx.drawing.layout.shell_layout` for details.
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Notes
+    -----
+    The layout is computed each time this function is called.
+    For repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.shell_layout` directly and reuse the result::
+
+        >>> G = nx.complete_graph(5)
+        >>> pos = nx.shell_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> shells = [[0], [1, 2, 3]]
+    >>> nx.draw_shell(G, nlist=shells)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.shell_layout`
+    """
+    draw(G, pos=nx.shell_layout(G, nlist=nlist), **kwargs)
+
+
+def draw_planar(G, **kwargs):
+    """Draw a planar networkx graph `G` with planar layout.
+
+    This is a convenience function equivalent to::
+
+        nx.draw(G, pos=nx.planar_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+        A planar networkx graph
+
+    kwargs : optional keywords
+        See `draw_networkx` for a description of optional keywords.
+
+    Raises
+    ------
+    NetworkXException
+        When `G` is not planar
+
+    Notes
+    -----
+    The layout is computed each time this function is called.
+    For repeated drawing it is much more efficient to call
+    `~networkx.drawing.layout.planar_layout` directly and reuse the result::
+
+        >>> G = nx.path_graph(5)
+        >>> pos = nx.planar_layout(G)
+        >>> nx.draw(G, pos=pos)  # Draw the original graph
+        >>> # Draw a subgraph, reusing the same node positions
+        >>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.draw_planar(G)
+
+    See Also
+    --------
+    :func:`~networkx.drawing.layout.planar_layout`
+    """
+    draw(G, pos=nx.planar_layout(G), **kwargs)
+
+
+def draw_forceatlas2(G, **kwargs):
+    """Draw a networkx graph with forceatlas2 layout.
+
+    This is a convenience function equivalent to::
+
+       nx.draw(G, pos=nx.forceatlas2_layout(G), **kwargs)
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+
+    kwargs : optional keywords
+       See networkx.draw_networkx() for a description of optional keywords,
+       with the exception of the pos parameter which is not used by this
+       function.
+    """
+    draw(G, pos=nx.forceatlas2_layout(G), **kwargs)
+
+
+def apply_alpha(colors, alpha, elem_list, cmap=None, vmin=None, vmax=None):
+    """Apply an alpha (or list of alphas) to the colors provided.
+
+    Parameters
+    ----------
+
+    colors : color string or array of floats (default='r')
+        Color of element. Can be a single color format string,
+        or a sequence of colors with the same length as nodelist.
+        If numeric values are specified they will be mapped to
+        colors using the cmap and vmin,vmax parameters.  See
+        matplotlib.scatter for more details.
+
+    alpha : float or array of floats
+        Alpha values for elements. This can be a single alpha value, in
+        which case it will be applied to all the elements of color. Otherwise,
+        if it is an array, the elements of alpha will be applied to the colors
+        in order (cycling through alpha multiple times if necessary).
+
+    elem_list : array of networkx objects
+        The list of elements which are being colored. These could be nodes,
+        edges or labels.
+
+    cmap : matplotlib colormap
+        Color map for use if colors is a list of floats corresponding to points
+        on a color mapping.
+
+    vmin, vmax : float
+        Minimum and maximum values for normalizing colors if a colormap is used
+
+    Returns
+    -------
+
+    rgba_colors : numpy ndarray
+        Array containing RGBA format values for each of the node colours.
+
+    """
+    from itertools import cycle, islice
+
+    import matplotlib as mpl
+    import matplotlib.cm  # call as mpl.cm
+    import matplotlib.colors  # call as mpl.colors
+    import numpy as np
+
+    # If we have been provided with a list of numbers as long as elem_list,
+    # apply the color mapping.
+    if len(colors) == len(elem_list) and isinstance(colors[0], Number):
+        mapper = mpl.cm.ScalarMappable(cmap=cmap)
+        mapper.set_clim(vmin, vmax)
+        rgba_colors = mapper.to_rgba(colors)
+    # Otherwise, convert colors to matplotlib's RGB using the colorConverter
+    # object.  These are converted to numpy ndarrays to be consistent with the
+    # to_rgba method of ScalarMappable.
+    else:
+        try:
+            rgba_colors = np.array([mpl.colors.colorConverter.to_rgba(colors)])
+        except ValueError:
+            rgba_colors = np.array(
+                [mpl.colors.colorConverter.to_rgba(color) for color in colors]
+            )
+    # Set the final column of the rgba_colors to have the relevant alpha values
+    try:
+        # If alpha is longer than the number of colors, resize to the number of
+        # elements.  Also, if rgba_colors.size (the number of elements of
+        # rgba_colors) is the same as the number of elements, resize the array,
+        # to avoid it being interpreted as a colormap by scatter()
+        if len(alpha) > len(rgba_colors) or rgba_colors.size == len(elem_list):
+            rgba_colors = np.resize(rgba_colors, (len(elem_list), 4))
+            rgba_colors[1:, 0] = rgba_colors[0, 0]
+            rgba_colors[1:, 1] = rgba_colors[0, 1]
+            rgba_colors[1:, 2] = rgba_colors[0, 2]
+        rgba_colors[:, 3] = list(islice(cycle(alpha), len(rgba_colors)))
+    except TypeError:
+        rgba_colors[:, -1] = alpha
+    return rgba_colors
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/__init__.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ec027c2405b6f9de2e7b6a0f7c18d782ac8761c
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/__init__.py
@@ -0,0 +1,34 @@
+"""
+A package for generating various graphs in networkx.
+
+"""
+
+from networkx.generators.atlas import *
+from networkx.generators.classic import *
+from networkx.generators.cographs import *
+from networkx.generators.community import *
+from networkx.generators.degree_seq import *
+from networkx.generators.directed import *
+from networkx.generators.duplication import *
+from networkx.generators.ego import *
+from networkx.generators.expanders import *
+from networkx.generators.geometric import *
+from networkx.generators.harary_graph import *
+from networkx.generators.internet_as_graphs import *
+from networkx.generators.intersection import *
+from networkx.generators.interval_graph import *
+from networkx.generators.joint_degree_seq import *
+from networkx.generators.lattice import *
+from networkx.generators.line import *
+from networkx.generators.mycielski import *
+from networkx.generators.nonisomorphic_trees import *
+from networkx.generators.random_clustered import *
+from networkx.generators.random_graphs import *
+from networkx.generators.small import *
+from networkx.generators.social import *
+from networkx.generators.spectral_graph_forge import *
+from networkx.generators.stochastic import *
+from networkx.generators.sudoku import *
+from networkx.generators.time_series import *
+from networkx.generators.trees import *
+from networkx.generators.triads import *
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/atlas.dat.gz b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/atlas.dat.gz
new file mode 100644
index 0000000000000000000000000000000000000000..2cb5d1308c1168df6218cee0a97552c5787a29b8
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/atlas.dat.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73fc416df0164923607751cb759f4ae81deb5f6550bf25be59c86de3b747e41d
+size 8887
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/atlas.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/atlas.py
new file mode 100644
index 0000000000000000000000000000000000000000..000d478b89210fe65f4a9bfc1a02e35dded889f3
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/atlas.py
@@ -0,0 +1,227 @@
+"""
+Generators for the small graph atlas.
+"""
+
+import gzip
+import importlib.resources
+from itertools import islice
+
+import networkx as nx
+
+__all__ = ["graph_atlas", "graph_atlas_g"]
+
+#: The total number of graphs in the atlas.
+#:
+#: The graphs are labeled starting from 0 and extending to (but not
+#: including) this number.
+NUM_GRAPHS = 1253
+
+#: The path to the data file containing the graph edge lists.
+#:
+#: This is the absolute path of the gzipped text file containing the
+#: edge list for each graph in the atlas. The file contains one entry
+#: per graph in the atlas, in sequential order, starting from graph
+#: number 0 and extending through graph number 1252 (see
+#: :data:`NUM_GRAPHS`). Each entry looks like
+#:
+#: .. sourcecode:: text
+#:
+#:    GRAPH 6
+#:    NODES 3
+#:    0 1
+#:    0 2
+#:
+#: where the first two lines are the graph's index in the atlas and the
+#: number of nodes in the graph, and the remaining lines are the edge
+#: list.
+#:
+#: This file was generated from a Python list of graphs via code like
+#: the following::
+#:
+#:     import gzip
+#:     from networkx.generators.atlas import graph_atlas_g
+#:     from networkx.readwrite.edgelist import write_edgelist
+#:
+#:     with gzip.open('atlas.dat.gz', 'wb') as f:
+#:         for i, G in enumerate(graph_atlas_g()):
+#:             f.write(bytes(f'GRAPH {i}\n', encoding='utf-8'))
+#:             f.write(bytes(f'NODES {len(G)}\n', encoding='utf-8'))
+#:             write_edgelist(G, f, data=False)
+#:
+
+# Path to the atlas file
+ATLAS_FILE = importlib.resources.files("networkx.generators") / "atlas.dat.gz"
+
+
+def _generate_graphs():
+    """Sequentially read the file containing the edge list data for the
+    graphs in the atlas and generate the graphs one at a time.
+
+    This function reads the file given in :data:`.ATLAS_FILE`.
+
+    """
+    with gzip.open(ATLAS_FILE, "rb") as f:
+        line = f.readline()
+        while line and line.startswith(b"GRAPH"):
+            # The first two lines of each entry tell us the index of the
+            # graph in the list and the number of nodes in the graph.
+            # They look like this:
+            #
+            #     GRAPH 3
+            #     NODES 2
+            #
+            graph_index = int(line[6:].rstrip())
+            line = f.readline()
+            num_nodes = int(line[6:].rstrip())
+            # The remaining lines contain the edge list, until the next
+            # GRAPH line (or until the end of the file).
+            edgelist = []
+            line = f.readline()
+            while line and not line.startswith(b"GRAPH"):
+                edgelist.append(line.rstrip())
+                line = f.readline()
+            G = nx.Graph()
+            G.name = f"G{graph_index}"
+            G.add_nodes_from(range(num_nodes))
+            G.add_edges_from(tuple(map(int, e.split())) for e in edgelist)
+            yield G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def graph_atlas(i):
+    """Returns graph number `i` from the Graph Atlas.
+
+    For more information, see :func:`.graph_atlas_g`.
+
+    Parameters
+    ----------
+    i : int
+        The index of the graph from the atlas to get. The graph at index
+        0 is assumed to be the null graph.
+
+    Returns
+    -------
+    list
+        A list of :class:`~networkx.Graph` objects, the one at index *i*
+        corresponding to the graph *i* in the Graph Atlas.
+
+    See also
+    --------
+    graph_atlas_g
+
+    Notes
+    -----
+    The time required by this function increases linearly with the
+    argument `i`, since it reads a large file sequentially in order to
+    generate the graph [1]_.
+
+    References
+    ----------
+    .. [1] Ronald C. Read and Robin J. Wilson, *An Atlas of Graphs*.
+           Oxford University Press, 1998.
+
+    """
+    if not (0 <= i < NUM_GRAPHS):
+        raise ValueError(f"index must be between 0 and {NUM_GRAPHS}")
+    return next(islice(_generate_graphs(), i, None))
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def graph_atlas_g():
+    """Returns the list of all graphs with up to seven nodes named in the
+    Graph Atlas.
+
+    The graphs are listed in increasing order by
+
+    1. number of nodes,
+    2. number of edges,
+    3. degree sequence (for example 111223 < 112222),
+    4. number of automorphisms,
+
+    in that order, with three exceptions as described in the *Notes*
+    section below. This causes the list to correspond with the index of
+    the graphs in the Graph Atlas [atlas]_, with the first graph,
+    ``G[0]``, being the null graph.
+
+    Returns
+    -------
+    list
+        A list of :class:`~networkx.Graph` objects, the one at index *i*
+        corresponding to the graph *i* in the Graph Atlas.
+
+    Examples
+    --------
+    >>> from pprint import pprint
+    >>> atlas = nx.graph_atlas_g()
+
+    There are 1253 graphs in the atlas
+
+    >>> len(atlas)
+    1253
+
+    The number of graphs with *n* nodes, where *n* ranges from 0 to 7:
+
+    >>> from collections import Counter
+    >>> num_nodes_per_graph = [len(G) for G in atlas]
+    >>> Counter(num_nodes_per_graph)
+    Counter({7: 1044, 6: 156, 5: 34, 4: 11, 3: 4, 2: 2, 0: 1, 1: 1})
+
+    Since the atlas is ordered by the number of nodes in the graph, all graphs
+    with *n* nodes can be obtained by slicing the atlas. For example, all
+    graphs with 5 nodes:
+
+    >>> G5_list = atlas[19:53]
+    >>> all(len(G) == 5 for G in G5_list)
+    True
+
+    Or all graphs with at least 3 nodes but fewer than 7 nodes:
+
+    >>> G3_6_list = atlas[4:209]
+
+    More generally, the indices that partition the atlas by the number of nodes
+    per graph:
+
+    >>> import itertools
+    >>> partition_indices = [0] + list(
+    ...     itertools.accumulate(Counter(num_nodes_per_graph).values())  # cumsum
+    ... )
+    >>> partition_indices
+    [0, 1, 2, 4, 8, 19, 53, 209, 1253]
+    >>> partition_mapping = dict(enumerate(itertools.pairwise(partition_indices)))
+    >>> pprint(partition_mapping)
+    {0: (0, 1),
+     1: (1, 2),
+     2: (2, 4),
+     3: (4, 8),
+     4: (8, 19),
+     5: (19, 53),
+     6: (53, 209),
+     7: (209, 1253)}
+
+    See also
+    --------
+    graph_atlas
+
+    Notes
+    -----
+    This function may be expensive in both time and space, since it
+    reads a large file sequentially in order to populate the list.
+
+    Although the NetworkX atlas functions match the order of graphs
+    given in the "Atlas of Graphs" book, there are (at least) three
+    errors in the ordering described in the book. The following three
+    pairs of nodes violate the lexicographically nondecreasing sorted
+    degree sequence rule:
+
+    - graphs 55 and 56 with degree sequences 001111 and 000112,
+    - graphs 1007 and 1008 with degree sequences 3333444 and 3333336,
+    - graphs 1012 and 1213 with degree sequences 1244555 and 1244456.
+
+    References
+    ----------
+    .. [atlas] Ronald C. Read and Robin J. Wilson,
+               *An Atlas of Graphs*.
+               Oxford University Press, 1998.
+
+    """
+    return list(_generate_graphs())
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/classic.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/classic.py
new file mode 100644
index 0000000000000000000000000000000000000000..a461e7bd23cd79000b834d3362ea3ca8cde93fb2
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/classic.py
@@ -0,0 +1,1068 @@
+"""Generators for some classic graphs.
+
+The typical graph builder function is called as follows:
+
+>>> G = nx.complete_graph(100)
+
+returning the complete graph on n nodes labeled 0, .., 99
+as a simple graph. Except for `empty_graph`, all the functions
+in this module return a Graph class (i.e. a simple, undirected graph).
+
+"""
+
+import itertools
+import numbers
+
+import networkx as nx
+from networkx.classes import Graph
+from networkx.exception import NetworkXError
+from networkx.utils import nodes_or_number, pairwise
+
+__all__ = [
+    "balanced_tree",
+    "barbell_graph",
+    "binomial_tree",
+    "complete_graph",
+    "complete_multipartite_graph",
+    "circular_ladder_graph",
+    "circulant_graph",
+    "cycle_graph",
+    "dorogovtsev_goltsev_mendes_graph",
+    "empty_graph",
+    "full_rary_tree",
+    "kneser_graph",
+    "ladder_graph",
+    "lollipop_graph",
+    "null_graph",
+    "path_graph",
+    "star_graph",
+    "tadpole_graph",
+    "trivial_graph",
+    "turan_graph",
+    "wheel_graph",
+]
+
+
+# -------------------------------------------------------------------
+#   Some Classic Graphs
+# -------------------------------------------------------------------
+
+
+def _tree_edges(n, r):
+    if n == 0:
+        return
+    # helper function for trees
+    # yields edges in rooted tree at 0 with n nodes and branching ratio r
+    nodes = iter(range(n))
+    parents = [next(nodes)]  # stack of max length r
+    while parents:
+        source = parents.pop(0)
+        for i in range(r):
+            try:
+                target = next(nodes)
+                parents.append(target)
+                yield source, target
+            except StopIteration:
+                break
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def full_rary_tree(r, n, create_using=None):
+    """Creates a full r-ary tree of `n` nodes.
+
+    Sometimes called a k-ary, n-ary, or m-ary tree.
+    "... all non-leaf nodes have exactly r children and all levels
+    are full except for some rightmost position of the bottom level
+    (if a leaf at the bottom level is missing, then so are all of the
+    leaves to its right." [1]_
+
+    .. plot::
+
+        >>> nx.draw(nx.full_rary_tree(2, 10))
+
+    Parameters
+    ----------
+    r : int
+        branching factor of the tree
+    n : int
+        Number of nodes in the tree
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        An r-ary tree with n nodes
+
+    References
+    ----------
+    .. [1] An introduction to data structures and algorithms,
+           James Andrew Storer,  Birkhauser Boston 2001, (page 225).
+    """
+    G = empty_graph(n, create_using)
+    G.add_edges_from(_tree_edges(n, r))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def kneser_graph(n, k):
+    """Returns the Kneser Graph with parameters `n` and `k`.
+
+    The Kneser Graph has nodes that are k-tuples (subsets) of the integers
+    between 0 and ``n-1``. Nodes are adjacent if their corresponding sets are disjoint.
+
+    Parameters
+    ----------
+    n: int
+        Number of integers from which to make node subsets.
+        Subsets are drawn from ``set(range(n))``.
+    k: int
+        Size of the subsets.
+
+    Returns
+    -------
+    G : NetworkX Graph
+
+    Examples
+    --------
+    >>> G = nx.kneser_graph(5, 2)
+    >>> G.number_of_nodes()
+    10
+    >>> G.number_of_edges()
+    15
+    >>> nx.is_isomorphic(G, nx.petersen_graph())
+    True
+    """
+    if n <= 0:
+        raise NetworkXError("n should be greater than zero")
+    if k <= 0 or k > n:
+        raise NetworkXError("k should be greater than zero and smaller than n")
+
+    G = nx.Graph()
+    # Create all k-subsets of [0, 1, ..., n-1]
+    subsets = list(itertools.combinations(range(n), k))
+
+    if 2 * k > n:
+        G.add_nodes_from(subsets)
+
+    universe = set(range(n))
+    comb = itertools.combinations  # only to make it all fit on one line
+    G.add_edges_from((s, t) for s in subsets for t in comb(universe - set(s), k))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def balanced_tree(r, h, create_using=None):
+    """Returns the perfectly balanced `r`-ary tree of height `h`.
+
+    .. plot::
+
+        >>> nx.draw(nx.balanced_tree(2, 3))
+
+    Parameters
+    ----------
+    r : int
+        Branching factor of the tree; each node will have `r`
+        children.
+
+    h : int
+        Height of the tree.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : NetworkX graph
+        A balanced `r`-ary tree of height `h`.
+
+    Notes
+    -----
+    This is the rooted tree where all leaves are at distance `h` from
+    the root. The root has degree `r` and all other internal nodes
+    have degree `r + 1`.
+
+    Node labels are integers, starting from zero.
+
+    A balanced tree is also known as a *complete r-ary tree*.
+
+    """
+    # The number of nodes in the balanced tree is `1 + r + ... + r^h`,
+    # which is computed by using the closed-form formula for a geometric
+    # sum with ratio `r`. In the special case that `r` is 1, the number
+    # of nodes is simply `h + 1` (since the tree is actually a path
+    # graph).
+    if r == 1:
+        n = h + 1
+    else:
+        # This must be an integer if both `r` and `h` are integers. If
+        # they are not, we force integer division anyway.
+        n = (1 - r ** (h + 1)) // (1 - r)
+    return full_rary_tree(r, n, create_using=create_using)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def barbell_graph(m1, m2, create_using=None):
+    """Returns the Barbell Graph: two complete graphs connected by a path.
+
+    .. plot::
+
+        >>> nx.draw(nx.barbell_graph(4, 2))
+
+    Parameters
+    ----------
+    m1 : int
+        Size of the left and right barbells, must be greater than 2.
+
+    m2 : int
+        Length of the path connecting the barbells.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+       Only undirected Graphs are supported.
+
+    Returns
+    -------
+    G : NetworkX graph
+        A barbell graph.
+
+    Notes
+    -----
+
+
+    Two identical complete graphs $K_{m1}$ form the left and right bells,
+    and are connected by a path $P_{m2}$.
+
+    The `2*m1+m2`  nodes are numbered
+        `0, ..., m1-1` for the left barbell,
+        `m1, ..., m1+m2-1` for the path,
+        and `m1+m2, ..., 2*m1+m2-1` for the right barbell.
+
+    The 3 subgraphs are joined via the edges `(m1-1, m1)` and
+    `(m1+m2-1, m1+m2)`. If `m2=0`, this is merely two complete
+    graphs joined together.
+
+    This graph is an extremal example in David Aldous
+    and Jim Fill's e-text on Random Walks on Graphs.
+
+    """
+    if m1 < 2:
+        raise NetworkXError("Invalid graph description, m1 should be >=2")
+    if m2 < 0:
+        raise NetworkXError("Invalid graph description, m2 should be >=0")
+
+    # left barbell
+    G = complete_graph(m1, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+
+    # connecting path
+    G.add_nodes_from(range(m1, m1 + m2 - 1))
+    if m2 > 1:
+        G.add_edges_from(pairwise(range(m1, m1 + m2)))
+
+    # right barbell
+    G.add_edges_from(
+        (u, v) for u in range(m1 + m2, 2 * m1 + m2) for v in range(u + 1, 2 * m1 + m2)
+    )
+
+    # connect it up
+    G.add_edge(m1 - 1, m1)
+    if m2 > 0:
+        G.add_edge(m1 + m2 - 1, m1 + m2)
+
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def binomial_tree(n, create_using=None):
+    """Returns the Binomial Tree of order n.
+
+    The binomial tree of order 0 consists of a single node. A binomial tree of order k
+    is defined recursively by linking two binomial trees of order k-1: the root of one is
+    the leftmost child of the root of the other.
+
+    .. plot::
+
+        >>> nx.draw(nx.binomial_tree(3))
+
+    Parameters
+    ----------
+    n : int
+        Order of the binomial tree.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : NetworkX graph
+        A binomial tree of $2^n$ nodes and $2^n - 1$ edges.
+
+    """
+    G = nx.empty_graph(1, create_using)
+
+    N = 1
+    for i in range(n):
+        # Use G.edges() to ensure 2-tuples. G.edges is 3-tuple for MultiGraph
+        edges = [(u + N, v + N) for (u, v) in G.edges()]
+        G.add_edges_from(edges)
+        G.add_edge(0, N)
+        N *= 2
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number(0)
+def complete_graph(n, create_using=None):
+    """Return the complete graph `K_n` with n nodes.
+
+    A complete graph on `n` nodes means that all pairs
+    of distinct nodes have an edge connecting them.
+
+    .. plot::
+
+        >>> nx.draw(nx.complete_graph(5))
+
+    Parameters
+    ----------
+    n : int or iterable container of nodes
+        If n is an integer, nodes are from range(n).
+        If n is a container of nodes, those nodes appear in the graph.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(9)
+    >>> len(G)
+    9
+    >>> G.size()
+    36
+    >>> G = nx.complete_graph(range(11, 14))
+    >>> list(G.nodes())
+    [11, 12, 13]
+    >>> G = nx.complete_graph(4, nx.DiGraph())
+    >>> G.is_directed()
+    True
+
+    """
+    _, nodes = n
+    G = empty_graph(nodes, create_using)
+    if len(nodes) > 1:
+        if G.is_directed():
+            edges = itertools.permutations(nodes, 2)
+        else:
+            edges = itertools.combinations(nodes, 2)
+        G.add_edges_from(edges)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def circular_ladder_graph(n, create_using=None):
+    """Returns the circular ladder graph $CL_n$ of length n.
+
+    $CL_n$ consists of two concentric n-cycles in which
+    each of the n pairs of concentric nodes are joined by an edge.
+
+    Node labels are the integers 0 to n-1
+
+    .. plot::
+
+        >>> nx.draw(nx.circular_ladder_graph(5))
+
+    """
+    G = ladder_graph(n, create_using)
+    G.add_edge(0, n - 1)
+    G.add_edge(n, 2 * n - 1)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def circulant_graph(n, offsets, create_using=None):
+    r"""Returns the circulant graph $Ci_n(x_1, x_2, ..., x_m)$ with $n$ nodes.
+
+    The circulant graph $Ci_n(x_1, ..., x_m)$ consists of $n$ nodes $0, ..., n-1$
+    such that node $i$ is connected to nodes $(i + x) \mod n$ and $(i - x) \mod n$
+    for all $x$ in $x_1, ..., x_m$. Thus $Ci_n(1)$ is a cycle graph.
+
+    .. plot::
+
+        >>> nx.draw(nx.circulant_graph(10, [1]))
+
+    Parameters
+    ----------
+    n : integer
+        The number of nodes in the graph.
+    offsets : list of integers
+        A list of node offsets, $x_1$ up to $x_m$, as described above.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    NetworkX Graph of type create_using
+
+    Examples
+    --------
+    Many well-known graph families are subfamilies of the circulant graphs;
+    for example, to create the cycle graph on n points, we connect every
+    node to nodes on either side (with offset plus or minus one). For n = 10,
+
+    >>> G = nx.circulant_graph(10, [1])
+    >>> edges = [
+    ...     (0, 9),
+    ...     (0, 1),
+    ...     (1, 2),
+    ...     (2, 3),
+    ...     (3, 4),
+    ...     (4, 5),
+    ...     (5, 6),
+    ...     (6, 7),
+    ...     (7, 8),
+    ...     (8, 9),
+    ... ]
+    >>> sorted(edges) == sorted(G.edges())
+    True
+
+    Similarly, we can create the complete graph
+    on 5 points with the set of offsets [1, 2]:
+
+    >>> G = nx.circulant_graph(5, [1, 2])
+    >>> edges = [
+    ...     (0, 1),
+    ...     (0, 2),
+    ...     (0, 3),
+    ...     (0, 4),
+    ...     (1, 2),
+    ...     (1, 3),
+    ...     (1, 4),
+    ...     (2, 3),
+    ...     (2, 4),
+    ...     (3, 4),
+    ... ]
+    >>> sorted(edges) == sorted(G.edges())
+    True
+
+    """
+    G = empty_graph(n, create_using)
+    for i in range(n):
+        for j in offsets:
+            G.add_edge(i, (i - j) % n)
+            G.add_edge(i, (i + j) % n)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number(0)
+def cycle_graph(n, create_using=None):
+    """Returns the cycle graph $C_n$ of cyclically connected nodes.
+
+    $C_n$ is a path with its two end-nodes connected.
+
+    .. plot::
+
+        >>> nx.draw(nx.cycle_graph(5))
+
+    Parameters
+    ----------
+    n : int or iterable container of nodes
+        If n is an integer, nodes are from `range(n)`.
+        If n is a container of nodes, those nodes appear in the graph.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Notes
+    -----
+    If create_using is directed, the direction is in increasing order.
+
+    """
+    _, nodes = n
+    G = empty_graph(nodes, create_using)
+    G.add_edges_from(pairwise(nodes, cyclic=True))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def dorogovtsev_goltsev_mendes_graph(n, create_using=None):
+    """Returns the hierarchically constructed Dorogovtsev--Goltsev--Mendes graph.
+
+    The Dorogovtsev--Goltsev--Mendes [1]_ procedure deterministically produces a
+    scale-free graph with ``3/2 * (3**(n-1) + 1)`` nodes
+    and ``3**n`` edges for a given `n`.
+
+    Note that `n` denotes the number of times the state transition is applied,
+    starting from the base graph with ``n = 0`` (no transitions), as in [2]_.
+    This is different from the parameter ``t = n - 1`` in [1]_.
+
+    .. plot::
+
+        >>> nx.draw(nx.dorogovtsev_goltsev_mendes_graph(3))
+
+    Parameters
+    ----------
+    n : integer
+        The generation number.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. Directed graphs and multigraphs are not supported.
+
+    Returns
+    -------
+    G : NetworkX `Graph`
+
+    Raises
+    ------
+    NetworkXError
+        If `n` is less than zero.
+
+        If `create_using` is a directed graph or multigraph.
+
+    Examples
+    --------
+    >>> G = nx.dorogovtsev_goltsev_mendes_graph(3)
+    >>> G.number_of_nodes()
+    15
+    >>> G.number_of_edges()
+    27
+    >>> nx.is_planar(G)
+    True
+
+    References
+    ----------
+    .. [1] S. N. Dorogovtsev, A. V. Goltsev and J. F. F. Mendes,
+        "Pseudofractal scale-free web", Physical Review E 65, 066122, 2002.
+        https://arxiv.org/pdf/cond-mat/0112143.pdf
+    .. [2] Weisstein, Eric W. "Dorogovtsev--Goltsev--Mendes Graph".
+        From MathWorld--A Wolfram Web Resource.
+        https://mathworld.wolfram.com/Dorogovtsev-Goltsev-MendesGraph.html
+    """
+    if n < 0:
+        raise NetworkXError("n must be greater than or equal to 0")
+
+    G = empty_graph(0, create_using)
+    if G.is_directed():
+        raise NetworkXError("directed graph not supported")
+    if G.is_multigraph():
+        raise NetworkXError("multigraph not supported")
+
+    G.add_edge(0, 1)
+    new_node = 2  # next node to be added
+    for _ in range(n):  # iterate over number of generations.
+        new_edges = []
+        for u, v in G.edges():
+            new_edges.append((u, new_node))
+            new_edges.append((v, new_node))
+            new_node += 1
+
+        G.add_edges_from(new_edges)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number(0)
+def empty_graph(n=0, create_using=None, default=Graph):
+    """Returns the empty graph with n nodes and zero edges.
+
+    .. plot::
+
+        >>> nx.draw(nx.empty_graph(5))
+
+    Parameters
+    ----------
+    n : int or iterable container of nodes (default = 0)
+        If n is an integer, nodes are from `range(n)`.
+        If n is a container of nodes, those nodes appear in the graph.
+    create_using : Graph Instance, Constructor or None
+        Indicator of type of graph to return.
+        If a Graph-type instance, then clear and use it.
+        If None, use the `default` constructor.
+        If a constructor, call it to create an empty graph.
+    default : Graph constructor (optional, default = nx.Graph)
+        The constructor to use if create_using is None.
+        If None, then nx.Graph is used.
+        This is used when passing an unknown `create_using` value
+        through your home-grown function to `empty_graph` and
+        you want a default constructor other than nx.Graph.
+
+    Examples
+    --------
+    >>> G = nx.empty_graph(10)
+    >>> G.number_of_nodes()
+    10
+    >>> G.number_of_edges()
+    0
+    >>> G = nx.empty_graph("ABC")
+    >>> G.number_of_nodes()
+    3
+    >>> sorted(G)
+    ['A', 'B', 'C']
+
+    Notes
+    -----
+    The variable create_using should be a Graph Constructor or a
+    "graph"-like object. Constructors, e.g. `nx.Graph` or `nx.MultiGraph`
+    will be used to create the returned graph. "graph"-like objects
+    will be cleared (nodes and edges will be removed) and refitted as
+    an empty "graph" with nodes specified in n. This capability
+    is useful for specifying the class-nature of the resulting empty
+    "graph" (i.e. Graph, DiGraph, MyWeirdGraphClass, etc.).
+
+    The variable create_using has three main uses:
+    Firstly, the variable create_using can be used to create an
+    empty digraph, multigraph, etc.  For example,
+
+    >>> n = 10
+    >>> G = nx.empty_graph(n, create_using=nx.DiGraph)
+
+    will create an empty digraph on n nodes.
+
+    Secondly, one can pass an existing graph (digraph, multigraph,
+    etc.) via create_using. For example, if G is an existing graph
+    (resp. digraph, multigraph, etc.), then empty_graph(n, create_using=G)
+    will empty G (i.e. delete all nodes and edges using G.clear())
+    and then add n nodes and zero edges, and return the modified graph.
+
+    Thirdly, when constructing your home-grown graph creation function
+    you can use empty_graph to construct the graph by passing a user
+    defined create_using to empty_graph. In this case, if you want the
+    default constructor to be other than nx.Graph, specify `default`.
+
+    >>> def mygraph(n, create_using=None):
+    ...     G = nx.empty_graph(n, create_using, nx.MultiGraph)
+    ...     G.add_edges_from([(0, 1), (0, 1)])
+    ...     return G
+    >>> G = mygraph(3)
+    >>> G.is_multigraph()
+    True
+    >>> G = mygraph(3, nx.Graph)
+    >>> G.is_multigraph()
+    False
+
+    See also create_empty_copy(G).
+
+    """
+    if create_using is None:
+        G = default()
+    elif isinstance(create_using, type):
+        G = create_using()
+    elif not hasattr(create_using, "adj"):
+        raise TypeError("create_using is not a valid NetworkX graph type or instance")
+    else:
+        # create_using is a NetworkX style Graph
+        create_using.clear()
+        G = create_using
+
+    _, nodes = n
+    G.add_nodes_from(nodes)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def ladder_graph(n, create_using=None):
+    """Returns the Ladder graph of length n.
+
+    This is two paths of n nodes, with
+    each pair connected by a single edge.
+
+    Node labels are the integers 0 to 2*n - 1.
+
+    .. plot::
+
+        >>> nx.draw(nx.ladder_graph(5))
+
+    """
+    G = empty_graph(2 * n, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+    G.add_edges_from(pairwise(range(n)))
+    G.add_edges_from(pairwise(range(n, 2 * n)))
+    G.add_edges_from((v, v + n) for v in range(n))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number([0, 1])
+def lollipop_graph(m, n, create_using=None):
+    """Returns the Lollipop Graph; ``K_m`` connected to ``P_n``.
+
+    This is the Barbell Graph without the right barbell.
+
+    .. plot::
+
+        >>> nx.draw(nx.lollipop_graph(3, 4))
+
+    Parameters
+    ----------
+    m, n : int or iterable container of nodes
+        If an integer, nodes are from ``range(m)`` and ``range(m, m+n)``.
+        If a container of nodes, those nodes appear in the graph.
+        Warning: `m` and `n` are not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+
+        The nodes for `m` appear in the complete graph $K_m$ and the nodes
+        for `n` appear in the path $P_n$
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    Networkx graph
+       A complete graph with `m` nodes connected to a path of length `n`.
+
+    Notes
+    -----
+    The 2 subgraphs are joined via an edge ``(m-1, m)``.
+    If ``n=0``, this is merely a complete graph.
+
+    (This graph is an extremal example in David Aldous and Jim
+    Fill's etext on Random Walks on Graphs.)
+
+    """
+    m, m_nodes = m
+    M = len(m_nodes)
+    if M < 2:
+        raise NetworkXError("Invalid description: m should indicate at least 2 nodes")
+
+    n, n_nodes = n
+    if isinstance(m, numbers.Integral) and isinstance(n, numbers.Integral):
+        n_nodes = list(range(M, M + n))
+    N = len(n_nodes)
+
+    # the ball
+    G = complete_graph(m_nodes, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+
+    # the stick
+    G.add_nodes_from(n_nodes)
+    if N > 1:
+        G.add_edges_from(pairwise(n_nodes))
+
+    if len(G) != M + N:
+        raise NetworkXError("Nodes must be distinct in containers m and n")
+
+    # connect ball to stick
+    if M > 0 and N > 0:
+        G.add_edge(m_nodes[-1], n_nodes[0])
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def null_graph(create_using=None):
+    """Returns the Null graph with no nodes or edges.
+
+    See empty_graph for the use of create_using.
+
+    """
+    G = empty_graph(0, create_using)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number(0)
+def path_graph(n, create_using=None):
+    """Returns the Path graph `P_n` of linearly connected nodes.
+
+    .. plot::
+
+        >>> nx.draw(nx.path_graph(5))
+
+    Parameters
+    ----------
+    n : int or iterable
+        If an integer, nodes are 0 to n - 1.
+        If an iterable of nodes, in the order they appear in the path.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    """
+    _, nodes = n
+    G = empty_graph(nodes, create_using)
+    G.add_edges_from(pairwise(nodes))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number(0)
+def star_graph(n, create_using=None):
+    """Return the star graph
+
+    The star graph consists of one center node connected to n outer nodes.
+
+    .. plot::
+
+        >>> nx.draw(nx.star_graph(6))
+
+    Parameters
+    ----------
+    n : int or iterable
+        If an integer, node labels are 0 to n with center 0.
+        If an iterable of nodes, the center is the first.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Notes
+    -----
+    The graph has n+1 nodes for integer n.
+    So star_graph(3) is the same as star_graph(range(4)).
+    """
+    n, nodes = n
+    if isinstance(n, numbers.Integral):
+        nodes.append(int(n))  # there should be n+1 nodes
+    G = empty_graph(nodes, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+
+    if len(nodes) > 1:
+        hub, *spokes = nodes
+        G.add_edges_from((hub, node) for node in spokes)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number([0, 1])
+def tadpole_graph(m, n, create_using=None):
+    """Returns the (m,n)-tadpole graph; ``C_m`` connected to ``P_n``.
+
+    This graph on m+n nodes connects a cycle of size `m` to a path of length `n`.
+    It looks like a tadpole. It is also called a kite graph or a dragon graph.
+
+    .. plot::
+
+        >>> nx.draw(nx.tadpole_graph(3, 5))
+
+    Parameters
+    ----------
+    m, n : int or iterable container of nodes
+        If an integer, nodes are from ``range(m)`` and ``range(m,m+n)``.
+        If a container of nodes, those nodes appear in the graph.
+        Warning: `m` and `n` are not checked for duplicates and if present the
+        resulting graph may not be as desired.
+
+        The nodes for `m` appear in the cycle graph $C_m$ and the nodes
+        for `n` appear in the path $P_n$.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    Networkx graph
+       A cycle of size `m` connected to a path of length `n`.
+
+    Raises
+    ------
+    NetworkXError
+        If ``m < 2``. The tadpole graph is undefined for ``m<2``.
+
+    Notes
+    -----
+    The 2 subgraphs are joined via an edge ``(m-1, m)``.
+    If ``n=0``, this is a cycle graph.
+    `m` and/or `n` can be a container of nodes instead of an integer.
+
+    """
+    m, m_nodes = m
+    M = len(m_nodes)
+    if M < 2:
+        raise NetworkXError("Invalid description: m should indicate at least 2 nodes")
+
+    n, n_nodes = n
+    if isinstance(m, numbers.Integral) and isinstance(n, numbers.Integral):
+        n_nodes = list(range(M, M + n))
+
+    # the circle
+    G = cycle_graph(m_nodes, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+
+    # the stick
+    nx.add_path(G, [m_nodes[-1]] + list(n_nodes))
+
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def trivial_graph(create_using=None):
+    """Return the Trivial graph with one node (with label 0) and no edges.
+
+    .. plot::
+
+        >>> nx.draw(nx.trivial_graph(), with_labels=True)
+
+    """
+    G = empty_graph(1, create_using)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def turan_graph(n, r):
+    r"""Return the Turan Graph
+
+    The Turan Graph is a complete multipartite graph on $n$ nodes
+    with $r$ disjoint subsets. That is, edges connect each node to
+    every node not in its subset.
+
+    Given $n$ and $r$, we create a complete multipartite graph with
+    $r-(n \mod r)$ partitions of size $n/r$, rounded down, and
+    $n \mod r$ partitions of size $n/r+1$, rounded down.
+
+    .. plot::
+
+        >>> nx.draw(nx.turan_graph(6, 2))
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    r : int
+        The number of partitions.
+        Must be less than or equal to n.
+
+    Notes
+    -----
+    Must satisfy $1 <= r <= n$.
+    The graph has $(r-1)(n^2)/(2r)$ edges, rounded down.
+    """
+
+    if not 1 <= r <= n:
+        raise NetworkXError("Must satisfy 1 <= r <= n")
+
+    partitions = [n // r] * (r - (n % r)) + [n // r + 1] * (n % r)
+    G = complete_multipartite_graph(*partitions)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number(0)
+def wheel_graph(n, create_using=None):
+    """Return the wheel graph
+
+    The wheel graph consists of a hub node connected to a cycle of (n-1) nodes.
+
+    .. plot::
+
+        >>> nx.draw(nx.wheel_graph(5))
+
+    Parameters
+    ----------
+    n : int or iterable
+        If an integer, node labels are 0 to n with center 0.
+        If an iterable of nodes, the center is the first.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Node labels are the integers 0 to n - 1.
+    """
+    _, nodes = n
+    G = empty_graph(nodes, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+
+    if len(nodes) > 1:
+        hub, *rim = nodes
+        G.add_edges_from((hub, node) for node in rim)
+        if len(rim) > 1:
+            G.add_edges_from(pairwise(rim, cyclic=True))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def complete_multipartite_graph(*subset_sizes):
+    """Returns the complete multipartite graph with the specified subset sizes.
+
+    .. plot::
+
+        >>> nx.draw(nx.complete_multipartite_graph(1, 2, 3))
+
+    Parameters
+    ----------
+    subset_sizes : tuple of integers or tuple of node iterables
+       The arguments can either all be integer number of nodes or they
+       can all be iterables of nodes. If integers, they represent the
+       number of nodes in each subset of the multipartite graph.
+       If iterables, each is used to create the nodes for that subset.
+       The length of subset_sizes is the number of subsets.
+
+    Returns
+    -------
+    G : NetworkX Graph
+       Returns the complete multipartite graph with the specified subsets.
+
+       For each node, the node attribute 'subset' is an integer
+       indicating which subset contains the node.
+
+    Examples
+    --------
+    Creating a complete tripartite graph, with subsets of one, two, and three
+    nodes, respectively.
+
+        >>> G = nx.complete_multipartite_graph(1, 2, 3)
+        >>> [G.nodes[u]["subset"] for u in G]
+        [0, 1, 1, 2, 2, 2]
+        >>> list(G.edges(0))
+        [(0, 1), (0, 2), (0, 3), (0, 4), (0, 5)]
+        >>> list(G.edges(2))
+        [(2, 0), (2, 3), (2, 4), (2, 5)]
+        >>> list(G.edges(4))
+        [(4, 0), (4, 1), (4, 2)]
+
+        >>> G = nx.complete_multipartite_graph("a", "bc", "def")
+        >>> [G.nodes[u]["subset"] for u in sorted(G)]
+        [0, 1, 1, 2, 2, 2]
+
+    Notes
+    -----
+    This function generalizes several other graph builder functions.
+
+    - If no subset sizes are given, this returns the null graph.
+    - If a single subset size `n` is given, this returns the empty graph on
+      `n` nodes.
+    - If two subset sizes `m` and `n` are given, this returns the complete
+      bipartite graph on `m + n` nodes.
+    - If subset sizes `1` and `n` are given, this returns the star graph on
+      `n + 1` nodes.
+
+    See also
+    --------
+    complete_bipartite_graph
+    """
+    # The complete multipartite graph is an undirected simple graph.
+    G = Graph()
+
+    if len(subset_sizes) == 0:
+        return G
+
+    # set up subsets of nodes
+    try:
+        extents = pairwise(itertools.accumulate((0,) + subset_sizes))
+        subsets = [range(start, end) for start, end in extents]
+    except TypeError:
+        subsets = subset_sizes
+    else:
+        if any(size < 0 for size in subset_sizes):
+            raise NetworkXError(f"Negative number of nodes not valid: {subset_sizes}")
+
+    # add nodes with subset attribute
+    # while checking that ints are not mixed with iterables
+    try:
+        for i, subset in enumerate(subsets):
+            G.add_nodes_from(subset, subset=i)
+    except TypeError as err:
+        raise NetworkXError("Arguments must be all ints or all iterables") from err
+
+    # Across subsets, all nodes should be adjacent.
+    # We can use itertools.combinations() because undirected.
+    for subset1, subset2 in itertools.combinations(subsets, 2):
+        G.add_edges_from(itertools.product(subset1, subset2))
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/cographs.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/cographs.py
new file mode 100644
index 0000000000000000000000000000000000000000..6635b32f691696c1b6f309ad0da81c3cbc43bed9
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/cographs.py
@@ -0,0 +1,68 @@
+r"""Generators for cographs
+
+A cograph is a graph containing no path on four vertices.
+Cographs or $P_4$-free graphs can be obtained from a single vertex
+by disjoint union and complementation operations.
+
+References
+----------
+.. [0] D.G. Corneil, H. Lerchs, L.Stewart Burlingham,
+    "Complement reducible graphs",
+    Discrete Applied Mathematics, Volume 3, Issue 3, 1981, Pages 163-174,
+    ISSN 0166-218X.
+"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["random_cograph"]
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_cograph(n, seed=None):
+    r"""Returns a random cograph with $2 ^ n$ nodes.
+
+    A cograph is a graph containing no path on four vertices.
+    Cographs or $P_4$-free graphs can be obtained from a single vertex
+    by disjoint union and complementation operations.
+
+    This generator starts off from a single vertex and performs disjoint
+    union and full join operations on itself.
+    The decision on which operation will take place is random.
+
+    Parameters
+    ----------
+    n : int
+        The order of the cograph.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : A random graph containing no path on four vertices.
+
+    See Also
+    --------
+    full_join
+    union
+
+    References
+    ----------
+    .. [1] D.G. Corneil, H. Lerchs, L.Stewart Burlingham,
+       "Complement reducible graphs",
+       Discrete Applied Mathematics, Volume 3, Issue 3, 1981, Pages 163-174,
+       ISSN 0166-218X.
+    """
+    R = nx.empty_graph(1)
+
+    for i in range(n):
+        RR = nx.relabel_nodes(R.copy(), lambda x: x + len(R))
+
+        if seed.randint(0, 1) == 0:
+            R = nx.full_join(R, RR)
+        else:
+            R = nx.disjoint_union(R, RR)
+
+    return R
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/community.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/community.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7f2294c5cf9137dc1fab2a50d7ffcd6c59b6dec
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/community.py
@@ -0,0 +1,1070 @@
+"""Generators for classes of graphs used in studying social networks."""
+
+import itertools
+import math
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "caveman_graph",
+    "connected_caveman_graph",
+    "relaxed_caveman_graph",
+    "random_partition_graph",
+    "planted_partition_graph",
+    "gaussian_random_partition_graph",
+    "ring_of_cliques",
+    "windmill_graph",
+    "stochastic_block_model",
+    "LFR_benchmark_graph",
+]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def caveman_graph(l, k):
+    """Returns a caveman graph of `l` cliques of size `k`.
+
+    Parameters
+    ----------
+    l : int
+      Number of cliques
+    k : int
+      Size of cliques
+
+    Returns
+    -------
+    G : NetworkX Graph
+      caveman graph
+
+    Notes
+    -----
+    This returns an undirected graph, it can be converted to a directed
+    graph using :func:`nx.to_directed`, or a multigraph using
+    ``nx.MultiGraph(nx.caveman_graph(l, k))``. Only the undirected version is
+    described in [1]_ and it is unclear which of the directed
+    generalizations is most useful.
+
+    Examples
+    --------
+    >>> G = nx.caveman_graph(3, 3)
+
+    See also
+    --------
+
+    connected_caveman_graph
+
+    References
+    ----------
+    .. [1] Watts, D. J. 'Networks, Dynamics, and the Small-World Phenomenon.'
+       Amer. J. Soc. 105, 493-527, 1999.
+    """
+    # l disjoint cliques of size k
+    G = nx.empty_graph(l * k)
+    if k > 1:
+        for start in range(0, l * k, k):
+            edges = itertools.combinations(range(start, start + k), 2)
+            G.add_edges_from(edges)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def connected_caveman_graph(l, k):
+    """Returns a connected caveman graph of `l` cliques of size `k`.
+
+    The connected caveman graph is formed by creating `n` cliques of size
+    `k`, then a single edge in each clique is rewired to a node in an
+    adjacent clique.
+
+    Parameters
+    ----------
+    l : int
+      number of cliques
+    k : int
+      size of cliques (k at least 2 or NetworkXError is raised)
+
+    Returns
+    -------
+    G : NetworkX Graph
+      connected caveman graph
+
+    Raises
+    ------
+    NetworkXError
+        If the size of cliques `k` is smaller than 2.
+
+    Notes
+    -----
+    This returns an undirected graph, it can be converted to a directed
+    graph using :func:`nx.to_directed`, or a multigraph using
+    ``nx.MultiGraph(nx.caveman_graph(l, k))``. Only the undirected version is
+    described in [1]_ and it is unclear which of the directed
+    generalizations is most useful.
+
+    Examples
+    --------
+    >>> G = nx.connected_caveman_graph(3, 3)
+
+    References
+    ----------
+    .. [1] Watts, D. J. 'Networks, Dynamics, and the Small-World Phenomenon.'
+       Amer. J. Soc. 105, 493-527, 1999.
+    """
+    if k < 2:
+        raise nx.NetworkXError(
+            "The size of cliques in a connected caveman graph must be at least 2."
+        )
+
+    G = nx.caveman_graph(l, k)
+    for start in range(0, l * k, k):
+        G.remove_edge(start, start + 1)
+        G.add_edge(start, (start - 1) % (l * k))
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def relaxed_caveman_graph(l, k, p, seed=None):
+    """Returns a relaxed caveman graph.
+
+    A relaxed caveman graph starts with `l` cliques of size `k`.  Edges are
+    then randomly rewired with probability `p` to link different cliques.
+
+    Parameters
+    ----------
+    l : int
+      Number of groups
+    k : int
+      Size of cliques
+    p : float
+      Probability of rewiring each edge.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : NetworkX Graph
+      Relaxed Caveman Graph
+
+    Raises
+    ------
+    NetworkXError
+     If p is not in [0,1]
+
+    Examples
+    --------
+    >>> G = nx.relaxed_caveman_graph(2, 3, 0.1, seed=42)
+
+    References
+    ----------
+    .. [1] Santo Fortunato, Community Detection in Graphs,
+       Physics Reports Volume 486, Issues 3-5, February 2010, Pages 75-174.
+       https://arxiv.org/abs/0906.0612
+    """
+    G = nx.caveman_graph(l, k)
+    nodes = list(G)
+    for u, v in G.edges():
+        if seed.random() < p:  # rewire the edge
+            x = seed.choice(nodes)
+            if G.has_edge(u, x):
+                continue
+            G.remove_edge(u, v)
+            G.add_edge(u, x)
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_partition_graph(sizes, p_in, p_out, seed=None, directed=False):
+    """Returns the random partition graph with a partition of sizes.
+
+    A partition graph is a graph of communities with sizes defined by
+    s in sizes. Nodes in the same group are connected with probability
+    p_in and nodes of different groups are connected with probability
+    p_out.
+
+    Parameters
+    ----------
+    sizes : list of ints
+      Sizes of groups
+    p_in : float
+      probability of edges with in groups
+    p_out : float
+      probability of edges between groups
+    directed : boolean optional, default=False
+      Whether to create a directed graph
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : NetworkX Graph or DiGraph
+      random partition graph of size sum(gs)
+
+    Raises
+    ------
+    NetworkXError
+      If p_in or p_out is not in [0,1]
+
+    Examples
+    --------
+    >>> G = nx.random_partition_graph([10, 10, 10], 0.25, 0.01)
+    >>> len(G)
+    30
+    >>> partition = G.graph["partition"]
+    >>> len(partition)
+    3
+
+    Notes
+    -----
+    This is a generalization of the planted-l-partition described in
+    [1]_.  It allows for the creation of groups of any size.
+
+    The partition is store as a graph attribute 'partition'.
+
+    References
+    ----------
+    .. [1] Santo Fortunato 'Community Detection in Graphs' Physical Reports
+       Volume 486, Issue 3-5 p. 75-174. https://arxiv.org/abs/0906.0612
+    """
+    # Use geometric method for O(n+m) complexity algorithm
+    # partition = nx.community_sets(nx.get_node_attributes(G, 'affiliation'))
+    if not 0.0 <= p_in <= 1.0:
+        raise nx.NetworkXError("p_in must be in [0,1]")
+    if not 0.0 <= p_out <= 1.0:
+        raise nx.NetworkXError("p_out must be in [0,1]")
+
+    # create connection matrix
+    num_blocks = len(sizes)
+    p = [[p_out for s in range(num_blocks)] for r in range(num_blocks)]
+    for r in range(num_blocks):
+        p[r][r] = p_in
+
+    return stochastic_block_model(
+        sizes,
+        p,
+        nodelist=None,
+        seed=seed,
+        directed=directed,
+        selfloops=False,
+        sparse=True,
+    )
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def planted_partition_graph(l, k, p_in, p_out, seed=None, directed=False):
+    """Returns the planted l-partition graph.
+
+    This model partitions a graph with n=l*k vertices in
+    l groups with k vertices each. Vertices of the same
+    group are linked with a probability p_in, and vertices
+    of different groups are linked with probability p_out.
+
+    Parameters
+    ----------
+    l : int
+      Number of groups
+    k : int
+      Number of vertices in each group
+    p_in : float
+      probability of connecting vertices within a group
+    p_out : float
+      probability of connected vertices between groups
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : bool,optional (default=False)
+      If True return a directed graph
+
+    Returns
+    -------
+    G : NetworkX Graph or DiGraph
+      planted l-partition graph
+
+    Raises
+    ------
+    NetworkXError
+      If `p_in`, `p_out` are not in `[0, 1]`
+
+    Examples
+    --------
+    >>> G = nx.planted_partition_graph(4, 3, 0.5, 0.1, seed=42)
+
+    See Also
+    --------
+    random_partition_model
+
+    References
+    ----------
+    .. [1] A. Condon, R.M. Karp, Algorithms for graph partitioning
+        on the planted partition model,
+        Random Struct. Algor. 18 (2001) 116-140.
+
+    .. [2] Santo Fortunato 'Community Detection in Graphs' Physical Reports
+       Volume 486, Issue 3-5 p. 75-174. https://arxiv.org/abs/0906.0612
+    """
+    return random_partition_graph([k] * l, p_in, p_out, seed=seed, directed=directed)
+
+
+@py_random_state(6)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gaussian_random_partition_graph(n, s, v, p_in, p_out, directed=False, seed=None):
+    """Generate a Gaussian random partition graph.
+
+    A Gaussian random partition graph is created by creating k partitions
+    each with a size drawn from a normal distribution with mean s and variance
+    s/v. Nodes are connected within clusters with probability p_in and
+    between clusters with probability p_out[1]
+
+    Parameters
+    ----------
+    n : int
+      Number of nodes in the graph
+    s : float
+      Mean cluster size
+    v : float
+      Shape parameter. The variance of cluster size distribution is s/v.
+    p_in : float
+      Probability of intra cluster connection.
+    p_out : float
+      Probability of inter cluster connection.
+    directed : boolean, optional default=False
+      Whether to create a directed graph or not
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : NetworkX Graph or DiGraph
+      gaussian random partition graph
+
+    Raises
+    ------
+    NetworkXError
+      If s is > n
+      If p_in or p_out is not in [0,1]
+
+    Notes
+    -----
+    Note the number of partitions is dependent on s,v and n, and that the
+    last partition may be considerably smaller, as it is sized to simply
+    fill out the nodes [1]
+
+    See Also
+    --------
+    random_partition_graph
+
+    Examples
+    --------
+    >>> G = nx.gaussian_random_partition_graph(100, 10, 10, 0.25, 0.1)
+    >>> len(G)
+    100
+
+    References
+    ----------
+    .. [1] Ulrik Brandes, Marco Gaertler, Dorothea Wagner,
+       Experiments on Graph Clustering Algorithms,
+       In the proceedings of the 11th Europ. Symp. Algorithms, 2003.
+    """
+    if s > n:
+        raise nx.NetworkXError("s must be <= n")
+    assigned = 0
+    sizes = []
+    while True:
+        size = int(seed.gauss(s, s / v + 0.5))
+        if size < 1:  # how to handle 0 or negative sizes?
+            continue
+        if assigned + size >= n:
+            sizes.append(n - assigned)
+            break
+        assigned += size
+        sizes.append(size)
+    return random_partition_graph(sizes, p_in, p_out, seed=seed, directed=directed)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def ring_of_cliques(num_cliques, clique_size):
+    """Defines a "ring of cliques" graph.
+
+    A ring of cliques graph is consisting of cliques, connected through single
+    links. Each clique is a complete graph.
+
+    Parameters
+    ----------
+    num_cliques : int
+        Number of cliques
+    clique_size : int
+        Size of cliques
+
+    Returns
+    -------
+    G : NetworkX Graph
+        ring of cliques graph
+
+    Raises
+    ------
+    NetworkXError
+        If the number of cliques is lower than 2 or
+        if the size of cliques is smaller than 2.
+
+    Examples
+    --------
+    >>> G = nx.ring_of_cliques(8, 4)
+
+    See Also
+    --------
+    connected_caveman_graph
+
+    Notes
+    -----
+    The `connected_caveman_graph` graph removes a link from each clique to
+    connect it with the next clique. Instead, the `ring_of_cliques` graph
+    simply adds the link without removing any link from the cliques.
+    """
+    if num_cliques < 2:
+        raise nx.NetworkXError("A ring of cliques must have at least two cliques")
+    if clique_size < 2:
+        raise nx.NetworkXError("The cliques must have at least two nodes")
+
+    G = nx.Graph()
+    for i in range(num_cliques):
+        edges = itertools.combinations(
+            range(i * clique_size, i * clique_size + clique_size), 2
+        )
+        G.add_edges_from(edges)
+        G.add_edge(
+            i * clique_size + 1, (i + 1) * clique_size % (num_cliques * clique_size)
+        )
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def windmill_graph(n, k):
+    """Generate a windmill graph.
+    A windmill graph is a graph of `n` cliques each of size `k` that are all
+    joined at one node.
+    It can be thought of as taking a disjoint union of `n` cliques of size `k`,
+    selecting one point from each, and contracting all of the selected points.
+    Alternatively, one could generate `n` cliques of size `k-1` and one node
+    that is connected to all other nodes in the graph.
+
+    Parameters
+    ----------
+    n : int
+        Number of cliques
+    k : int
+        Size of cliques
+
+    Returns
+    -------
+    G : NetworkX Graph
+        windmill graph with n cliques of size k
+
+    Raises
+    ------
+    NetworkXError
+        If the number of cliques is less than two
+        If the size of the cliques are less than two
+
+    Examples
+    --------
+    >>> G = nx.windmill_graph(4, 5)
+
+    Notes
+    -----
+    The node labeled `0` will be the node connected to all other nodes.
+    Note that windmill graphs are usually denoted `Wd(k,n)`, so the parameters
+    are in the opposite order as the parameters of this method.
+    """
+    if n < 2:
+        msg = "A windmill graph must have at least two cliques"
+        raise nx.NetworkXError(msg)
+    if k < 2:
+        raise nx.NetworkXError("The cliques must have at least two nodes")
+
+    G = nx.disjoint_union_all(
+        itertools.chain(
+            [nx.complete_graph(k)], (nx.complete_graph(k - 1) for _ in range(n - 1))
+        )
+    )
+    G.add_edges_from((0, i) for i in range(k, G.number_of_nodes()))
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def stochastic_block_model(
+    sizes, p, nodelist=None, seed=None, directed=False, selfloops=False, sparse=True
+):
+    """Returns a stochastic block model graph.
+
+    This model partitions the nodes in blocks of arbitrary sizes, and places
+    edges between pairs of nodes independently, with a probability that depends
+    on the blocks.
+
+    Parameters
+    ----------
+    sizes : list of ints
+        Sizes of blocks
+    p : list of list of floats
+        Element (r,s) gives the density of edges going from the nodes
+        of group r to nodes of group s.
+        p must match the number of groups (len(sizes) == len(p)),
+        and it must be symmetric if the graph is undirected.
+    nodelist : list, optional
+        The block tags are assigned according to the node identifiers
+        in nodelist. If nodelist is None, then the ordering is the
+        range [0,sum(sizes)-1].
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : boolean optional, default=False
+        Whether to create a directed graph or not.
+    selfloops : boolean optional, default=False
+        Whether to include self-loops or not.
+    sparse: boolean optional, default=True
+        Use the sparse heuristic to speed up the generator.
+
+    Returns
+    -------
+    g : NetworkX Graph or DiGraph
+        Stochastic block model graph of size sum(sizes)
+
+    Raises
+    ------
+    NetworkXError
+      If probabilities are not in [0,1].
+      If the probability matrix is not square (directed case).
+      If the probability matrix is not symmetric (undirected case).
+      If the sizes list does not match nodelist or the probability matrix.
+      If nodelist contains duplicate.
+
+    Examples
+    --------
+    >>> sizes = [75, 75, 300]
+    >>> probs = [[0.25, 0.05, 0.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]]
+    >>> g = nx.stochastic_block_model(sizes, probs, seed=0)
+    >>> len(g)
+    450
+    >>> H = nx.quotient_graph(g, g.graph["partition"], relabel=True)
+    >>> for v in H.nodes(data=True):
+    ...     print(round(v[1]["density"], 3))
+    0.245
+    0.348
+    0.405
+    >>> for v in H.edges(data=True):
+    ...     print(round(1.0 * v[2]["weight"] / (sizes[v[0]] * sizes[v[1]]), 3))
+    0.051
+    0.022
+    0.07
+
+    See Also
+    --------
+    random_partition_graph
+    planted_partition_graph
+    gaussian_random_partition_graph
+    gnp_random_graph
+
+    References
+    ----------
+    .. [1] Holland, P. W., Laskey, K. B., & Leinhardt, S.,
+           "Stochastic blockmodels: First steps",
+           Social networks, 5(2), 109-137, 1983.
+    """
+    # Check if dimensions match
+    if len(sizes) != len(p):
+        raise nx.NetworkXException("'sizes' and 'p' do not match.")
+    # Check for probability symmetry (undirected) and shape (directed)
+    for row in p:
+        if len(p) != len(row):
+            raise nx.NetworkXException("'p' must be a square matrix.")
+    if not directed:
+        p_transpose = [list(i) for i in zip(*p)]
+        for i in zip(p, p_transpose):
+            for j in zip(i[0], i[1]):
+                if abs(j[0] - j[1]) > 1e-08:
+                    raise nx.NetworkXException("'p' must be symmetric.")
+    # Check for probability range
+    for row in p:
+        for prob in row:
+            if prob < 0 or prob > 1:
+                raise nx.NetworkXException("Entries of 'p' not in [0,1].")
+    # Check for nodelist consistency
+    if nodelist is not None:
+        if len(nodelist) != sum(sizes):
+            raise nx.NetworkXException("'nodelist' and 'sizes' do not match.")
+        if len(nodelist) != len(set(nodelist)):
+            raise nx.NetworkXException("nodelist contains duplicate.")
+    else:
+        nodelist = range(sum(sizes))
+
+    # Setup the graph conditionally to the directed switch.
+    block_range = range(len(sizes))
+    if directed:
+        g = nx.DiGraph()
+        block_iter = itertools.product(block_range, block_range)
+    else:
+        g = nx.Graph()
+        block_iter = itertools.combinations_with_replacement(block_range, 2)
+    # Split nodelist in a partition (list of sets).
+    size_cumsum = [sum(sizes[0:x]) for x in range(len(sizes) + 1)]
+    g.graph["partition"] = [
+        set(nodelist[size_cumsum[x] : size_cumsum[x + 1]])
+        for x in range(len(size_cumsum) - 1)
+    ]
+    # Setup nodes and graph name
+    for block_id, nodes in enumerate(g.graph["partition"]):
+        for node in nodes:
+            g.add_node(node, block=block_id)
+
+    g.name = "stochastic_block_model"
+
+    # Test for edge existence
+    parts = g.graph["partition"]
+    for i, j in block_iter:
+        if i == j:
+            if directed:
+                if selfloops:
+                    edges = itertools.product(parts[i], parts[i])
+                else:
+                    edges = itertools.permutations(parts[i], 2)
+            else:
+                edges = itertools.combinations(parts[i], 2)
+                if selfloops:
+                    edges = itertools.chain(edges, zip(parts[i], parts[i]))
+            for e in edges:
+                if seed.random() < p[i][j]:
+                    g.add_edge(*e)
+        else:
+            edges = itertools.product(parts[i], parts[j])
+        if sparse:
+            if p[i][j] == 1:  # Test edges cases p_ij = 0 or 1
+                for e in edges:
+                    g.add_edge(*e)
+            elif p[i][j] > 0:
+                while True:
+                    try:
+                        logrand = math.log(seed.random())
+                        skip = math.floor(logrand / math.log(1 - p[i][j]))
+                        # consume "skip" edges
+                        next(itertools.islice(edges, skip, skip), None)
+                        e = next(edges)
+                        g.add_edge(*e)  # __safe
+                    except StopIteration:
+                        break
+        else:
+            for e in edges:
+                if seed.random() < p[i][j]:
+                    g.add_edge(*e)  # __safe
+    return g
+
+
+def _zipf_rv_below(gamma, xmin, threshold, seed):
+    """Returns a random value chosen from the bounded Zipf distribution.
+
+    Repeatedly draws values from the Zipf distribution until the
+    threshold is met, then returns that value.
+    """
+    result = nx.utils.zipf_rv(gamma, xmin, seed)
+    while result > threshold:
+        result = nx.utils.zipf_rv(gamma, xmin, seed)
+    return result
+
+
+def _powerlaw_sequence(gamma, low, high, condition, length, max_iters, seed):
+    """Returns a list of numbers obeying a constrained power law distribution.
+
+    ``gamma`` and ``low`` are the parameters for the Zipf distribution.
+
+    ``high`` is the maximum allowed value for values draw from the Zipf
+    distribution. For more information, see :func:`_zipf_rv_below`.
+
+    ``condition`` and ``length`` are Boolean-valued functions on
+    lists. While generating the list, random values are drawn and
+    appended to the list until ``length`` is satisfied by the created
+    list. Once ``condition`` is satisfied, the sequence generated in
+    this way is returned.
+
+    ``max_iters`` indicates the number of times to generate a list
+    satisfying ``length``. If the number of iterations exceeds this
+    value, :exc:`~networkx.exception.ExceededMaxIterations` is raised.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    """
+    for i in range(max_iters):
+        seq = []
+        while not length(seq):
+            seq.append(_zipf_rv_below(gamma, low, high, seed))
+        if condition(seq):
+            return seq
+    raise nx.ExceededMaxIterations("Could not create power law sequence")
+
+
+def _hurwitz_zeta(x, q, tolerance):
+    """The Hurwitz zeta function, or the Riemann zeta function of two arguments.
+
+    ``x`` must be greater than one and ``q`` must be positive.
+
+    This function repeatedly computes subsequent partial sums until
+    convergence, as decided by ``tolerance``.
+    """
+    z = 0
+    z_prev = -float("inf")
+    k = 0
+    while abs(z - z_prev) > tolerance:
+        z_prev = z
+        z += 1 / ((k + q) ** x)
+        k += 1
+    return z
+
+
+def _generate_min_degree(gamma, average_degree, max_degree, tolerance, max_iters):
+    """Returns a minimum degree from the given average degree."""
+    # Defines zeta function whether or not Scipy is available
+    try:
+        from scipy.special import zeta
+    except ImportError:
+
+        def zeta(x, q):
+            return _hurwitz_zeta(x, q, tolerance)
+
+    min_deg_top = max_degree
+    min_deg_bot = 1
+    min_deg_mid = (min_deg_top - min_deg_bot) / 2 + min_deg_bot
+    itrs = 0
+    mid_avg_deg = 0
+    while abs(mid_avg_deg - average_degree) > tolerance:
+        if itrs > max_iters:
+            raise nx.ExceededMaxIterations("Could not match average_degree")
+        mid_avg_deg = 0
+        for x in range(int(min_deg_mid), max_degree + 1):
+            mid_avg_deg += (x ** (-gamma + 1)) / zeta(gamma, min_deg_mid)
+        if mid_avg_deg > average_degree:
+            min_deg_top = min_deg_mid
+            min_deg_mid = (min_deg_top - min_deg_bot) / 2 + min_deg_bot
+        else:
+            min_deg_bot = min_deg_mid
+            min_deg_mid = (min_deg_top - min_deg_bot) / 2 + min_deg_bot
+        itrs += 1
+    # return int(min_deg_mid + 0.5)
+    return round(min_deg_mid)
+
+
+def _generate_communities(degree_seq, community_sizes, mu, max_iters, seed):
+    """Returns a list of sets, each of which represents a community.
+
+    ``degree_seq`` is the degree sequence that must be met by the
+    graph.
+
+    ``community_sizes`` is the community size distribution that must be
+    met by the generated list of sets.
+
+    ``mu`` is a float in the interval [0, 1] indicating the fraction of
+    intra-community edges incident to each node.
+
+    ``max_iters`` is the number of times to try to add a node to a
+    community. This must be greater than the length of
+    ``degree_seq``, otherwise this function will always fail. If
+    the number of iterations exceeds this value,
+    :exc:`~networkx.exception.ExceededMaxIterations` is raised.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    The communities returned by this are sets of integers in the set {0,
+    ..., *n* - 1}, where *n* is the length of ``degree_seq``.
+
+    """
+    # This assumes the nodes in the graph will be natural numbers.
+    result = [set() for _ in community_sizes]
+    n = len(degree_seq)
+    free = list(range(n))
+    for i in range(max_iters):
+        v = free.pop()
+        c = seed.choice(range(len(community_sizes)))
+        # s = int(degree_seq[v] * (1 - mu) + 0.5)
+        s = round(degree_seq[v] * (1 - mu))
+        # If the community is large enough, add the node to the chosen
+        # community. Otherwise, return it to the list of unaffiliated
+        # nodes.
+        if s < community_sizes[c]:
+            result[c].add(v)
+        else:
+            free.append(v)
+        # If the community is too big, remove a node from it.
+        if len(result[c]) > community_sizes[c]:
+            free.append(result[c].pop())
+        if not free:
+            return result
+    msg = "Could not assign communities; try increasing min_community"
+    raise nx.ExceededMaxIterations(msg)
+
+
+@py_random_state(11)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def LFR_benchmark_graph(
+    n,
+    tau1,
+    tau2,
+    mu,
+    average_degree=None,
+    min_degree=None,
+    max_degree=None,
+    min_community=None,
+    max_community=None,
+    tol=1.0e-7,
+    max_iters=500,
+    seed=None,
+):
+    r"""Returns the LFR benchmark graph.
+
+    This algorithm proceeds as follows:
+
+    1) Find a degree sequence with a power law distribution, and minimum
+       value ``min_degree``, which has approximate average degree
+       ``average_degree``. This is accomplished by either
+
+       a) specifying ``min_degree`` and not ``average_degree``,
+       b) specifying ``average_degree`` and not ``min_degree``, in which
+          case a suitable minimum degree will be found.
+
+       ``max_degree`` can also be specified, otherwise it will be set to
+       ``n``. Each node *u* will have $\mu \mathrm{deg}(u)$ edges
+       joining it to nodes in communities other than its own and $(1 -
+       \mu) \mathrm{deg}(u)$ edges joining it to nodes in its own
+       community.
+    2) Generate community sizes according to a power law distribution
+       with exponent ``tau2``. If ``min_community`` and
+       ``max_community`` are not specified they will be selected to be
+       ``min_degree`` and ``max_degree``, respectively.  Community sizes
+       are generated until the sum of their sizes equals ``n``.
+    3) Each node will be randomly assigned a community with the
+       condition that the community is large enough for the node's
+       intra-community degree, $(1 - \mu) \mathrm{deg}(u)$ as
+       described in step 2. If a community grows too large, a random node
+       will be selected for reassignment to a new community, until all
+       nodes have been assigned a community.
+    4) Each node *u* then adds $(1 - \mu) \mathrm{deg}(u)$
+       intra-community edges and $\mu \mathrm{deg}(u)$ inter-community
+       edges.
+
+    Parameters
+    ----------
+    n : int
+        Number of nodes in the created graph.
+
+    tau1 : float
+        Power law exponent for the degree distribution of the created
+        graph. This value must be strictly greater than one.
+
+    tau2 : float
+        Power law exponent for the community size distribution in the
+        created graph. This value must be strictly greater than one.
+
+    mu : float
+        Fraction of inter-community edges incident to each node. This
+        value must be in the interval [0, 1].
+
+    average_degree : float
+        Desired average degree of nodes in the created graph. This value
+        must be in the interval [0, *n*]. Exactly one of this and
+        ``min_degree`` must be specified, otherwise a
+        :exc:`NetworkXError` is raised.
+
+    min_degree : int
+        Minimum degree of nodes in the created graph. This value must be
+        in the interval [0, *n*]. Exactly one of this and
+        ``average_degree`` must be specified, otherwise a
+        :exc:`NetworkXError` is raised.
+
+    max_degree : int
+        Maximum degree of nodes in the created graph. If not specified,
+        this is set to ``n``, the total number of nodes in the graph.
+
+    min_community : int
+        Minimum size of communities in the graph. If not specified, this
+        is set to ``min_degree``.
+
+    max_community : int
+        Maximum size of communities in the graph. If not specified, this
+        is set to ``n``, the total number of nodes in the graph.
+
+    tol : float
+        Tolerance when comparing floats, specifically when comparing
+        average degree values.
+
+    max_iters : int
+        Maximum number of iterations to try to create the community sizes,
+        degree distribution, and community affiliations.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : NetworkX graph
+        The LFR benchmark graph generated according to the specified
+        parameters.
+
+        Each node in the graph has a node attribute ``'community'`` that
+        stores the community (that is, the set of nodes) that includes
+        it.
+
+    Raises
+    ------
+    NetworkXError
+        If any of the parameters do not meet their upper and lower bounds:
+
+        - ``tau1`` and ``tau2`` must be strictly greater than 1.
+        - ``mu`` must be in [0, 1].
+        - ``max_degree`` must be in {1, ..., *n*}.
+        - ``min_community`` and ``max_community`` must be in {0, ...,
+          *n*}.
+
+        If not exactly one of ``average_degree`` and ``min_degree`` is
+        specified.
+
+        If ``min_degree`` is not specified and a suitable ``min_degree``
+        cannot be found.
+
+    ExceededMaxIterations
+        If a valid degree sequence cannot be created within
+        ``max_iters`` number of iterations.
+
+        If a valid set of community sizes cannot be created within
+        ``max_iters`` number of iterations.
+
+        If a valid community assignment cannot be created within ``10 *
+        n * max_iters`` number of iterations.
+
+    Examples
+    --------
+    Basic usage::
+
+        >>> from networkx.generators.community import LFR_benchmark_graph
+        >>> n = 250
+        >>> tau1 = 3
+        >>> tau2 = 1.5
+        >>> mu = 0.1
+        >>> G = LFR_benchmark_graph(
+        ...     n, tau1, tau2, mu, average_degree=5, min_community=20, seed=10
+        ... )
+
+    Continuing the example above, you can get the communities from the
+    node attributes of the graph::
+
+        >>> communities = {frozenset(G.nodes[v]["community"]) for v in G}
+
+    Notes
+    -----
+    This algorithm differs slightly from the original way it was
+    presented in [1].
+
+    1) Rather than connecting the graph via a configuration model then
+       rewiring to match the intra-community and inter-community
+       degrees, we do this wiring explicitly at the end, which should be
+       equivalent.
+    2) The code posted on the author's website [2] calculates the random
+       power law distributed variables and their average using
+       continuous approximations, whereas we use the discrete
+       distributions here as both degree and community size are
+       discrete.
+
+    Though the authors describe the algorithm as quite robust, testing
+    during development indicates that a somewhat narrower parameter set
+    is likely to successfully produce a graph. Some suggestions have
+    been provided in the event of exceptions.
+
+    References
+    ----------
+    .. [1] "Benchmark graphs for testing community detection algorithms",
+           Andrea Lancichinetti, Santo Fortunato, and Filippo Radicchi,
+           Phys. Rev. E 78, 046110 2008
+    .. [2] https://www.santofortunato.net/resources
+
+    """
+    # Perform some basic parameter validation.
+    if not tau1 > 1:
+        raise nx.NetworkXError("tau1 must be greater than one")
+    if not tau2 > 1:
+        raise nx.NetworkXError("tau2 must be greater than one")
+    if not 0 <= mu <= 1:
+        raise nx.NetworkXError("mu must be in the interval [0, 1]")
+
+    # Validate parameters for generating the degree sequence.
+    if max_degree is None:
+        max_degree = n
+    elif not 0 < max_degree <= n:
+        raise nx.NetworkXError("max_degree must be in the interval (0, n]")
+    if not ((min_degree is None) ^ (average_degree is None)):
+        raise nx.NetworkXError(
+            "Must assign exactly one of min_degree and average_degree"
+        )
+    if min_degree is None:
+        min_degree = _generate_min_degree(
+            tau1, average_degree, max_degree, tol, max_iters
+        )
+
+    # Generate a degree sequence with a power law distribution.
+    low, high = min_degree, max_degree
+
+    def condition(seq):
+        return sum(seq) % 2 == 0
+
+    def length(seq):
+        return len(seq) >= n
+
+    deg_seq = _powerlaw_sequence(tau1, low, high, condition, length, max_iters, seed)
+
+    # Validate parameters for generating the community size sequence.
+    if min_community is None:
+        min_community = min(deg_seq)
+    if max_community is None:
+        max_community = max(deg_seq)
+
+    # Generate a community size sequence with a power law distribution.
+    #
+    # TODO The original code incremented the number of iterations each
+    # time a new Zipf random value was drawn from the distribution. This
+    # differed from the way the number of iterations was incremented in
+    # `_powerlaw_degree_sequence`, so this code was changed to match
+    # that one. As a result, this code is allowed many more chances to
+    # generate a valid community size sequence.
+    low, high = min_community, max_community
+
+    def condition(seq):
+        return sum(seq) == n
+
+    def length(seq):
+        return sum(seq) >= n
+
+    comms = _powerlaw_sequence(tau2, low, high, condition, length, max_iters, seed)
+
+    # Generate the communities based on the given degree sequence and
+    # community sizes.
+    max_iters *= 10 * n
+    communities = _generate_communities(deg_seq, comms, mu, max_iters, seed)
+
+    # Finally, generate the benchmark graph based on the given
+    # communities, joining nodes according to the intra- and
+    # inter-community degrees.
+    G = nx.Graph()
+    G.add_nodes_from(range(n))
+    for c in communities:
+        for u in c:
+            while G.degree(u) < round(deg_seq[u] * (1 - mu)):
+                v = seed.choice(list(c))
+                G.add_edge(u, v)
+            while G.degree(u) < deg_seq[u]:
+                v = seed.choice(range(n))
+                if v not in c:
+                    G.add_edge(u, v)
+            G.nodes[u]["community"] = c
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/degree_seq.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/degree_seq.py
new file mode 100644
index 0000000000000000000000000000000000000000..d920f85de14772a5be594816a19626e4c05e0c1b
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/degree_seq.py
@@ -0,0 +1,867 @@
+"""Generate graphs with a given degree sequence or expected degree sequence."""
+
+import heapq
+import math
+from itertools import chain, combinations, zip_longest
+from operator import itemgetter
+
+import networkx as nx
+from networkx.utils import py_random_state, random_weighted_sample
+
+__all__ = [
+    "configuration_model",
+    "directed_configuration_model",
+    "expected_degree_graph",
+    "havel_hakimi_graph",
+    "directed_havel_hakimi_graph",
+    "degree_sequence_tree",
+    "random_degree_sequence_graph",
+]
+
+chaini = chain.from_iterable
+
+
+def _to_stublist(degree_sequence):
+    """Returns a list of degree-repeated node numbers.
+
+    ``degree_sequence`` is a list of nonnegative integers representing
+    the degrees of nodes in a graph.
+
+    This function returns a list of node numbers with multiplicities
+    according to the given degree sequence. For example, if the first
+    element of ``degree_sequence`` is ``3``, then the first node number,
+    ``0``, will appear at the head of the returned list three times. The
+    node numbers are assumed to be the numbers zero through
+    ``len(degree_sequence) - 1``.
+
+    Examples
+    --------
+
+    >>> degree_sequence = [1, 2, 3]
+    >>> _to_stublist(degree_sequence)
+    [0, 1, 1, 2, 2, 2]
+
+    If a zero appears in the sequence, that means the node exists but
+    has degree zero, so that number will be skipped in the returned
+    list::
+
+    >>> degree_sequence = [2, 0, 1]
+    >>> _to_stublist(degree_sequence)
+    [0, 0, 2]
+
+    """
+    return list(chaini([n] * d for n, d in enumerate(degree_sequence)))
+
+
+def _configuration_model(
+    deg_sequence, create_using, directed=False, in_deg_sequence=None, seed=None
+):
+    """Helper function for generating either undirected or directed
+    configuration model graphs.
+
+    ``deg_sequence`` is a list of nonnegative integers representing the
+    degree of the node whose label is the index of the list element.
+
+    ``create_using`` see :func:`~networkx.empty_graph`.
+
+    ``directed`` and ``in_deg_sequence`` are required if you want the
+    returned graph to be generated using the directed configuration
+    model algorithm. If ``directed`` is ``False``, then ``deg_sequence``
+    is interpreted as the degree sequence of an undirected graph and
+    ``in_deg_sequence`` is ignored. Otherwise, if ``directed`` is
+    ``True``, then ``deg_sequence`` is interpreted as the out-degree
+    sequence and ``in_deg_sequence`` as the in-degree sequence of a
+    directed graph.
+
+    .. note::
+
+       ``deg_sequence`` and ``in_deg_sequence`` need not be the same
+       length.
+
+    ``seed`` is a random.Random or numpy.random.RandomState instance
+
+    This function returns a graph, directed if and only if ``directed``
+    is ``True``, generated according to the configuration model
+    algorithm. For more information on the algorithm, see the
+    :func:`configuration_model` or :func:`directed_configuration_model`
+    functions.
+
+    """
+    n = len(deg_sequence)
+    G = nx.empty_graph(n, create_using)
+    # If empty, return the null graph immediately.
+    if n == 0:
+        return G
+    # Build a list of available degree-repeated nodes.  For example,
+    # for degree sequence [3, 2, 1, 1, 1], the "stub list" is
+    # initially [0, 0, 0, 1, 1, 2, 3, 4], that is, node 0 has degree
+    # 3 and thus is repeated 3 times, etc.
+    #
+    # Also, shuffle the stub list in order to get a random sequence of
+    # node pairs.
+    if directed:
+        pairs = zip_longest(deg_sequence, in_deg_sequence, fillvalue=0)
+        # Unzip the list of pairs into a pair of lists.
+        out_deg, in_deg = zip(*pairs)
+
+        out_stublist = _to_stublist(out_deg)
+        in_stublist = _to_stublist(in_deg)
+
+        seed.shuffle(out_stublist)
+        seed.shuffle(in_stublist)
+    else:
+        stublist = _to_stublist(deg_sequence)
+        # Choose a random balanced bipartition of the stublist, which
+        # gives a random pairing of nodes. In this implementation, we
+        # shuffle the list and then split it in half.
+        n = len(stublist)
+        half = n // 2
+        seed.shuffle(stublist)
+        out_stublist, in_stublist = stublist[:half], stublist[half:]
+    G.add_edges_from(zip(out_stublist, in_stublist))
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def configuration_model(deg_sequence, create_using=None, seed=None):
+    """Returns a random graph with the given degree sequence.
+
+    The configuration model generates a random pseudograph (graph with
+    parallel edges and self loops) by randomly assigning edges to
+    match the given degree sequence.
+
+    Parameters
+    ----------
+    deg_sequence :  list of nonnegative integers
+        Each list entry corresponds to the degree of a node.
+    create_using : NetworkX graph constructor, optional (default MultiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : MultiGraph
+        A graph with the specified degree sequence.
+        Nodes are labeled starting at 0 with an index
+        corresponding to the position in deg_sequence.
+
+    Raises
+    ------
+    NetworkXError
+        If the degree sequence does not have an even sum.
+
+    See Also
+    --------
+    is_graphical
+
+    Notes
+    -----
+    As described by Newman [1]_.
+
+    A non-graphical degree sequence (not realizable by some simple
+    graph) is allowed since this function returns graphs with self
+    loops and parallel edges.  An exception is raised if the degree
+    sequence does not have an even sum.
+
+    This configuration model construction process can lead to
+    duplicate edges and loops.  You can remove the self-loops and
+    parallel edges (see below) which will likely result in a graph
+    that doesn't have the exact degree sequence specified.
+
+    The density of self-loops and parallel edges tends to decrease as
+    the number of nodes increases. However, typically the number of
+    self-loops will approach a Poisson distribution with a nonzero mean,
+    and similarly for the number of parallel edges.  Consider a node
+    with *k* stubs. The probability of being joined to another stub of
+    the same node is basically (*k* - *1*) / *N*, where *k* is the
+    degree and *N* is the number of nodes. So the probability of a
+    self-loop scales like *c* / *N* for some constant *c*. As *N* grows,
+    this means we expect *c* self-loops. Similarly for parallel edges.
+
+    References
+    ----------
+    .. [1] M.E.J. Newman, "The structure and function of complex networks",
+       SIAM REVIEW 45-2, pp 167-256, 2003.
+
+    Examples
+    --------
+    You can create a degree sequence following a particular distribution
+    by using the one of the distribution functions in
+    :mod:`~networkx.utils.random_sequence` (or one of your own). For
+    example, to create an undirected multigraph on one hundred nodes
+    with degree sequence chosen from the power law distribution:
+
+    >>> sequence = nx.random_powerlaw_tree_sequence(100, tries=5000)
+    >>> G = nx.configuration_model(sequence)
+    >>> len(G)
+    100
+    >>> actual_degrees = [d for v, d in G.degree()]
+    >>> actual_degrees == sequence
+    True
+
+    The returned graph is a multigraph, which may have parallel
+    edges. To remove any parallel edges from the returned graph:
+
+    >>> G = nx.Graph(G)
+
+    Similarly, to remove self-loops:
+
+    >>> G.remove_edges_from(nx.selfloop_edges(G))
+
+    """
+    if sum(deg_sequence) % 2 != 0:
+        msg = "Invalid degree sequence: sum of degrees must be even, not odd"
+        raise nx.NetworkXError(msg)
+
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed():
+        raise nx.NetworkXNotImplemented("not implemented for directed graphs")
+
+    G = _configuration_model(deg_sequence, G, seed=seed)
+
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def directed_configuration_model(
+    in_degree_sequence, out_degree_sequence, create_using=None, seed=None
+):
+    """Returns a directed_random graph with the given degree sequences.
+
+    The configuration model generates a random directed pseudograph
+    (graph with parallel edges and self loops) by randomly assigning
+    edges to match the given degree sequences.
+
+    Parameters
+    ----------
+    in_degree_sequence :  list of nonnegative integers
+       Each list entry corresponds to the in-degree of a node.
+    out_degree_sequence :  list of nonnegative integers
+       Each list entry corresponds to the out-degree of a node.
+    create_using : NetworkX graph constructor, optional (default MultiDiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : MultiDiGraph
+        A graph with the specified degree sequences.
+        Nodes are labeled starting at 0 with an index
+        corresponding to the position in deg_sequence.
+
+    Raises
+    ------
+    NetworkXError
+        If the degree sequences do not have the same sum.
+
+    See Also
+    --------
+    configuration_model
+
+    Notes
+    -----
+    Algorithm as described by Newman [1]_.
+
+    A non-graphical degree sequence (not realizable by some simple
+    graph) is allowed since this function returns graphs with self
+    loops and parallel edges.  An exception is raised if the degree
+    sequences does not have the same sum.
+
+    This configuration model construction process can lead to
+    duplicate edges and loops.  You can remove the self-loops and
+    parallel edges (see below) which will likely result in a graph
+    that doesn't have the exact degree sequence specified.  This
+    "finite-size effect" decreases as the size of the graph increases.
+
+    References
+    ----------
+    .. [1] Newman, M. E. J. and Strogatz, S. H. and Watts, D. J.
+       Random graphs with arbitrary degree distributions and their applications
+       Phys. Rev. E, 64, 026118 (2001)
+
+    Examples
+    --------
+    One can modify the in- and out-degree sequences from an existing
+    directed graph in order to create a new directed graph. For example,
+    here we modify the directed path graph:
+
+    >>> D = nx.DiGraph([(0, 1), (1, 2), (2, 3)])
+    >>> din = list(d for n, d in D.in_degree())
+    >>> dout = list(d for n, d in D.out_degree())
+    >>> din.append(1)
+    >>> dout[0] = 2
+    >>> # We now expect an edge from node 0 to a new node, node 3.
+    ... D = nx.directed_configuration_model(din, dout)
+
+    The returned graph is a directed multigraph, which may have parallel
+    edges. To remove any parallel edges from the returned graph:
+
+    >>> D = nx.DiGraph(D)
+
+    Similarly, to remove self-loops:
+
+    >>> D.remove_edges_from(nx.selfloop_edges(D))
+
+    """
+    if sum(in_degree_sequence) != sum(out_degree_sequence):
+        msg = "Invalid degree sequences: sequences must have equal sums"
+        raise nx.NetworkXError(msg)
+
+    if create_using is None:
+        create_using = nx.MultiDiGraph
+
+    G = _configuration_model(
+        out_degree_sequence,
+        create_using,
+        directed=True,
+        in_deg_sequence=in_degree_sequence,
+        seed=seed,
+    )
+
+    name = "directed configuration_model {} nodes {} edges"
+    return G
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def expected_degree_graph(w, seed=None, selfloops=True):
+    r"""Returns a random graph with given expected degrees.
+
+    Given a sequence of expected degrees $W=(w_0,w_1,\ldots,w_{n-1})$
+    of length $n$ this algorithm assigns an edge between node $u$ and
+    node $v$ with probability
+
+    .. math::
+
+       p_{uv} = \frac{w_u w_v}{\sum_k w_k} .
+
+    Parameters
+    ----------
+    w : list
+        The list of expected degrees.
+    selfloops: bool (default=True)
+        Set to False to remove the possibility of self-loop edges.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    Graph
+
+    Examples
+    --------
+    >>> z = [10 for i in range(100)]
+    >>> G = nx.expected_degree_graph(z)
+
+    Notes
+    -----
+    The nodes have integer labels corresponding to index of expected degrees
+    input sequence.
+
+    The complexity of this algorithm is $\mathcal{O}(n+m)$ where $n$ is the
+    number of nodes and $m$ is the expected number of edges.
+
+    The model in [1]_ includes the possibility of self-loop edges.
+    Set selfloops=False to produce a graph without self loops.
+
+    For finite graphs this model doesn't produce exactly the given
+    expected degree sequence.  Instead the expected degrees are as
+    follows.
+
+    For the case without self loops (selfloops=False),
+
+    .. math::
+
+       E[deg(u)] = \sum_{v \ne u} p_{uv}
+                = w_u \left( 1 - \frac{w_u}{\sum_k w_k} \right) .
+
+
+    NetworkX uses the standard convention that a self-loop edge counts 2
+    in the degree of a node, so with self loops (selfloops=True),
+
+    .. math::
+
+       E[deg(u)] =  \sum_{v \ne u} p_{uv}  + 2 p_{uu}
+                = w_u \left( 1 + \frac{w_u}{\sum_k w_k} \right) .
+
+    References
+    ----------
+    .. [1] Fan Chung and L. Lu, Connected components in random graphs with
+       given expected degree sequences, Ann. Combinatorics, 6,
+       pp. 125-145, 2002.
+    .. [2] Joel Miller and Aric Hagberg,
+       Efficient generation of networks with given expected degrees,
+       in Algorithms and Models for the Web-Graph (WAW 2011),
+       Alan Frieze, Paul Horn, and Paweł Prałat (Eds), LNCS 6732,
+       pp. 115-126, 2011.
+    """
+    n = len(w)
+    G = nx.empty_graph(n)
+
+    # If there are no nodes are no edges in the graph, return the empty graph.
+    if n == 0 or max(w) == 0:
+        return G
+
+    rho = 1 / sum(w)
+    # Sort the weights in decreasing order. The original order of the
+    # weights dictates the order of the (integer) node labels, so we
+    # need to remember the permutation applied in the sorting.
+    order = sorted(enumerate(w), key=itemgetter(1), reverse=True)
+    mapping = {c: u for c, (u, v) in enumerate(order)}
+    seq = [v for u, v in order]
+    last = n
+    if not selfloops:
+        last -= 1
+    for u in range(last):
+        v = u
+        if not selfloops:
+            v += 1
+        factor = seq[u] * rho
+        p = min(seq[v] * factor, 1)
+        while v < n and p > 0:
+            if p != 1:
+                r = seed.random()
+                v += math.floor(math.log(r, 1 - p))
+            if v < n:
+                q = min(seq[v] * factor, 1)
+                if seed.random() < q / p:
+                    G.add_edge(mapping[u], mapping[v])
+                v += 1
+                p = q
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def havel_hakimi_graph(deg_sequence, create_using=None):
+    """Returns a simple graph with given degree sequence constructed
+    using the Havel-Hakimi algorithm.
+
+    Parameters
+    ----------
+    deg_sequence: list of integers
+        Each integer corresponds to the degree of a node (need not be sorted).
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Directed graphs are not allowed.
+
+    Raises
+    ------
+    NetworkXException
+        For a non-graphical degree sequence (i.e. one
+        not realizable by some simple graph).
+
+    Notes
+    -----
+    The Havel-Hakimi algorithm constructs a simple graph by
+    successively connecting the node of highest degree to other nodes
+    of highest degree, resorting remaining nodes by degree, and
+    repeating the process. The resulting graph has a high
+    degree-associativity.  Nodes are labeled 1,.., len(deg_sequence),
+    corresponding to their position in deg_sequence.
+
+    The basic algorithm is from Hakimi [1]_ and was generalized by
+    Kleitman and Wang [2]_.
+
+    References
+    ----------
+    .. [1] Hakimi S., On Realizability of a Set of Integers as
+       Degrees of the Vertices of a Linear Graph. I,
+       Journal of SIAM, 10(3), pp. 496-506 (1962)
+    .. [2] Kleitman D.J. and Wang D.L.
+       Algorithms for Constructing Graphs and Digraphs with Given Valences
+       and Factors  Discrete Mathematics, 6(1), pp. 79-88 (1973)
+    """
+    if not nx.is_graphical(deg_sequence):
+        raise nx.NetworkXError("Invalid degree sequence")
+
+    p = len(deg_sequence)
+    G = nx.empty_graph(p, create_using)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed graphs are not supported")
+    num_degs = [[] for i in range(p)]
+    dmax, dsum, n = 0, 0, 0
+    for d in deg_sequence:
+        # Process only the non-zero integers
+        if d > 0:
+            num_degs[d].append(n)
+            dmax, dsum, n = max(dmax, d), dsum + d, n + 1
+    # Return graph if no edges
+    if n == 0:
+        return G
+
+    modstubs = [(0, 0)] * (dmax + 1)
+    # Successively reduce degree sequence by removing the maximum degree
+    while n > 0:
+        # Retrieve the maximum degree in the sequence
+        while len(num_degs[dmax]) == 0:
+            dmax -= 1
+        # If there are not enough stubs to connect to, then the sequence is
+        # not graphical
+        if dmax > n - 1:
+            raise nx.NetworkXError("Non-graphical integer sequence")
+
+        # Remove largest stub in list
+        source = num_degs[dmax].pop()
+        n -= 1
+        # Reduce the next dmax largest stubs
+        mslen = 0
+        k = dmax
+        for i in range(dmax):
+            while len(num_degs[k]) == 0:
+                k -= 1
+            target = num_degs[k].pop()
+            G.add_edge(source, target)
+            n -= 1
+            if k > 1:
+                modstubs[mslen] = (k - 1, target)
+                mslen += 1
+        # Add back to the list any nonzero stubs that were removed
+        for i in range(mslen):
+            (stubval, stubtarget) = modstubs[i]
+            num_degs[stubval].append(stubtarget)
+            n += 1
+
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def directed_havel_hakimi_graph(in_deg_sequence, out_deg_sequence, create_using=None):
+    """Returns a directed graph with the given degree sequences.
+
+    Parameters
+    ----------
+    in_deg_sequence :  list of integers
+        Each list entry corresponds to the in-degree of a node.
+    out_deg_sequence : list of integers
+        Each list entry corresponds to the out-degree of a node.
+    create_using : NetworkX graph constructor, optional (default DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : DiGraph
+        A graph with the specified degree sequences.
+        Nodes are labeled starting at 0 with an index
+        corresponding to the position in deg_sequence
+
+    Raises
+    ------
+    NetworkXError
+        If the degree sequences are not digraphical.
+
+    See Also
+    --------
+    configuration_model
+
+    Notes
+    -----
+    Algorithm as described by Kleitman and Wang [1]_.
+
+    References
+    ----------
+    .. [1] D.J. Kleitman and D.L. Wang
+       Algorithms for Constructing Graphs and Digraphs with Given Valences
+       and Factors Discrete Mathematics, 6(1), pp. 79-88 (1973)
+    """
+    in_deg_sequence = nx.utils.make_list_of_ints(in_deg_sequence)
+    out_deg_sequence = nx.utils.make_list_of_ints(out_deg_sequence)
+
+    # Process the sequences and form two heaps to store degree pairs with
+    # either zero or nonzero out degrees
+    sumin, sumout = 0, 0
+    nin, nout = len(in_deg_sequence), len(out_deg_sequence)
+    maxn = max(nin, nout)
+    G = nx.empty_graph(maxn, create_using, default=nx.DiGraph)
+    if maxn == 0:
+        return G
+    maxin = 0
+    stubheap, zeroheap = [], []
+    for n in range(maxn):
+        in_deg, out_deg = 0, 0
+        if n < nout:
+            out_deg = out_deg_sequence[n]
+        if n < nin:
+            in_deg = in_deg_sequence[n]
+        if in_deg < 0 or out_deg < 0:
+            raise nx.NetworkXError(
+                "Invalid degree sequences. Sequence values must be positive."
+            )
+        sumin, sumout, maxin = sumin + in_deg, sumout + out_deg, max(maxin, in_deg)
+        if in_deg > 0:
+            stubheap.append((-1 * out_deg, -1 * in_deg, n))
+        elif out_deg > 0:
+            zeroheap.append((-1 * out_deg, n))
+    if sumin != sumout:
+        raise nx.NetworkXError(
+            "Invalid degree sequences. Sequences must have equal sums."
+        )
+    heapq.heapify(stubheap)
+    heapq.heapify(zeroheap)
+
+    modstubs = [(0, 0, 0)] * (maxin + 1)
+    # Successively reduce degree sequence by removing the maximum
+    while stubheap:
+        # Remove first value in the sequence with a non-zero in degree
+        (freeout, freein, target) = heapq.heappop(stubheap)
+        freein *= -1
+        if freein > len(stubheap) + len(zeroheap):
+            raise nx.NetworkXError("Non-digraphical integer sequence")
+
+        # Attach arcs from the nodes with the most stubs
+        mslen = 0
+        for i in range(freein):
+            if zeroheap and (not stubheap or stubheap[0][0] > zeroheap[0][0]):
+                (stubout, stubsource) = heapq.heappop(zeroheap)
+                stubin = 0
+            else:
+                (stubout, stubin, stubsource) = heapq.heappop(stubheap)
+            if stubout == 0:
+                raise nx.NetworkXError("Non-digraphical integer sequence")
+            G.add_edge(stubsource, target)
+            # Check if source is now totally connected
+            if stubout + 1 < 0 or stubin < 0:
+                modstubs[mslen] = (stubout + 1, stubin, stubsource)
+                mslen += 1
+
+        # Add the nodes back to the heaps that still have available stubs
+        for i in range(mslen):
+            stub = modstubs[i]
+            if stub[1] < 0:
+                heapq.heappush(stubheap, stub)
+            else:
+                heapq.heappush(zeroheap, (stub[0], stub[2]))
+        if freeout < 0:
+            heapq.heappush(zeroheap, (freeout, target))
+
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def degree_sequence_tree(deg_sequence, create_using=None):
+    """Make a tree for the given degree sequence.
+
+    A tree has #nodes-#edges=1 so
+    the degree sequence must have
+    len(deg_sequence)-sum(deg_sequence)/2=1
+    """
+    # The sum of the degree sequence must be even (for any undirected graph).
+    degree_sum = sum(deg_sequence)
+    if degree_sum % 2 != 0:
+        msg = "Invalid degree sequence: sum of degrees must be even, not odd"
+        raise nx.NetworkXError(msg)
+    if len(deg_sequence) - degree_sum // 2 != 1:
+        msg = (
+            "Invalid degree sequence: tree must have number of nodes equal"
+            " to one less than the number of edges"
+        )
+        raise nx.NetworkXError(msg)
+    G = nx.empty_graph(0, create_using)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    # Sort all degrees greater than 1 in decreasing order.
+    #
+    # TODO Does this need to be sorted in reverse order?
+    deg = sorted((s for s in deg_sequence if s > 1), reverse=True)
+
+    # make path graph as backbone
+    n = len(deg) + 2
+    nx.add_path(G, range(n))
+    last = n
+
+    # add the leaves
+    for source in range(1, n - 1):
+        nedges = deg.pop() - 2
+        for target in range(last, last + nedges):
+            G.add_edge(source, target)
+        last += nedges
+
+    # in case we added one too many
+    if len(G) > len(deg_sequence):
+        G.remove_node(0)
+    return G
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_degree_sequence_graph(sequence, seed=None, tries=10):
+    r"""Returns a simple random graph with the given degree sequence.
+
+    If the maximum degree $d_m$ in the sequence is $O(m^{1/4})$ then the
+    algorithm produces almost uniform random graphs in $O(m d_m)$ time
+    where $m$ is the number of edges.
+
+    Parameters
+    ----------
+    sequence :  list of integers
+        Sequence of degrees
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    tries : int, optional
+        Maximum number of tries to create a graph
+
+    Returns
+    -------
+    G : Graph
+        A graph with the specified degree sequence.
+        Nodes are labeled starting at 0 with an index
+        corresponding to the position in the sequence.
+
+    Raises
+    ------
+    NetworkXUnfeasible
+        If the degree sequence is not graphical.
+    NetworkXError
+        If a graph is not produced in specified number of tries
+
+    See Also
+    --------
+    is_graphical, configuration_model
+
+    Notes
+    -----
+    The generator algorithm [1]_ is not guaranteed to produce a graph.
+
+    References
+    ----------
+    .. [1] Moshen Bayati, Jeong Han Kim, and Amin Saberi,
+       A sequential algorithm for generating random graphs.
+       Algorithmica, Volume 58, Number 4, 860-910,
+       DOI: 10.1007/s00453-009-9340-1
+
+    Examples
+    --------
+    >>> sequence = [1, 2, 2, 3]
+    >>> G = nx.random_degree_sequence_graph(sequence, seed=42)
+    >>> sorted(d for n, d in G.degree())
+    [1, 2, 2, 3]
+    """
+    DSRG = DegreeSequenceRandomGraph(sequence, seed)
+    for try_n in range(tries):
+        try:
+            return DSRG.generate()
+        except nx.NetworkXUnfeasible:
+            pass
+    raise nx.NetworkXError(f"failed to generate graph in {tries} tries")
+
+
+class DegreeSequenceRandomGraph:
+    # class to generate random graphs with a given degree sequence
+    # use random_degree_sequence_graph()
+    def __init__(self, degree, rng):
+        self.rng = rng
+        self.degree = list(degree)
+        if not nx.is_graphical(self.degree):
+            raise nx.NetworkXUnfeasible("degree sequence is not graphical")
+        # node labels are integers 0,...,n-1
+        self.m = sum(self.degree) / 2.0  # number of edges
+        try:
+            self.dmax = max(self.degree)  # maximum degree
+        except ValueError:
+            self.dmax = 0
+
+    def generate(self):
+        # remaining_degree is mapping from int->remaining degree
+        self.remaining_degree = dict(enumerate(self.degree))
+        # add all nodes to make sure we get isolated nodes
+        self.graph = nx.Graph()
+        self.graph.add_nodes_from(self.remaining_degree)
+        # remove zero degree nodes
+        for n, d in list(self.remaining_degree.items()):
+            if d == 0:
+                del self.remaining_degree[n]
+        if len(self.remaining_degree) > 0:
+            # build graph in three phases according to how many unmatched edges
+            self.phase1()
+            self.phase2()
+            self.phase3()
+        return self.graph
+
+    def update_remaining(self, u, v, aux_graph=None):
+        # decrement remaining nodes, modify auxiliary graph if in phase3
+        if aux_graph is not None:
+            # remove edges from auxiliary graph
+            aux_graph.remove_edge(u, v)
+        if self.remaining_degree[u] == 1:
+            del self.remaining_degree[u]
+            if aux_graph is not None:
+                aux_graph.remove_node(u)
+        else:
+            self.remaining_degree[u] -= 1
+        if self.remaining_degree[v] == 1:
+            del self.remaining_degree[v]
+            if aux_graph is not None:
+                aux_graph.remove_node(v)
+        else:
+            self.remaining_degree[v] -= 1
+
+    def p(self, u, v):
+        # degree probability
+        return 1 - self.degree[u] * self.degree[v] / (4.0 * self.m)
+
+    def q(self, u, v):
+        # remaining degree probability
+        norm = max(self.remaining_degree.values()) ** 2
+        return self.remaining_degree[u] * self.remaining_degree[v] / norm
+
+    def suitable_edge(self):
+        """Returns True if and only if an arbitrary remaining node can
+        potentially be joined with some other remaining node.
+
+        """
+        nodes = iter(self.remaining_degree)
+        u = next(nodes)
+        return any(v not in self.graph[u] for v in nodes)
+
+    def phase1(self):
+        # choose node pairs from (degree) weighted distribution
+        rem_deg = self.remaining_degree
+        while sum(rem_deg.values()) >= 2 * self.dmax**2:
+            u, v = sorted(random_weighted_sample(rem_deg, 2, self.rng))
+            if self.graph.has_edge(u, v):
+                continue
+            if self.rng.random() < self.p(u, v):  # accept edge
+                self.graph.add_edge(u, v)
+                self.update_remaining(u, v)
+
+    def phase2(self):
+        # choose remaining nodes uniformly at random and use rejection sampling
+        remaining_deg = self.remaining_degree
+        rng = self.rng
+        while len(remaining_deg) >= 2 * self.dmax:
+            while True:
+                u, v = sorted(rng.sample(list(remaining_deg.keys()), 2))
+                if self.graph.has_edge(u, v):
+                    continue
+                if rng.random() < self.q(u, v):
+                    break
+            if rng.random() < self.p(u, v):  # accept edge
+                self.graph.add_edge(u, v)
+                self.update_remaining(u, v)
+
+    def phase3(self):
+        # build potential remaining edges and choose with rejection sampling
+        potential_edges = combinations(self.remaining_degree, 2)
+        # build auxiliary graph of potential edges not already in graph
+        H = nx.Graph(
+            [(u, v) for (u, v) in potential_edges if not self.graph.has_edge(u, v)]
+        )
+        rng = self.rng
+        while self.remaining_degree:
+            if not self.suitable_edge():
+                raise nx.NetworkXUnfeasible("no suitable edges left")
+            while True:
+                u, v = sorted(rng.choice(list(H.edges())))
+                if rng.random() < self.q(u, v):
+                    break
+            if rng.random() < self.p(u, v):  # accept edge
+                self.graph.add_edge(u, v)
+                self.update_remaining(u, v, aux_graph=H)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/directed.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/directed.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b2b5c399b55fa38aecb389d02790a8a83edf1ab
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/directed.py
@@ -0,0 +1,572 @@
+"""
+Generators for some directed graphs, including growing network (GN) graphs and
+scale-free graphs.
+
+"""
+
+import numbers
+from collections import Counter
+
+import networkx as nx
+from networkx.generators.classic import empty_graph
+from networkx.utils import (
+    discrete_sequence,
+    np_random_state,
+    py_random_state,
+    weighted_choice,
+)
+
+__all__ = [
+    "gn_graph",
+    "gnc_graph",
+    "gnr_graph",
+    "random_k_out_graph",
+    "scale_free_graph",
+]
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gn_graph(n, kernel=None, create_using=None, seed=None):
+    """Returns the growing network (GN) digraph with `n` nodes.
+
+    The GN graph is built by adding nodes one at a time with a link to one
+    previously added node.  The target node for the link is chosen with
+    probability based on degree.  The default attachment kernel is a linear
+    function of the degree of a node.
+
+    The graph is always a (directed) tree.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes for the generated graph.
+    kernel : function
+        The attachment kernel.
+    create_using : NetworkX graph constructor, optional (default DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Examples
+    --------
+    To create the undirected GN graph, use the :meth:`~DiGraph.to_directed`
+    method::
+
+    >>> D = nx.gn_graph(10)  # the GN graph
+    >>> G = D.to_undirected()  # the undirected version
+
+    To specify an attachment kernel, use the `kernel` keyword argument::
+
+    >>> D = nx.gn_graph(10, kernel=lambda x: x**1.5)  # A_k = k^1.5
+
+    References
+    ----------
+    .. [1] P. L. Krapivsky and S. Redner,
+           Organization of Growing Random Networks,
+           Phys. Rev. E, 63, 066123, 2001.
+    """
+    G = empty_graph(1, create_using, default=nx.DiGraph)
+    if not G.is_directed():
+        raise nx.NetworkXError("create_using must indicate a Directed Graph")
+
+    if kernel is None:
+
+        def kernel(x):
+            return x
+
+    if n == 1:
+        return G
+
+    G.add_edge(1, 0)  # get started
+    ds = [1, 1]  # degree sequence
+
+    for source in range(2, n):
+        # compute distribution from kernel and degree
+        dist = [kernel(d) for d in ds]
+        # choose target from discrete distribution
+        target = discrete_sequence(1, distribution=dist, seed=seed)[0]
+        G.add_edge(source, target)
+        ds.append(1)  # the source has only one link (degree one)
+        ds[target] += 1  # add one to the target link degree
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gnr_graph(n, p, create_using=None, seed=None):
+    """Returns the growing network with redirection (GNR) digraph with `n`
+    nodes and redirection probability `p`.
+
+    The GNR graph is built by adding nodes one at a time with a link to one
+    previously added node.  The previous target node is chosen uniformly at
+    random.  With probability `p` the link is instead "redirected" to the
+    successor node of the target.
+
+    The graph is always a (directed) tree.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes for the generated graph.
+    p : float
+        The redirection probability.
+    create_using : NetworkX graph constructor, optional (default DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Examples
+    --------
+    To create the undirected GNR graph, use the :meth:`~DiGraph.to_directed`
+    method::
+
+    >>> D = nx.gnr_graph(10, 0.5)  # the GNR graph
+    >>> G = D.to_undirected()  # the undirected version
+
+    References
+    ----------
+    .. [1] P. L. Krapivsky and S. Redner,
+           Organization of Growing Random Networks,
+           Phys. Rev. E, 63, 066123, 2001.
+    """
+    G = empty_graph(1, create_using, default=nx.DiGraph)
+    if not G.is_directed():
+        raise nx.NetworkXError("create_using must indicate a Directed Graph")
+
+    if n == 1:
+        return G
+
+    for source in range(1, n):
+        target = seed.randrange(0, source)
+        if seed.random() < p and target != 0:
+            target = next(G.successors(target))
+        G.add_edge(source, target)
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gnc_graph(n, create_using=None, seed=None):
+    """Returns the growing network with copying (GNC) digraph with `n` nodes.
+
+    The GNC graph is built by adding nodes one at a time with a link to one
+    previously added node (chosen uniformly at random) and to all of that
+    node's successors.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes for the generated graph.
+    create_using : NetworkX graph constructor, optional (default DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    References
+    ----------
+    .. [1] P. L. Krapivsky and S. Redner,
+           Network Growth by Copying,
+           Phys. Rev. E, 71, 036118, 2005k.},
+    """
+    G = empty_graph(1, create_using, default=nx.DiGraph)
+    if not G.is_directed():
+        raise nx.NetworkXError("create_using must indicate a Directed Graph")
+
+    if n == 1:
+        return G
+
+    for source in range(1, n):
+        target = seed.randrange(0, source)
+        for succ in G.successors(target):
+            G.add_edge(source, succ)
+        G.add_edge(source, target)
+    return G
+
+
+@py_random_state(6)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def scale_free_graph(
+    n,
+    alpha=0.41,
+    beta=0.54,
+    gamma=0.05,
+    delta_in=0.2,
+    delta_out=0,
+    seed=None,
+    initial_graph=None,
+):
+    """Returns a scale-free directed graph.
+
+    Parameters
+    ----------
+    n : integer
+        Number of nodes in graph
+    alpha : float
+        Probability for adding a new node connected to an existing node
+        chosen randomly according to the in-degree distribution.
+    beta : float
+        Probability for adding an edge between two existing nodes.
+        One existing node is chosen randomly according the in-degree
+        distribution and the other chosen randomly according to the out-degree
+        distribution.
+    gamma : float
+        Probability for adding a new node connected to an existing node
+        chosen randomly according to the out-degree distribution.
+    delta_in : float
+        Bias for choosing nodes from in-degree distribution.
+    delta_out : float
+        Bias for choosing nodes from out-degree distribution.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    initial_graph : MultiDiGraph instance, optional
+        Build the scale-free graph starting from this initial MultiDiGraph,
+        if provided.
+
+    Returns
+    -------
+    MultiDiGraph
+
+    Examples
+    --------
+    Create a scale-free graph on one hundred nodes::
+
+    >>> G = nx.scale_free_graph(100)
+
+    Notes
+    -----
+    The sum of `alpha`, `beta`, and `gamma` must be 1.
+
+    References
+    ----------
+    .. [1] B. Bollobás, C. Borgs, J. Chayes, and O. Riordan,
+           Directed scale-free graphs,
+           Proceedings of the fourteenth annual ACM-SIAM Symposium on
+           Discrete Algorithms, 132--139, 2003.
+    """
+
+    def _choose_node(candidates, node_list, delta):
+        if delta > 0:
+            bias_sum = len(node_list) * delta
+            p_delta = bias_sum / (bias_sum + len(candidates))
+            if seed.random() < p_delta:
+                return seed.choice(node_list)
+        return seed.choice(candidates)
+
+    if initial_graph is not None and hasattr(initial_graph, "_adj"):
+        if not isinstance(initial_graph, nx.MultiDiGraph):
+            raise nx.NetworkXError("initial_graph must be a MultiDiGraph.")
+        G = initial_graph
+    else:
+        # Start with 3-cycle
+        G = nx.MultiDiGraph([(0, 1), (1, 2), (2, 0)])
+
+    if alpha <= 0:
+        raise ValueError("alpha must be > 0.")
+    if beta <= 0:
+        raise ValueError("beta must be > 0.")
+    if gamma <= 0:
+        raise ValueError("gamma must be > 0.")
+
+    if abs(alpha + beta + gamma - 1.0) >= 1e-9:
+        raise ValueError("alpha+beta+gamma must equal 1.")
+
+    if delta_in < 0:
+        raise ValueError("delta_in must be >= 0.")
+
+    if delta_out < 0:
+        raise ValueError("delta_out must be >= 0.")
+
+    # pre-populate degree states
+    vs = sum((count * [idx] for idx, count in G.out_degree()), [])
+    ws = sum((count * [idx] for idx, count in G.in_degree()), [])
+
+    # pre-populate node state
+    node_list = list(G.nodes())
+
+    # see if there already are number-based nodes
+    numeric_nodes = [n for n in node_list if isinstance(n, numbers.Number)]
+    if len(numeric_nodes) > 0:
+        # set cursor for new nodes appropriately
+        cursor = max(int(n.real) for n in numeric_nodes) + 1
+    else:
+        # or start at zero
+        cursor = 0
+
+    while len(G) < n:
+        r = seed.random()
+
+        # random choice in alpha,beta,gamma ranges
+        if r < alpha:
+            # alpha
+            # add new node v
+            v = cursor
+            cursor += 1
+            # also add to node state
+            node_list.append(v)
+            # choose w according to in-degree and delta_in
+            w = _choose_node(ws, node_list, delta_in)
+
+        elif r < alpha + beta:
+            # beta
+            # choose v according to out-degree and delta_out
+            v = _choose_node(vs, node_list, delta_out)
+            # choose w according to in-degree and delta_in
+            w = _choose_node(ws, node_list, delta_in)
+
+        else:
+            # gamma
+            # choose v according to out-degree and delta_out
+            v = _choose_node(vs, node_list, delta_out)
+            # add new node w
+            w = cursor
+            cursor += 1
+            # also add to node state
+            node_list.append(w)
+
+        # add edge to graph
+        G.add_edge(v, w)
+
+        # update degree states
+        vs.append(v)
+        ws.append(w)
+
+    return G
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_uniform_k_out_graph(n, k, self_loops=True, with_replacement=True, seed=None):
+    """Returns a random `k`-out graph with uniform attachment.
+
+    A random `k`-out graph with uniform attachment is a multidigraph
+    generated by the following algorithm. For each node *u*, choose
+    `k` nodes *v* uniformly at random (with replacement). Add a
+    directed edge joining *u* to *v*.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the returned graph.
+
+    k : int
+        The out-degree of each node in the returned graph.
+
+    self_loops : bool
+        If True, self-loops are allowed when generating the graph.
+
+    with_replacement : bool
+        If True, neighbors are chosen with replacement and the
+        returned graph will be a directed multigraph. Otherwise,
+        neighbors are chosen without replacement and the returned graph
+        will be a directed graph.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    NetworkX graph
+        A `k`-out-regular directed graph generated according to the
+        above algorithm. It will be a multigraph if and only if
+        `with_replacement` is True.
+
+    Raises
+    ------
+    ValueError
+        If `with_replacement` is False and `k` is greater than
+        `n`.
+
+    See also
+    --------
+    random_k_out_graph
+
+    Notes
+    -----
+    The return digraph or multidigraph may not be strongly connected, or
+    even weakly connected.
+
+    If `with_replacement` is True, this function is similar to
+    :func:`random_k_out_graph`, if that function had parameter `alpha`
+    set to positive infinity.
+
+    """
+    if with_replacement:
+        create_using = nx.MultiDiGraph()
+
+        def sample(v, nodes):
+            if not self_loops:
+                nodes = nodes - {v}
+            return (seed.choice(list(nodes)) for i in range(k))
+
+    else:
+        create_using = nx.DiGraph()
+
+        def sample(v, nodes):
+            if not self_loops:
+                nodes = nodes - {v}
+            return seed.sample(list(nodes), k)
+
+    G = nx.empty_graph(n, create_using)
+    nodes = set(G)
+    for u in G:
+        G.add_edges_from((u, v) for v in sample(u, nodes))
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_k_out_graph(n, k, alpha, self_loops=True, seed=None):
+    """Returns a random `k`-out graph with preferential attachment.
+
+    .. versionchanged:: 3.5
+       Different implementations will be used based on whether NumPy is
+       available. See Notes for details.
+
+    A random `k`-out graph with preferential attachment is a
+    multidigraph generated by the following algorithm.
+
+    1. Begin with an empty digraph, and initially set each node to have
+       weight `alpha`.
+    2. Choose a node `u` with out-degree less than `k` uniformly at
+       random.
+    3. Choose a node `v` from with probability proportional to its
+       weight.
+    4. Add a directed edge from `u` to `v`, and increase the weight
+       of `v` by one.
+    5. If each node has out-degree `k`, halt, otherwise repeat from
+       step 2.
+
+    For more information on this model of random graph, see [1]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the returned graph.
+
+    k : int
+        The out-degree of each node in the returned graph.
+
+    alpha : float
+        A positive :class:`float` representing the initial weight of
+        each vertex. A higher number means that in step 3 above, nodes
+        will be chosen more like a true uniformly random sample, and a
+        lower number means that nodes are more likely to be chosen as
+        their in-degree increases. If this parameter is not positive, a
+        :exc:`ValueError` is raised.
+
+    self_loops : bool
+        If True, self-loops are allowed when generating the graph.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    :class:`~networkx.classes.MultiDiGraph`
+        A `k`-out-regular multidigraph generated according to the above
+        algorithm.
+
+    Raises
+    ------
+    ValueError
+        If `alpha` is not positive.
+
+    Notes
+    -----
+    The returned multidigraph may not be strongly connected, or even
+    weakly connected.
+
+    `random_k_out_graph` has two implementations: an array-based formulation that
+    uses `numpy` (``_random_k_out_graph_numpy``), and a pure-Python
+    implementation (``_random_k_out_graph_python``).
+    The NumPy implementation is more performant, especially for large `n`, and is
+    therefore used by default. If NumPy is not installed in the environment,
+    then the pure Python implementation is executed.
+    However, you can explicitly control which implementation is executed by directly
+    calling the corresponding function::
+
+        # Use numpy if available, else Python
+        nx.random_k_out_graph(1000, 5, alpha=1)
+
+        # Use the numpy-based implementation (raises ImportError if numpy not installed)
+        nx.generators.directed._random_k_out_graph_numpy(1000, 5, alpha=1)
+
+        # Use the Python-based implementation
+        nx.generators.directed._random_k_out_graph_python(1000, 5, alpha=1)
+
+    References
+    ----------
+    .. [1] Peterson, Nicholas R., and Boris Pittel.
+       "Distance between two random `k`-out digraphs, with and without preferential attachment."
+       arXiv preprint arXiv:1311.5961 (2013) <https://arxiv.org/abs/1311.5961>.
+
+    """
+    if alpha < 0:
+        raise ValueError("alpha must be positive")
+    try:  # Use numpy if available, otherwise fall back to pure Python implementation
+        return _random_k_out_graph_numpy(n, k, alpha, self_loops, seed)
+    except ImportError:
+        return _random_k_out_graph_python(n, k, alpha, self_loops, seed)
+
+
+@np_random_state(4)
+def _random_k_out_graph_numpy(n, k, alpha, self_loops=True, seed=None):
+    import numpy as np
+
+    G = nx.empty_graph(n, create_using=nx.MultiDiGraph)
+    nodes = np.arange(n)
+    remaining_mask = np.full(n, True)
+    weights = np.full(n, alpha)
+    total_weight = n * alpha
+    out_strengths = np.zeros(n)
+
+    for i in range(k * n):
+        u = seed.choice(nodes[remaining_mask])
+
+        if self_loops:
+            v = seed.choice(nodes, p=weights / total_weight)
+        else:  # Ignore weight of u when selecting v
+            u_weight = weights[u]
+            weights[u] = 0
+            v = seed.choice(nodes, p=weights / (total_weight - u_weight))
+            weights[u] = u_weight
+
+        G.add_edge(u, v)
+        weights[v] += 1
+        total_weight += 1
+        out_strengths[u] += 1
+        if out_strengths[u] == k:
+            remaining_mask[u] = False
+    return G
+
+
+@py_random_state(4)
+def _random_k_out_graph_python(n, k, alpha, self_loops=True, seed=None):
+    G = nx.empty_graph(n, create_using=nx.MultiDiGraph)
+    weights = Counter(dict.fromkeys(G, alpha))
+    out_strengths = Counter(dict.fromkeys(G, 0))
+
+    for i in range(k * n):
+        u = seed.choice(list(out_strengths.keys()))
+        # If self-loops are not allowed, make the source node `u` have
+        # weight zero.
+        if not self_loops:
+            uweight = weights.pop(u)
+
+        v = weighted_choice(weights, seed=seed)
+
+        if not self_loops:
+            weights[u] = uweight
+
+        G.add_edge(u, v)
+        weights[v] += 1
+        out_strengths[u] += 1
+        if out_strengths[u] == k:
+            out_strengths.pop(u)
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/duplication.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/duplication.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c3ade63f58237eeb927ff631b25f025d7d83fc1
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/duplication.py
@@ -0,0 +1,174 @@
+"""Functions for generating graphs based on the "duplication" method.
+
+These graph generators start with a small initial graph then duplicate
+nodes and (partially) duplicate their edges. These functions are
+generally inspired by biological networks.
+
+"""
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.utils import py_random_state
+from networkx.utils.misc import check_create_using
+
+__all__ = ["partial_duplication_graph", "duplication_divergence_graph"]
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def partial_duplication_graph(N, n, p, q, seed=None, *, create_using=None):
+    """Returns a random graph using the partial duplication model.
+
+    Parameters
+    ----------
+    N : int
+        The total number of nodes in the final graph.
+
+    n : int
+        The number of nodes in the initial clique.
+
+    p : float
+        The probability of joining each neighbor of a node to the
+        duplicate node. Must be a number in the between zero and one,
+        inclusive.
+
+    q : float
+        The probability of joining the source node to the duplicate
+        node. Must be a number in the between zero and one, inclusive.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    A graph of nodes is grown by creating a fully connected graph
+    of size `n`. The following procedure is then repeated until
+    a total of `N` nodes have been reached.
+
+    1. A random node, *u*, is picked and a new node, *v*, is created.
+    2. For each neighbor of *u* an edge from the neighbor to *v* is created
+       with probability `p`.
+    3. An edge from *u* to *v* is created with probability `q`.
+
+    This algorithm appears in [1].
+
+    This implementation allows the possibility of generating
+    disconnected graphs.
+
+    References
+    ----------
+    .. [1] Knudsen Michael, and Carsten Wiuf. "A Markov chain approach to
+           randomly grown graphs." Journal of Applied Mathematics 2008.
+           <https://doi.org/10.1155/2008/190836>
+
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if p < 0 or p > 1 or q < 0 or q > 1:
+        msg = "partial duplication graph must have 0 <= p, q <= 1."
+        raise NetworkXError(msg)
+    if n > N:
+        raise NetworkXError("partial duplication graph must have n <= N.")
+
+    G = nx.complete_graph(n, create_using)
+    for new_node in range(n, N):
+        # Pick a random vertex, u, already in the graph.
+        src_node = seed.randint(0, new_node - 1)
+
+        # Add a new vertex, v, to the graph.
+        G.add_node(new_node)
+
+        # For each neighbor of u...
+        for nbr_node in list(nx.all_neighbors(G, src_node)):
+            # Add the neighbor to v with probability p.
+            if seed.random() < p:
+                G.add_edge(new_node, nbr_node)
+
+        # Join v and u with probability q.
+        if seed.random() < q:
+            G.add_edge(new_node, src_node)
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def duplication_divergence_graph(n, p, seed=None, *, create_using=None):
+    """Returns an undirected graph using the duplication-divergence model.
+
+    A graph of `n` nodes is created by duplicating the initial nodes
+    and retaining edges incident to the original nodes with a retention
+    probability `p`.
+
+    Parameters
+    ----------
+    n : int
+        The desired number of nodes in the graph.
+    p : float
+        The probability for retaining the edge of the replicated node.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `p` is not a valid probability.
+        If `n` is less than 2.
+
+    Notes
+    -----
+    This algorithm appears in [1].
+
+    This implementation disallows the possibility of generating
+    disconnected graphs.
+
+    References
+    ----------
+    .. [1] I. Ispolatov, P. L. Krapivsky, A. Yuryev,
+       "Duplication-divergence model of protein interaction network",
+       Phys. Rev. E, 71, 061911, 2005.
+
+    """
+    if p > 1 or p < 0:
+        msg = f"NetworkXError p={p} is not in [0,1]."
+        raise nx.NetworkXError(msg)
+    if n < 2:
+        msg = "n must be greater than or equal to 2"
+        raise nx.NetworkXError(msg)
+
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    G = nx.empty_graph(create_using=create_using)
+
+    # Initialize the graph with two connected nodes.
+    G.add_edge(0, 1)
+    i = 2
+    while i < n:
+        # Choose a random node from current graph to duplicate.
+        random_node = seed.choice(list(G))
+        # Make the replica.
+        G.add_node(i)
+        # flag indicates whether at least one edge is connected on the replica.
+        flag = False
+        for nbr in G.neighbors(random_node):
+            if seed.random() < p:
+                # Link retention step.
+                G.add_edge(i, nbr)
+                flag = True
+        if not flag:
+            # Delete replica if no edges retained.
+            G.remove_node(i)
+        else:
+            # Successful duplication.
+            i += 1
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/ego.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/ego.py
new file mode 100644
index 0000000000000000000000000000000000000000..91ff3e3fe8573e0764094d030a8656fafa59c929
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/ego.py
@@ -0,0 +1,66 @@
+"""
+Ego graph.
+"""
+
+__all__ = ["ego_graph"]
+
+import networkx as nx
+
+
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def ego_graph(G, n, radius=1, center=True, undirected=False, distance=None):
+    """Returns induced subgraph of neighbors centered at node n within
+    a given radius.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX Graph or DiGraph
+
+    n : node
+      A single node
+
+    radius : number, optional
+      Include all neighbors of distance<=radius from n.
+
+    center : bool, optional
+      If False, do not include center node in graph
+
+    undirected : bool, optional
+      If True use both in- and out-neighbors of directed graphs.
+
+    distance : key, optional
+      Use specified edge data key as distance.  For example, setting
+      distance='weight' will use the edge weight to measure the
+      distance from the node n.
+
+    Notes
+    -----
+    For directed graphs D this produces the "out" neighborhood
+    or successors.  If you want the neighborhood of predecessors
+    first reverse the graph with D.reverse().  If you want both
+    directions use the keyword argument undirected=True.
+
+    Node, edge, and graph attributes are copied to the returned subgraph.
+    """
+    if undirected:
+        if distance is not None:
+            sp, _ = nx.single_source_dijkstra(
+                G.to_undirected(), n, cutoff=radius, weight=distance
+            )
+        else:
+            sp = dict(
+                nx.single_source_shortest_path_length(
+                    G.to_undirected(), n, cutoff=radius
+                )
+            )
+    else:
+        if distance is not None:
+            sp, _ = nx.single_source_dijkstra(G, n, cutoff=radius, weight=distance)
+        else:
+            sp = nx.single_source_shortest_path_length(G, n, cutoff=radius)
+
+    H = G.subgraph(sp).copy()
+    if not center:
+        H.remove_node(n)
+    return H
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/expanders.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/expanders.py
new file mode 100644
index 0000000000000000000000000000000000000000..65db764773ff59febb26a23192883abe33c8b7a4
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/expanders.py
@@ -0,0 +1,476 @@
+"""Provides explicit constructions of expander graphs."""
+
+import itertools
+
+import networkx as nx
+
+__all__ = [
+    "margulis_gabber_galil_graph",
+    "chordal_cycle_graph",
+    "paley_graph",
+    "maybe_regular_expander",
+    "is_regular_expander",
+    "random_regular_expander_graph",
+]
+
+
+# Other discrete torus expanders can be constructed by using the following edge
+# sets. For more information, see Chapter 4, "Expander Graphs", in
+# "Pseudorandomness", by Salil Vadhan.
+#
+# For a directed expander, add edges from (x, y) to:
+#
+#     (x, y),
+#     ((x + 1) % n, y),
+#     (x, (y + 1) % n),
+#     (x, (x + y) % n),
+#     (-y % n, x)
+#
+# For an undirected expander, add the reverse edges.
+#
+# Also appearing in the paper of Gabber and Galil:
+#
+#     (x, y),
+#     (x, (x + y) % n),
+#     (x, (x + y + 1) % n),
+#     ((x + y) % n, y),
+#     ((x + y + 1) % n, y)
+#
+# and:
+#
+#     (x, y),
+#     ((x + 2*y) % n, y),
+#     ((x + (2*y + 1)) % n, y),
+#     ((x + (2*y + 2)) % n, y),
+#     (x, (y + 2*x) % n),
+#     (x, (y + (2*x + 1)) % n),
+#     (x, (y + (2*x + 2)) % n),
+#
+@nx._dispatchable(graphs=None, returns_graph=True)
+def margulis_gabber_galil_graph(n, create_using=None):
+    r"""Returns the Margulis-Gabber-Galil undirected MultiGraph on `n^2` nodes.
+
+    The undirected MultiGraph is regular with degree `8`. Nodes are integer
+    pairs. The second-largest eigenvalue of the adjacency matrix of the graph
+    is at most `5 \sqrt{2}`, regardless of `n`.
+
+    Parameters
+    ----------
+    n : int
+        Determines the number of nodes in the graph: `n^2`.
+    create_using : NetworkX graph constructor, optional (default MultiGraph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : graph
+        The constructed undirected multigraph.
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is directed or not a multigraph.
+
+    """
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed() or not G.is_multigraph():
+        msg = "`create_using` must be an undirected multigraph."
+        raise nx.NetworkXError(msg)
+
+    for x, y in itertools.product(range(n), repeat=2):
+        for u, v in (
+            ((x + 2 * y) % n, y),
+            ((x + (2 * y + 1)) % n, y),
+            (x, (y + 2 * x) % n),
+            (x, (y + (2 * x + 1)) % n),
+        ):
+            G.add_edge((x, y), (u, v))
+    G.graph["name"] = f"margulis_gabber_galil_graph({n})"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def chordal_cycle_graph(p, create_using=None):
+    """Returns the chordal cycle graph on `p` nodes.
+
+    The returned graph is a cycle graph on `p` nodes with chords joining each
+    vertex `x` to its inverse modulo `p`. This graph is a (mildly explicit)
+    3-regular expander [1]_.
+
+    `p` *must* be a prime number.
+
+    Parameters
+    ----------
+    p : a prime number
+
+        The number of vertices in the graph. This also indicates where the
+        chordal edges in the cycle will be created.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : graph
+        The constructed undirected multigraph.
+
+    Raises
+    ------
+    NetworkXError
+
+        If `create_using` indicates directed or not a multigraph.
+
+    References
+    ----------
+
+    .. [1] Theorem 4.4.2 in A. Lubotzky. "Discrete groups, expanding graphs and
+           invariant measures", volume 125 of Progress in Mathematics.
+           Birkhäuser Verlag, Basel, 1994.
+
+    """
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed() or not G.is_multigraph():
+        msg = "`create_using` must be an undirected multigraph."
+        raise nx.NetworkXError(msg)
+
+    for x in range(p):
+        left = (x - 1) % p
+        right = (x + 1) % p
+        # Here we apply Fermat's Little Theorem to compute the multiplicative
+        # inverse of x in Z/pZ. By Fermat's Little Theorem,
+        #
+        #     x^p = x (mod p)
+        #
+        # Therefore,
+        #
+        #     x * x^(p - 2) = 1 (mod p)
+        #
+        # The number 0 is a special case: we just let its inverse be itself.
+        chord = pow(x, p - 2, p) if x > 0 else 0
+        for y in (left, right, chord):
+            G.add_edge(x, y)
+    G.graph["name"] = f"chordal_cycle_graph({p})"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def paley_graph(p, create_using=None):
+    r"""Returns the Paley $\frac{(p-1)}{2}$ -regular graph on $p$ nodes.
+
+    The returned graph is a graph on $\mathbb{Z}/p\mathbb{Z}$ with edges between $x$ and $y$
+    if and only if $x-y$ is a nonzero square in $\mathbb{Z}/p\mathbb{Z}$.
+
+    If $p \equiv 1  \pmod 4$, $-1$ is a square in
+    $\mathbb{Z}/p\mathbb{Z}$ and therefore $x-y$ is a square if and
+    only if $y-x$ is also a square, i.e the edges in the Paley graph are symmetric.
+
+    If $p \equiv 3 \pmod 4$, $-1$ is not a square in $\mathbb{Z}/p\mathbb{Z}$
+    and therefore either $x-y$ or $y-x$ is a square in $\mathbb{Z}/p\mathbb{Z}$ but not both.
+
+    Note that a more general definition of Paley graphs extends this construction
+    to graphs over $q=p^n$ vertices, by using the finite field $F_q$ instead of
+    $\mathbb{Z}/p\mathbb{Z}$.
+    This construction requires to compute squares in general finite fields and is
+    not what is implemented here (i.e `paley_graph(25)` does not return the true
+    Paley graph associated with $5^2$).
+
+    Parameters
+    ----------
+    p : int, an odd prime number.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : graph
+        The constructed directed graph.
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is a multigraph.
+
+    References
+    ----------
+    Chapter 13 in B. Bollobas, Random Graphs. Second edition.
+    Cambridge Studies in Advanced Mathematics, 73.
+    Cambridge University Press, Cambridge (2001).
+    """
+    G = nx.empty_graph(0, create_using, default=nx.DiGraph)
+    if G.is_multigraph():
+        msg = "`create_using` cannot be a multigraph."
+        raise nx.NetworkXError(msg)
+
+    # Compute the squares in Z/pZ.
+    # Make it a set to uniquify (there are exactly (p-1)/2 squares in Z/pZ
+    # when is prime).
+    square_set = {(x**2) % p for x in range(1, p) if (x**2) % p != 0}
+
+    for x in range(p):
+        for x2 in square_set:
+            G.add_edge(x, (x + x2) % p)
+    G.graph["name"] = f"paley({p})"
+    return G
+
+
+@nx.utils.decorators.np_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def maybe_regular_expander(n, d, *, create_using=None, max_tries=100, seed=None):
+    r"""Utility for creating a random regular expander.
+
+    Returns a random $d$-regular graph on $n$ nodes which is an expander
+    graph with very good probability.
+
+    Parameters
+    ----------
+    n : int
+      The number of nodes.
+    d : int
+      The degree of each node.
+    create_using : Graph Instance or Constructor
+      Indicator of type of graph to return.
+      If a Graph-type instance, then clear and use it.
+      If a constructor, call it to create an empty graph.
+      Use the Graph constructor by default.
+    max_tries : int. (default: 100)
+      The number of allowed loops when generating each independent cycle
+    seed : (default: None)
+      Seed used to set random number generation state. See :ref`Randomness<randomness>`.
+
+    Notes
+    -----
+    The nodes are numbered from $0$ to $n - 1$.
+
+    The graph is generated by taking $d / 2$ random independent cycles.
+
+    Joel Friedman proved that in this model the resulting
+    graph is an expander with probability
+    $1 - O(n^{-\tau})$ where $\tau = \lceil (\sqrt{d - 1}) / 2 \rceil - 1$. [1]_
+
+    Examples
+    --------
+    >>> G = nx.maybe_regular_expander(n=200, d=6, seed=8020)
+
+    Returns
+    -------
+    G : graph
+        The constructed undirected graph.
+
+    Raises
+    ------
+    NetworkXError
+        If $d % 2 != 0$ as the degree must be even.
+        If $n - 1$ is less than $ 2d $ as the graph is complete at most.
+        If max_tries is reached
+
+    See Also
+    --------
+    is_regular_expander
+    random_regular_expander_graph
+
+    References
+    ----------
+    .. [1] Joel Friedman,
+       A Proof of Alon's Second Eigenvalue Conjecture and Related Problems, 2004
+       https://arxiv.org/abs/cs/0405020
+
+    """
+
+    import numpy as np
+
+    if n < 1:
+        raise nx.NetworkXError("n must be a positive integer")
+
+    if not (d >= 2):
+        raise nx.NetworkXError("d must be greater than or equal to 2")
+
+    if not (d % 2 == 0):
+        raise nx.NetworkXError("d must be even")
+
+    if not (n - 1 >= d):
+        raise nx.NetworkXError(
+            f"Need n-1>= d to have room for {d // 2} independent cycles with {n} nodes"
+        )
+
+    G = nx.empty_graph(n, create_using)
+
+    if n < 2:
+        return G
+
+    cycles = []
+    edges = set()
+
+    # Create d / 2 cycles
+    for i in range(d // 2):
+        iterations = max_tries
+        # Make sure the cycles are independent to have a regular graph
+        while len(edges) != (i + 1) * n:
+            iterations -= 1
+            # Faster than random.permutation(n) since there are only
+            # (n-1)! distinct cycles against n! permutations of size n
+            cycle = seed.permutation(n - 1).tolist()
+            cycle.append(n - 1)
+
+            new_edges = {
+                (u, v)
+                for u, v in nx.utils.pairwise(cycle, cyclic=True)
+                if (u, v) not in edges and (v, u) not in edges
+            }
+            # If the new cycle has no edges in common with previous cycles
+            # then add it to the list otherwise try again
+            if len(new_edges) == n:
+                cycles.append(cycle)
+                edges.update(new_edges)
+
+            if iterations == 0:
+                raise nx.NetworkXError("Too many iterations in maybe_regular_expander")
+
+    G.add_edges_from(edges)
+
+    return G
+
+
+@nx.utils.not_implemented_for("directed")
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable(preserve_edge_attrs={"G": {"weight": 1}})
+def is_regular_expander(G, *, epsilon=0):
+    r"""Determines whether the graph G is a regular expander. [1]_
+
+    An expander graph is a sparse graph with strong connectivity properties.
+
+    More precisely, this helper checks whether the graph is a
+    regular $(n, d, \lambda)$-expander with $\lambda$ close to
+    the Alon-Boppana bound and given by
+    $\lambda = 2 \sqrt{d - 1} + \epsilon$. [2]_
+
+    In the case where $\epsilon = 0$ then if the graph successfully passes the test
+    it is a Ramanujan graph. [3]_
+
+    A Ramanujan graph has spectral gap almost as large as possible, which makes them
+    excellent expanders.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+    epsilon : int, float, default=0
+
+    Returns
+    -------
+    bool
+        Whether the given graph is a regular $(n, d, \lambda)$-expander
+        where $\lambda = 2 \sqrt{d - 1} + \epsilon$.
+
+    Examples
+    --------
+    >>> G = nx.random_regular_expander_graph(20, 4)
+    >>> nx.is_regular_expander(G)
+    True
+
+    See Also
+    --------
+    maybe_regular_expander
+    random_regular_expander_graph
+
+    References
+    ----------
+    .. [1] Expander graph, https://en.wikipedia.org/wiki/Expander_graph
+    .. [2] Alon-Boppana bound, https://en.wikipedia.org/wiki/Alon%E2%80%93Boppana_bound
+    .. [3] Ramanujan graphs, https://en.wikipedia.org/wiki/Ramanujan_graph
+
+    """
+
+    import numpy as np
+    import scipy as sp
+
+    if epsilon < 0:
+        raise nx.NetworkXError("epsilon must be non negative")
+
+    if not nx.is_regular(G):
+        return False
+
+    _, d = nx.utils.arbitrary_element(G.degree)
+
+    A = nx.adjacency_matrix(G, dtype=float)
+    lams = sp.sparse.linalg.eigsh(A, which="LM", k=2, return_eigenvectors=False)
+
+    # lambda2 is the second biggest eigenvalue
+    lambda2 = min(lams)
+
+    # Use bool() to convert numpy scalar to Python Boolean
+    return bool(abs(lambda2) < 2 * np.sqrt(d - 1) + epsilon)
+
+
+@nx.utils.decorators.np_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_regular_expander_graph(
+    n, d, *, epsilon=0, create_using=None, max_tries=100, seed=None
+):
+    r"""Returns a random regular expander graph on $n$ nodes with degree $d$.
+
+    An expander graph is a sparse graph with strong connectivity properties. [1]_
+
+    More precisely the returned graph is a $(n, d, \lambda)$-expander with
+    $\lambda = 2 \sqrt{d - 1} + \epsilon$, close to the Alon-Boppana bound. [2]_
+
+    In the case where $\epsilon = 0$ it returns a Ramanujan graph.
+    A Ramanujan graph has spectral gap almost as large as possible,
+    which makes them excellent expanders. [3]_
+
+    Parameters
+    ----------
+    n : int
+      The number of nodes.
+    d : int
+      The degree of each node.
+    epsilon : int, float, default=0
+    max_tries : int, (default: 100)
+      The number of allowed loops, also used in the maybe_regular_expander utility
+    seed : (default: None)
+      Seed used to set random number generation state. See :ref`Randomness<randomness>`.
+
+    Raises
+    ------
+    NetworkXError
+        If max_tries is reached
+
+    Examples
+    --------
+    >>> G = nx.random_regular_expander_graph(20, 4)
+    >>> nx.is_regular_expander(G)
+    True
+
+    Notes
+    -----
+    This loops over `maybe_regular_expander` and can be slow when
+    $n$ is too big or $\epsilon$ too small.
+
+    See Also
+    --------
+    maybe_regular_expander
+    is_regular_expander
+
+    References
+    ----------
+    .. [1] Expander graph, https://en.wikipedia.org/wiki/Expander_graph
+    .. [2] Alon-Boppana bound, https://en.wikipedia.org/wiki/Alon%E2%80%93Boppana_bound
+    .. [3] Ramanujan graphs, https://en.wikipedia.org/wiki/Ramanujan_graph
+
+    """
+    G = maybe_regular_expander(
+        n, d, create_using=create_using, max_tries=max_tries, seed=seed
+    )
+    iterations = max_tries
+
+    while not is_regular_expander(G, epsilon=epsilon):
+        iterations -= 1
+        G = maybe_regular_expander(
+            n=n, d=d, create_using=create_using, max_tries=max_tries, seed=seed
+        )
+
+        if iterations == 0:
+            raise nx.NetworkXError(
+                "Too many iterations in random_regular_expander_graph"
+            )
+
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/geometric.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/geometric.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdd4f627b46a1f0fbd2f6cd1700aecb12549516a
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/geometric.py
@@ -0,0 +1,1037 @@
+"""Generators for geometric graphs."""
+
+import math
+from bisect import bisect_left
+from itertools import accumulate, combinations, product
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "geometric_edges",
+    "geographical_threshold_graph",
+    "navigable_small_world_graph",
+    "random_geometric_graph",
+    "soft_random_geometric_graph",
+    "thresholded_random_geometric_graph",
+    "waxman_graph",
+    "geometric_soft_configuration_graph",
+]
+
+
+@nx._dispatchable(node_attrs="pos_name")
+def geometric_edges(G, radius, p=2, *, pos_name="pos"):
+    """Returns edge list of node pairs within `radius` of each other.
+
+    Parameters
+    ----------
+    G : networkx graph
+        The graph from which to generate the edge list. The nodes in `G` should
+        have an attribute ``pos`` corresponding to the node position, which is
+        used to compute the distance to other nodes.
+    radius : scalar
+        The distance threshold. Edges are included in the edge list if the
+        distance between the two nodes is less than `radius`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position of each
+        node in 2D coordinates. Every node in the Graph must have this attribute.
+    p : scalar, default=2
+        The `Minkowski distance metric
+        <https://en.wikipedia.org/wiki/Minkowski_distance>`_ used to compute
+        distances. The default value is 2, i.e. Euclidean distance.
+
+    Returns
+    -------
+    edges : list
+        List of edges whose distances are less than `radius`
+
+    Notes
+    -----
+    Radius uses Minkowski distance metric `p`.
+    If scipy is available, `scipy.spatial.cKDTree` is used to speed computation.
+
+    Examples
+    --------
+    Create a graph with nodes that have a "pos" attribute representing 2D
+    coordinates.
+
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from(
+    ...     [
+    ...         (0, {"pos": (0, 0)}),
+    ...         (1, {"pos": (3, 0)}),
+    ...         (2, {"pos": (8, 0)}),
+    ...     ]
+    ... )
+    >>> nx.geometric_edges(G, radius=1)
+    []
+    >>> nx.geometric_edges(G, radius=4)
+    [(0, 1)]
+    >>> nx.geometric_edges(G, radius=6)
+    [(0, 1), (1, 2)]
+    >>> nx.geometric_edges(G, radius=9)
+    [(0, 1), (0, 2), (1, 2)]
+    """
+    # Input validation - every node must have a "pos" attribute
+    for n, pos in G.nodes(data=pos_name):
+        if pos is None:
+            raise nx.NetworkXError(
+                f"Node {n} (and all nodes) must have a '{pos_name}' attribute."
+            )
+
+    # NOTE: See _geometric_edges for the actual implementation. The reason this
+    # is split into two functions is to avoid the overhead of input validation
+    # every time the function is called internally in one of the other
+    # geometric generators
+    return _geometric_edges(G, radius, p, pos_name)
+
+
+def _geometric_edges(G, radius, p, pos_name):
+    """
+    Implements `geometric_edges` without input validation. See `geometric_edges`
+    for complete docstring.
+    """
+    nodes_pos = G.nodes(data=pos_name)
+    try:
+        import scipy as sp
+    except ImportError:
+        # no scipy KDTree so compute by for-loop
+        radius_p = radius**p
+        edges = [
+            (u, v)
+            for (u, pu), (v, pv) in combinations(nodes_pos, 2)
+            if sum(abs(a - b) ** p for a, b in zip(pu, pv)) <= radius_p
+        ]
+        return edges
+    # scipy KDTree is available
+    nodes, coords = list(zip(*nodes_pos))
+    kdtree = sp.spatial.cKDTree(coords)  # Cannot provide generator.
+    edge_indexes = kdtree.query_pairs(radius, p)
+    edges = [(nodes[u], nodes[v]) for u, v in sorted(edge_indexes)]
+    return edges
+
+
+@py_random_state(5)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_geometric_graph(
+    n, radius, dim=2, pos=None, p=2, seed=None, *, pos_name="pos"
+):
+    """Returns a random geometric graph in the unit cube of dimensions `dim`.
+
+    The random geometric graph model places `n` nodes uniformly at
+    random in the unit cube. Two nodes are joined by an edge if the
+    distance between the nodes is at most `radius`.
+
+    Edges are determined using a KDTree when SciPy is available.
+    This reduces the time complexity from $O(n^2)$ to $O(n)$.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    radius: float
+        Distance threshold value
+    dim : int, optional
+        Dimension of graph
+    pos : dict, optional
+        A dictionary keyed by node with node positions as values.
+    p : float, optional
+        Which Minkowski distance metric to use.  `p` has to meet the condition
+        ``1 <= p <= infinity``.
+
+        If this argument is not specified, the :math:`L^2` metric
+        (the Euclidean distance metric), p = 2 is used.
+        This should not be confused with the `p` of an Erdős-Rényi random
+        graph, which represents probability.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A random geometric graph, undirected and without self-loops.
+        Each node has a node attribute ``'pos'`` that stores the
+        position of that node in Euclidean space as provided by the
+        ``pos`` keyword argument or, if ``pos`` was not provided, as
+        generated by this function.
+
+    Examples
+    --------
+    Create a random geometric graph on twenty nodes where nodes are joined by
+    an edge if their distance is at most 0.1::
+
+    >>> G = nx.random_geometric_graph(20, 0.1)
+
+    Notes
+    -----
+    This uses a *k*-d tree to build the graph.
+
+    The `pos` keyword argument can be used to specify node positions so you
+    can create an arbitrary distribution and domain for positions.
+
+    For example, to use a 2D Gaussian distribution of node positions with mean
+    (0, 0) and standard deviation 2::
+
+    >>> import random
+    >>> n = 20
+    >>> pos = {i: (random.gauss(0, 2), random.gauss(0, 2)) for i in range(n)}
+    >>> G = nx.random_geometric_graph(n, 0.2, pos=pos)
+
+    References
+    ----------
+    .. [1] Penrose, Mathew, *Random Geometric Graphs*,
+           Oxford Studies in Probability, 5, 2003.
+
+    """
+    # TODO Is this function just a special case of the geographical
+    # threshold graph?
+    #
+    #     half_radius = {v: radius / 2 for v in n}
+    #     return geographical_threshold_graph(nodes, theta=1, alpha=1,
+    #                                         weight=half_radius)
+    #
+    G = nx.empty_graph(n)
+    # If no positions are provided, choose uniformly random vectors in
+    # Euclidean space of the specified dimension.
+    if pos is None:
+        pos = {v: [seed.random() for i in range(dim)] for v in G}
+    nx.set_node_attributes(G, pos, pos_name)
+
+    G.add_edges_from(_geometric_edges(G, radius, p, pos_name))
+    return G
+
+
+@py_random_state(6)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def soft_random_geometric_graph(
+    n, radius, dim=2, pos=None, p=2, p_dist=None, seed=None, *, pos_name="pos"
+):
+    r"""Returns a soft random geometric graph in the unit cube.
+
+    The soft random geometric graph [1] model places `n` nodes uniformly at
+    random in the unit cube in dimension `dim`. Two nodes of distance, `dist`,
+    computed by the `p`-Minkowski distance metric are joined by an edge with
+    probability `p_dist` if the computed distance metric value of the nodes
+    is at most `radius`, otherwise they are not joined.
+
+    Edges within `radius` of each other are determined using a KDTree when
+    SciPy is available. This reduces the time complexity from :math:`O(n^2)`
+    to :math:`O(n)`.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    radius: float
+        Distance threshold value
+    dim : int, optional
+        Dimension of graph
+    pos : dict, optional
+        A dictionary keyed by node with node positions as values.
+    p : float, optional
+        Which Minkowski distance metric to use.
+        `p` has to meet the condition ``1 <= p <= infinity``.
+
+        If this argument is not specified, the :math:`L^2` metric
+        (the Euclidean distance metric), p = 2 is used.
+
+        This should not be confused with the `p` of an Erdős-Rényi random
+        graph, which represents probability.
+    p_dist : function, optional
+        A probability density function computing the probability of
+        connecting two nodes that are of distance, dist, computed by the
+        Minkowski distance metric. The probability density function, `p_dist`,
+        must be any function that takes the metric value as input
+        and outputs a single probability value between 0-1. The `scipy.stats`
+        package has many probability distribution functions implemented and
+        tools for custom probability distribution definitions [2], and passing
+        the .pdf method of `scipy.stats` distributions can be used here.  If the
+        probability function, `p_dist`, is not supplied, the default function
+        is an exponential distribution with rate parameter :math:`\lambda=1`.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A soft random geometric graph, undirected and without self-loops.
+        Each node has a node attribute ``'pos'`` that stores the
+        position of that node in Euclidean space as provided by the
+        ``pos`` keyword argument or, if ``pos`` was not provided, as
+        generated by this function.
+
+    Notes
+    -----
+    This uses a *k*-d tree to build the graph.
+
+    References
+    ----------
+    .. [1] Penrose, Mathew D. "Connectivity of soft random geometric graphs."
+           The Annals of Applied Probability 26.2 (2016): 986-1028.
+
+    Examples
+    --------
+    Default Graph:
+
+    >>> G = nx.soft_random_geometric_graph(50, 0.2)
+
+    Custom Graph:
+
+    The `pos` keyword argument can be used to specify node positions so you
+    can create an arbitrary distribution and domain for positions.
+
+    The `scipy.stats` package can be used to define the probability distribution
+    with the ``.pdf`` method used as `p_dist`.
+
+    For example, create a soft random geometric graph on 100 nodes using a 2D
+    Gaussian distribution of node positions with mean (0, 0) and standard deviation 2,
+    where nodes are joined by an edge with probability computed from an
+    exponential distribution with rate parameter :math:`\lambda=1` if their
+    Euclidean distance is at most 0.2.
+
+    >>> import random
+    >>> from scipy.stats import expon
+    >>> n = 100
+    >>> pos = {i: (random.gauss(0, 2), random.gauss(0, 2)) for i in range(n)}
+    >>> p_dist = lambda x: expon.pdf(x, scale=1)
+    >>> G = nx.soft_random_geometric_graph(n, 0.2, pos=pos, p_dist=p_dist)
+
+    """
+    G = nx.empty_graph(n)
+    G.name = f"soft_random_geometric_graph({n}, {radius}, {dim})"
+    # If no positions are provided, choose uniformly random vectors in
+    # Euclidean space of the specified dimension.
+    if pos is None:
+        pos = {v: [seed.random() for i in range(dim)] for v in G}
+    nx.set_node_attributes(G, pos, pos_name)
+
+    # if p_dist function not supplied the default function is an exponential
+    # distribution with rate parameter :math:`\lambda=1`.
+    if p_dist is None:
+
+        def p_dist(dist):
+            return math.exp(-dist)
+
+    def should_join(edge):
+        u, v = edge
+        dist = (sum(abs(a - b) ** p for a, b in zip(pos[u], pos[v]))) ** (1 / p)
+        return seed.random() < p_dist(dist)
+
+    G.add_edges_from(filter(should_join, _geometric_edges(G, radius, p, pos_name)))
+    return G
+
+
+@py_random_state(7)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def geographical_threshold_graph(
+    n,
+    theta,
+    dim=2,
+    pos=None,
+    weight=None,
+    metric=None,
+    p_dist=None,
+    seed=None,
+    *,
+    pos_name="pos",
+    weight_name="weight",
+):
+    r"""Returns a geographical threshold graph.
+
+    The geographical threshold graph model places $n$ nodes uniformly at
+    random in a rectangular domain.  Each node $u$ is assigned a weight
+    $w_u$. Two nodes $u$ and $v$ are joined by an edge if
+
+    .. math::
+
+       (w_u + w_v)p_{dist}(r) \ge \theta
+
+    where `r` is the distance between `u` and `v`, `p_dist` is any function of
+    `r`, and :math:`\theta` as the threshold parameter. `p_dist` is used to
+    give weight to the distance between nodes when deciding whether or not
+    they should be connected. The larger `p_dist` is, the more prone nodes
+    separated by `r` are to be connected, and vice versa.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    theta: float
+        Threshold value
+    dim : int, optional
+        Dimension of graph
+    pos : dict
+        Node positions as a dictionary of tuples keyed by node.
+    weight : dict
+        Node weights as a dictionary of numbers keyed by node.
+    metric : function
+        A metric on vectors of numbers (represented as lists or
+        tuples). This must be a function that accepts two lists (or
+        tuples) as input and yields a number as output. The function
+        must also satisfy the four requirements of a `metric`_.
+        Specifically, if $d$ is the function and $x$, $y$,
+        and $z$ are vectors in the graph, then $d$ must satisfy
+
+        1. $d(x, y) \ge 0$,
+        2. $d(x, y) = 0$ if and only if $x = y$,
+        3. $d(x, y) = d(y, x)$,
+        4. $d(x, z) \le d(x, y) + d(y, z)$.
+
+        If this argument is not specified, the Euclidean distance metric is
+        used.
+
+        .. _metric: https://en.wikipedia.org/wiki/Metric_%28mathematics%29
+    p_dist : function, optional
+        Any function used to give weight to the distance between nodes when
+        deciding whether or not they should be connected. `p_dist` was
+        originally conceived as a probability density function giving the
+        probability of connecting two nodes that are of metric distance `r`
+        apart. The implementation here allows for more arbitrary definitions
+        of `p_dist` that do not need to correspond to valid probability
+        density functions. The :mod:`scipy.stats` package has many
+        probability density functions implemented and tools for custom
+        probability density definitions, and passing the ``.pdf`` method of
+        `scipy.stats` distributions can be used here. If ``p_dist=None``
+        (the default), the exponential function :math:`r^{-2}` is used.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+    weight_name : string, default="weight"
+        The name of the node attribute which represents the weight
+        of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A random geographic threshold graph, undirected and without
+        self-loops.
+
+        Each node has a node attribute ``pos`` that stores the
+        position of that node in Euclidean space as provided by the
+        ``pos`` keyword argument or, if ``pos`` was not provided, as
+        generated by this function. Similarly, each node has a node
+        attribute ``weight`` that stores the weight of that node as
+        provided or as generated.
+
+    Examples
+    --------
+    Specify an alternate distance metric using the ``metric`` keyword
+    argument. For example, to use the `taxicab metric`_ instead of the
+    default `Euclidean metric`_::
+
+        >>> dist = lambda x, y: sum(abs(a - b) for a, b in zip(x, y))
+        >>> G = nx.geographical_threshold_graph(10, 0.1, metric=dist)
+
+    .. _taxicab metric: https://en.wikipedia.org/wiki/Taxicab_geometry
+    .. _Euclidean metric: https://en.wikipedia.org/wiki/Euclidean_distance
+
+    Notes
+    -----
+    If weights are not specified they are assigned to nodes by drawing randomly
+    from the exponential distribution with rate parameter $\lambda=1$.
+    To specify weights from a different distribution, use the `weight` keyword
+    argument::
+
+    >>> import random
+    >>> n = 20
+    >>> w = {i: random.expovariate(5.0) for i in range(n)}
+    >>> G = nx.geographical_threshold_graph(20, 50, weight=w)
+
+    If node positions are not specified they are randomly assigned from the
+    uniform distribution.
+
+    References
+    ----------
+    .. [1] Masuda, N., Miwa, H., Konno, N.:
+       Geographical threshold graphs with small-world and scale-free
+       properties.
+       Physical Review E 71, 036108 (2005)
+    .. [2]  Milan Bradonjić, Aric Hagberg and Allon G. Percus,
+       Giant component and connectivity in geographical threshold graphs,
+       in Algorithms and Models for the Web-Graph (WAW 2007),
+       Antony Bonato and Fan Chung (Eds), pp. 209--216, 2007
+    """
+    G = nx.empty_graph(n)
+    # If no weights are provided, choose them from an exponential
+    # distribution.
+    if weight is None:
+        weight = {v: seed.expovariate(1) for v in G}
+    # If no positions are provided, choose uniformly random vectors in
+    # Euclidean space of the specified dimension.
+    if pos is None:
+        pos = {v: [seed.random() for i in range(dim)] for v in G}
+    # If no distance metric is provided, use Euclidean distance.
+    if metric is None:
+        metric = math.dist
+    nx.set_node_attributes(G, weight, weight_name)
+    nx.set_node_attributes(G, pos, pos_name)
+
+    # if p_dist is not supplied, use default r^-2
+    if p_dist is None:
+
+        def p_dist(r):
+            return r**-2
+
+    # Returns ``True`` if and only if the nodes whose attributes are
+    # ``du`` and ``dv`` should be joined, according to the threshold
+    # condition.
+    def should_join(pair):
+        u, v = pair
+        u_pos, v_pos = pos[u], pos[v]
+        u_weight, v_weight = weight[u], weight[v]
+        return (u_weight + v_weight) * p_dist(metric(u_pos, v_pos)) >= theta
+
+    G.add_edges_from(filter(should_join, combinations(G, 2)))
+    return G
+
+
+@py_random_state(6)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def waxman_graph(
+    n,
+    beta=0.4,
+    alpha=0.1,
+    L=None,
+    domain=(0, 0, 1, 1),
+    metric=None,
+    seed=None,
+    *,
+    pos_name="pos",
+):
+    r"""Returns a Waxman random graph.
+
+    The Waxman random graph model places `n` nodes uniformly at random
+    in a rectangular domain. Each pair of nodes at distance `d` is
+    joined by an edge with probability
+
+    .. math::
+            p = \beta \exp(-d / \alpha L).
+
+    This function implements both Waxman models, using the `L` keyword
+    argument.
+
+    * Waxman-1: if `L` is not specified, it is set to be the maximum distance
+      between any pair of nodes.
+    * Waxman-2: if `L` is specified, the distance between a pair of nodes is
+      chosen uniformly at random from the interval `[0, L]`.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    beta: float
+        Model parameter
+    alpha: float
+        Model parameter
+    L : float, optional
+        Maximum distance between nodes.  If not specified, the actual distance
+        is calculated.
+    domain : four-tuple of numbers, optional
+        Domain size, given as a tuple of the form `(x_min, y_min, x_max,
+        y_max)`.
+    metric : function
+        A metric on vectors of numbers (represented as lists or
+        tuples). This must be a function that accepts two lists (or
+        tuples) as input and yields a number as output. The function
+        must also satisfy the four requirements of a `metric`_.
+        Specifically, if $d$ is the function and $x$, $y$,
+        and $z$ are vectors in the graph, then $d$ must satisfy
+
+        1. $d(x, y) \ge 0$,
+        2. $d(x, y) = 0$ if and only if $x = y$,
+        3. $d(x, y) = d(y, x)$,
+        4. $d(x, z) \le d(x, y) + d(y, z)$.
+
+        If this argument is not specified, the Euclidean distance metric is
+        used.
+
+        .. _metric: https://en.wikipedia.org/wiki/Metric_%28mathematics%29
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A random Waxman graph, undirected and without self-loops. Each
+        node has a node attribute ``'pos'`` that stores the position of
+        that node in Euclidean space as generated by this function.
+
+    Examples
+    --------
+    Specify an alternate distance metric using the ``metric`` keyword
+    argument. For example, to use the "`taxicab metric`_" instead of the
+    default `Euclidean metric`_::
+
+        >>> dist = lambda x, y: sum(abs(a - b) for a, b in zip(x, y))
+        >>> G = nx.waxman_graph(10, 0.5, 0.1, metric=dist)
+
+    .. _taxicab metric: https://en.wikipedia.org/wiki/Taxicab_geometry
+    .. _Euclidean metric: https://en.wikipedia.org/wiki/Euclidean_distance
+
+    Notes
+    -----
+    Starting in NetworkX 2.0 the parameters alpha and beta align with their
+    usual roles in the probability distribution. In earlier versions their
+    positions in the expression were reversed. Their position in the calling
+    sequence reversed as well to minimize backward incompatibility.
+
+    References
+    ----------
+    .. [1]  B. M. Waxman, *Routing of multipoint connections*.
+       IEEE J. Select. Areas Commun. 6(9),(1988) 1617--1622.
+    """
+    G = nx.empty_graph(n)
+    (xmin, ymin, xmax, ymax) = domain
+    # Each node gets a uniformly random position in the given rectangle.
+    pos = {v: (seed.uniform(xmin, xmax), seed.uniform(ymin, ymax)) for v in G}
+    nx.set_node_attributes(G, pos, pos_name)
+    # If no distance metric is provided, use Euclidean distance.
+    if metric is None:
+        metric = math.dist
+    # If the maximum distance L is not specified (that is, we are in the
+    # Waxman-1 model), then find the maximum distance between any pair
+    # of nodes.
+    #
+    # In the Waxman-1 model, join nodes randomly based on distance. In
+    # the Waxman-2 model, join randomly based on random l.
+    if L is None:
+        L = max(metric(x, y) for x, y in combinations(pos.values(), 2))
+
+        def dist(u, v):
+            return metric(pos[u], pos[v])
+
+    else:
+
+        def dist(u, v):
+            return seed.random() * L
+
+    # `pair` is the pair of nodes to decide whether to join.
+    def should_join(pair):
+        return seed.random() < beta * math.exp(-dist(*pair) / (alpha * L))
+
+    G.add_edges_from(filter(should_join, combinations(G, 2)))
+    return G
+
+
+@py_random_state(5)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def navigable_small_world_graph(n, p=1, q=1, r=2, dim=2, seed=None):
+    r"""Returns a navigable small-world graph.
+
+    A navigable small-world graph is a directed grid with additional long-range
+    connections that are chosen randomly.
+
+      [...] we begin with a set of nodes [...] that are identified with the set
+      of lattice points in an $n \times n$ square,
+      $\{(i, j): i \in \{1, 2, \ldots, n\}, j \in \{1, 2, \ldots, n\}\}$,
+      and we define the *lattice distance* between two nodes $(i, j)$ and
+      $(k, l)$ to be the number of "lattice steps" separating them:
+      $d((i, j), (k, l)) = |k - i| + |l - j|$.
+
+      For a universal constant $p >= 1$, the node $u$ has a directed edge to
+      every other node within lattice distance $p$---these are its *local
+      contacts*. For universal constants $q >= 0$ and $r >= 0$ we also
+      construct directed edges from $u$ to $q$ other nodes (the *long-range
+      contacts*) using independent random trials; the $i$th directed edge from
+      $u$ has endpoint $v$ with probability proportional to $[d(u,v)]^{-r}$.
+
+      -- [1]_
+
+    Parameters
+    ----------
+    n : int
+        The length of one side of the lattice; the number of nodes in
+        the graph is therefore $n^2$.
+    p : int
+        The diameter of short range connections. Each node is joined with every
+        other node within this lattice distance.
+    q : int
+        The number of long-range connections for each node.
+    r : float
+        Exponent for decaying probability of connections.  The probability of
+        connecting to a node at lattice distance $d$ is $1/d^r$.
+    dim : int
+        Dimension of grid
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    References
+    ----------
+    .. [1] J. Kleinberg. The small-world phenomenon: An algorithmic
+       perspective. Proc. 32nd ACM Symposium on Theory of Computing, 2000.
+    """
+    if p < 1:
+        raise nx.NetworkXException("p must be >= 1")
+    if q < 0:
+        raise nx.NetworkXException("q must be >= 0")
+    if r < 0:
+        raise nx.NetworkXException("r must be >= 0")
+
+    G = nx.DiGraph()
+    nodes = list(product(range(n), repeat=dim))
+    for p1 in nodes:
+        probs = [0]
+        for p2 in nodes:
+            if p1 == p2:
+                continue
+            d = sum((abs(b - a) for a, b in zip(p1, p2)))
+            if d <= p:
+                G.add_edge(p1, p2)
+            probs.append(d**-r)
+        cdf = list(accumulate(probs))
+        for _ in range(q):
+            target = nodes[bisect_left(cdf, seed.uniform(0, cdf[-1]))]
+            G.add_edge(p1, target)
+    return G
+
+
+@py_random_state(7)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def thresholded_random_geometric_graph(
+    n,
+    radius,
+    theta,
+    dim=2,
+    pos=None,
+    weight=None,
+    p=2,
+    seed=None,
+    *,
+    pos_name="pos",
+    weight_name="weight",
+):
+    r"""Returns a thresholded random geometric graph in the unit cube.
+
+    The thresholded random geometric graph [1] model places `n` nodes
+    uniformly at random in the unit cube of dimensions `dim`. Each node
+    `u` is assigned a weight :math:`w_u`. Two nodes `u` and `v` are
+    joined by an edge if they are within the maximum connection distance,
+    `radius` computed by the `p`-Minkowski distance and the summation of
+    weights :math:`w_u` + :math:`w_v` is greater than or equal
+    to the threshold parameter `theta`.
+
+    Edges within `radius` of each other are determined using a KDTree when
+    SciPy is available. This reduces the time complexity from :math:`O(n^2)`
+    to :math:`O(n)`.
+
+    Parameters
+    ----------
+    n : int or iterable
+        Number of nodes or iterable of nodes
+    radius: float
+        Distance threshold value
+    theta: float
+        Threshold value
+    dim : int, optional
+        Dimension of graph
+    pos : dict, optional
+        A dictionary keyed by node with node positions as values.
+    weight : dict, optional
+        Node weights as a dictionary of numbers keyed by node.
+    p : float, optional (default 2)
+        Which Minkowski distance metric to use.  `p` has to meet the condition
+        ``1 <= p <= infinity``.
+
+        If this argument is not specified, the :math:`L^2` metric
+        (the Euclidean distance metric), p = 2 is used.
+
+        This should not be confused with the `p` of an Erdős-Rényi random
+        graph, which represents probability.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    pos_name : string, default="pos"
+        The name of the node attribute which represents the position
+        in 2D coordinates of the node in the returned graph.
+    weight_name : string, default="weight"
+        The name of the node attribute which represents the weight
+        of the node in the returned graph.
+
+    Returns
+    -------
+    Graph
+        A thresholded random geographic graph, undirected and without
+        self-loops.
+
+        Each node has a node attribute ``'pos'`` that stores the
+        position of that node in Euclidean space as provided by the
+        ``pos`` keyword argument or, if ``pos`` was not provided, as
+        generated by this function. Similarly, each node has a nodethre
+        attribute ``'weight'`` that stores the weight of that node as
+        provided or as generated.
+
+    Notes
+    -----
+    This uses a *k*-d tree to build the graph.
+
+    References
+    ----------
+    .. [1] http://cole-maclean.github.io/blog/files/thesis.pdf
+
+    Examples
+    --------
+    Default Graph:
+
+    >>> G = nx.thresholded_random_geometric_graph(50, 0.2, 0.1)
+
+    Custom Graph:
+
+    The `pos` keyword argument can be used to specify node positions so you
+    can create an arbitrary distribution and domain for positions.
+
+    If weights are not specified they are assigned to nodes by drawing randomly
+    from the exponential distribution with rate parameter :math:`\lambda=1`.
+    To specify weights from a different distribution, use the `weight` keyword
+    argument.
+
+    For example, create a thresholded random geometric graph on 50 nodes using a 2D
+    Gaussian distribution of node positions with mean (0, 0) and standard deviation 2,
+    where nodes are joined by an edge if their sum weights drawn from
+    a exponential distribution with rate = 5 are >= theta = 0.1 and their
+    Euclidean distance is at most 0.2.
+
+    >>> import random
+    >>> n = 50
+    >>> pos = {i: (random.gauss(0, 2), random.gauss(0, 2)) for i in range(n)}
+    >>> w = {i: random.expovariate(5.0) for i in range(n)}
+    >>> G = nx.thresholded_random_geometric_graph(n, 0.2, 0.1, 2, pos, w)
+
+    """
+    G = nx.empty_graph(n)
+    G.name = f"thresholded_random_geometric_graph({n}, {radius}, {theta}, {dim})"
+    # If no weights are provided, choose them from an exponential
+    # distribution.
+    if weight is None:
+        weight = {v: seed.expovariate(1) for v in G}
+    # If no positions are provided, choose uniformly random vectors in
+    # Euclidean space of the specified dimension.
+    if pos is None:
+        pos = {v: [seed.random() for i in range(dim)] for v in G}
+    # If no distance metric is provided, use Euclidean distance.
+    nx.set_node_attributes(G, weight, weight_name)
+    nx.set_node_attributes(G, pos, pos_name)
+
+    edges = (
+        (u, v)
+        for u, v in _geometric_edges(G, radius, p, pos_name)
+        if weight[u] + weight[v] >= theta
+    )
+    G.add_edges_from(edges)
+    return G
+
+
+@py_random_state(5)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def geometric_soft_configuration_graph(
+    *, beta, n=None, gamma=None, mean_degree=None, kappas=None, seed=None
+):
+    r"""Returns a random graph from the geometric soft configuration model.
+
+    The $\mathbb{S}^1$ model [1]_ is the geometric soft configuration model
+    which is able to explain many fundamental features of real networks such as
+    small-world property, heteregenous degree distributions, high level of
+    clustering, and self-similarity.
+
+    In the geometric soft configuration model, a node $i$ is assigned two hidden
+    variables: a hidden degree $\kappa_i$, quantifying its popularity, influence,
+    or importance, and an angular position $\theta_i$ in a circle abstracting the
+    similarity space, where angular distances between nodes are a proxy for their
+    similarity. Focusing on the angular position, this model is often called
+    the $\mathbb{S}^1$ model (a one-dimensional sphere). The circle's radius is
+    adjusted to $R = N/2\pi$, where $N$ is the number of nodes, so that the density
+    is set to 1 without loss of generality.
+
+    The connection probability between any pair of nodes increases with
+    the product of their hidden degrees (i.e., their combined popularities),
+    and decreases with the angular distance between the two nodes.
+    Specifically, nodes $i$ and $j$ are connected with the probability
+
+    $p_{ij} = \frac{1}{1 + \frac{d_{ij}^\beta}{\left(\mu \kappa_i \kappa_j\right)^{\max(1, \beta)}}}$
+
+    where $d_{ij} = R\Delta\theta_{ij}$ is the arc length of the circle between
+    nodes $i$ and $j$ separated by an angular distance $\Delta\theta_{ij}$.
+    Parameters $\mu$ and $\beta$ (also called inverse temperature) control the
+    average degree and the clustering coefficient, respectively.
+
+    It can be shown [2]_ that the model undergoes a structural phase transition
+    at $\beta=1$ so that for $\beta<1$ networks are unclustered in the thermodynamic
+    limit (when $N\to \infty$) whereas for $\beta>1$ the ensemble generates
+    networks with finite clustering coefficient.
+
+    The $\mathbb{S}^1$ model can be expressed as a purely geometric model
+    $\mathbb{H}^2$ in the hyperbolic plane [3]_ by mapping the hidden degree of
+    each node into a radial coordinate as
+
+    $r_i = \hat{R} - \frac{2 \max(1, \beta)}{\beta \zeta} \ln \left(\frac{\kappa_i}{\kappa_0}\right)$
+
+    where $\hat{R}$ is the radius of the hyperbolic disk and $\zeta$ is the curvature,
+
+    $\hat{R} = \frac{2}{\zeta} \ln \left(\frac{N}{\pi}\right)
+    - \frac{2\max(1, \beta)}{\beta \zeta} \ln (\mu \kappa_0^2)$
+
+    The connection probability then reads
+
+    $p_{ij} = \frac{1}{1 + \exp\left({\frac{\beta\zeta}{2} (x_{ij} - \hat{R})}\right)}$
+
+    where
+
+    $x_{ij} = r_i + r_j + \frac{2}{\zeta} \ln \frac{\Delta\theta_{ij}}{2}$
+
+    is a good approximation of the hyperbolic distance between two nodes separated
+    by an angular distance $\Delta\theta_{ij}$ with radial coordinates $r_i$ and $r_j$.
+    For $\beta > 1$, the curvature $\zeta = 1$, for $\beta < 1$, $\zeta = \beta^{-1}$.
+
+
+    Parameters
+    ----------
+    Either `n`, `gamma`, `mean_degree` are provided or `kappas`. The values of
+    `n`, `gamma`, `mean_degree` (if provided) are used to construct a random
+    kappa-dict keyed by node with values sampled from a power-law distribution.
+
+    beta : positive number
+        Inverse temperature, controlling the clustering coefficient.
+    n : int (default: None)
+        Size of the network (number of nodes).
+        If not provided, `kappas` must be provided and holds the nodes.
+    gamma : float (default: None)
+        Exponent of the power-law distribution for hidden degrees `kappas`.
+        If not provided, `kappas` must be provided directly.
+    mean_degree : float (default: None)
+        The mean degree in the network.
+        If not provided, `kappas` must be provided directly.
+    kappas : dict (default: None)
+        A dict keyed by node to its hidden degree value.
+        If not provided, random values are computed based on a power-law
+        distribution using `n`, `gamma` and `mean_degree`.
+    seed : int, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    Graph
+        A random geometric soft configuration graph (undirected with no self-loops).
+        Each node has three node-attributes:
+
+        - ``kappa`` that represents the hidden degree.
+
+        - ``theta`` the position in the similarity space ($\mathbb{S}^1$) which is
+          also the angular position in the hyperbolic plane.
+
+        - ``radius`` the radial position in the hyperbolic plane
+          (based on the hidden degree).
+
+
+    Examples
+    --------
+    Generate a network with specified parameters:
+
+    >>> G = nx.geometric_soft_configuration_graph(
+    ...     beta=1.5, n=100, gamma=2.7, mean_degree=5
+    ... )
+
+    Create a geometric soft configuration graph with 100 nodes. The $\beta$ parameter
+    is set to 1.5 and the exponent of the powerlaw distribution of the hidden
+    degrees is 2.7 with mean value of 5.
+
+    Generate a network with predefined hidden degrees:
+
+    >>> kappas = {i: 10 for i in range(100)}
+    >>> G = nx.geometric_soft_configuration_graph(beta=2.5, kappas=kappas)
+
+    Create a geometric soft configuration graph with 100 nodes. The $\beta$ parameter
+    is set to 2.5 and all nodes with hidden degree $\kappa=10$.
+
+
+    References
+    ----------
+    .. [1] Serrano, M. Á., Krioukov, D., & Boguñá, M. (2008). Self-similarity
+       of complex networks and hidden metric spaces. Physical review letters, 100(7), 078701.
+
+    .. [2] van der Kolk, J., Serrano, M. Á., & Boguñá, M. (2022). An anomalous
+       topological phase transition in spatial random graphs. Communications Physics, 5(1), 245.
+
+    .. [3] Krioukov, D., Papadopoulos, F., Kitsak, M., Vahdat, A., & Boguná, M. (2010).
+       Hyperbolic geometry of complex networks. Physical Review E, 82(3), 036106.
+
+    """
+    if beta <= 0:
+        raise nx.NetworkXError("The parameter beta cannot be smaller or equal to 0.")
+
+    if kappas is not None:
+        if not all((n is None, gamma is None, mean_degree is None)):
+            raise nx.NetworkXError(
+                "When kappas is input, n, gamma and mean_degree must not be."
+            )
+
+        n = len(kappas)
+        mean_degree = sum(kappas) / len(kappas)
+    else:
+        if any((n is None, gamma is None, mean_degree is None)):
+            raise nx.NetworkXError(
+                "Please provide either kappas, or all 3 of: n, gamma and mean_degree."
+            )
+
+        # Generate `n` hidden degrees from a powerlaw distribution
+        # with given exponent `gamma` and mean value `mean_degree`
+        gam_ratio = (gamma - 2) / (gamma - 1)
+        kappa_0 = mean_degree * gam_ratio * (1 - 1 / n) / (1 - 1 / n**gam_ratio)
+        base = 1 - 1 / n
+        power = 1 / (1 - gamma)
+        kappas = {i: kappa_0 * (1 - seed.random() * base) ** power for i in range(n)}
+
+    G = nx.Graph()
+    R = n / (2 * math.pi)
+
+    # Approximate values for mu in the thermodynamic limit (when n -> infinity)
+    if beta > 1:
+        mu = beta * math.sin(math.pi / beta) / (2 * math.pi * mean_degree)
+    elif beta == 1:
+        mu = 1 / (2 * mean_degree * math.log(n))
+    else:
+        mu = (1 - beta) / (2**beta * mean_degree * n ** (1 - beta))
+
+    # Generate random positions on a circle
+    thetas = {k: seed.uniform(0, 2 * math.pi) for k in kappas}
+
+    for u in kappas:
+        for v in list(G):
+            angle = math.pi - math.fabs(math.pi - math.fabs(thetas[u] - thetas[v]))
+            dij = math.pow(R * angle, beta)
+            mu_kappas = math.pow(mu * kappas[u] * kappas[v], max(1, beta))
+            p_ij = 1 / (1 + dij / mu_kappas)
+
+            # Create an edge with a certain connection probability
+            if seed.random() < p_ij:
+                G.add_edge(u, v)
+        G.add_node(u)
+
+    nx.set_node_attributes(G, thetas, "theta")
+    nx.set_node_attributes(G, kappas, "kappa")
+
+    # Map hidden degrees into the radial coordinates
+    zeta = 1 if beta > 1 else 1 / beta
+    kappa_min = min(kappas.values())
+    R_c = 2 * max(1, beta) / (beta * zeta)
+    R_hat = (2 / zeta) * math.log(n / math.pi) - R_c * math.log(mu * kappa_min)
+    radii = {node: R_hat - R_c * math.log(kappa) for node, kappa in kappas.items()}
+    nx.set_node_attributes(G, radii, "radius")
+
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/harary_graph.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/harary_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..591587d3aca68176a1d781eb1900b0feb04567e4
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/harary_graph.py
@@ -0,0 +1,199 @@
+"""Generators for Harary graphs
+
+This module gives two generators for the Harary graph, which was
+introduced by the famous mathematician Frank Harary in his 1962 work [H]_.
+The first generator gives the Harary graph that maximizes the node
+connectivity with given number of nodes and given number of edges.
+The second generator gives the Harary graph that minimizes
+the number of edges in the graph with given node connectivity and
+number of nodes.
+
+References
+----------
+.. [H] Harary, F. "The Maximum Connectivity of a Graph."
+       Proc. Nat. Acad. Sci. USA 48, 1142-1146, 1962.
+
+"""
+
+import networkx as nx
+from networkx.exception import NetworkXError
+
+__all__ = ["hnm_harary_graph", "hkn_harary_graph"]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hnm_harary_graph(n, m, create_using=None):
+    """Returns the Harary graph with given numbers of nodes and edges.
+
+    The Harary graph $H_{n,m}$ is the graph that maximizes node connectivity
+    with $n$ nodes and $m$ edges.
+
+    This maximum node connectivity is known to be floor($2m/n$). [1]_
+
+    Parameters
+    ----------
+    n: integer
+       The number of nodes the generated graph is to contain
+
+    m: integer
+       The number of edges the generated graph is to contain
+
+    create_using : NetworkX graph constructor, optional Graph type
+     to create (default=nx.Graph). If graph instance, then cleared
+     before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        The Harary graph $H_{n,m}$.
+
+    See Also
+    --------
+    hkn_harary_graph
+
+    Notes
+    -----
+    This algorithm runs in $O(m)$ time.
+    It is implemented by following the Reference [2]_.
+
+    References
+    ----------
+    .. [1] F. T. Boesch, A. Satyanarayana, and C. L. Suffel,
+       "A Survey of Some Network Reliability Analysis and Synthesis Results,"
+       Networks, pp. 99-107, 2009.
+
+    .. [2] Harary, F. "The Maximum Connectivity of a Graph."
+       Proc. Nat. Acad. Sci. USA 48, 1142-1146, 1962.
+    """
+
+    if n < 1:
+        raise NetworkXError("The number of nodes must be >= 1!")
+    if m < n - 1:
+        raise NetworkXError("The number of edges must be >= n - 1 !")
+    if m > n * (n - 1) // 2:
+        raise NetworkXError("The number of edges must be <= n(n-1)/2")
+
+    # Construct an empty graph with n nodes first
+    H = nx.empty_graph(n, create_using)
+    # Get the floor of average node degree
+    d = 2 * m // n
+
+    # Test the parity of n and d
+    if (n % 2 == 0) or (d % 2 == 0):
+        # Start with a regular graph of d degrees
+        offset = d // 2
+        for i in range(n):
+            for j in range(1, offset + 1):
+                H.add_edge(i, (i - j) % n)
+                H.add_edge(i, (i + j) % n)
+        if d & 1:
+            # in case d is odd; n must be even in this case
+            half = n // 2
+            for i in range(half):
+                # add edges diagonally
+                H.add_edge(i, i + half)
+        # Get the remainder of 2*m modulo n
+        r = 2 * m % n
+        if r > 0:
+            # add remaining edges at offset+1
+            for i in range(r // 2):
+                H.add_edge(i, i + offset + 1)
+    else:
+        # Start with a regular graph of (d - 1) degrees
+        offset = (d - 1) // 2
+        for i in range(n):
+            for j in range(1, offset + 1):
+                H.add_edge(i, (i - j) % n)
+                H.add_edge(i, (i + j) % n)
+        half = n // 2
+        for i in range(m - n * offset):
+            # add the remaining m - n*offset edges between i and i+half
+            H.add_edge(i, (i + half) % n)
+
+    return H
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hkn_harary_graph(k, n, create_using=None):
+    """Returns the Harary graph with given node connectivity and node number.
+
+    The Harary graph $H_{k,n}$ is the graph that minimizes the number of
+    edges needed with given node connectivity $k$ and node number $n$.
+
+    This smallest number of edges is known to be ceil($kn/2$) [1]_.
+
+    Parameters
+    ----------
+    k: integer
+       The node connectivity of the generated graph
+
+    n: integer
+       The number of nodes the generated graph is to contain
+
+    create_using : NetworkX graph constructor, optional Graph type
+     to create (default=nx.Graph). If graph instance, then cleared
+     before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        The Harary graph $H_{k,n}$.
+
+    See Also
+    --------
+    hnm_harary_graph
+
+    Notes
+    -----
+    This algorithm runs in $O(kn)$ time.
+    It is implemented by following the Reference [2]_.
+
+    References
+    ----------
+    .. [1] Weisstein, Eric W. "Harary Graph." From MathWorld--A Wolfram Web
+     Resource. http://mathworld.wolfram.com/HararyGraph.html.
+
+    .. [2] Harary, F. "The Maximum Connectivity of a Graph."
+      Proc. Nat. Acad. Sci. USA 48, 1142-1146, 1962.
+    """
+
+    if k < 1:
+        raise NetworkXError("The node connectivity must be >= 1!")
+    if n < k + 1:
+        raise NetworkXError("The number of nodes must be >= k+1 !")
+
+    # in case of connectivity 1, simply return the path graph
+    if k == 1:
+        H = nx.path_graph(n, create_using)
+        return H
+
+    # Construct an empty graph with n nodes first
+    H = nx.empty_graph(n, create_using)
+
+    # Test the parity of k and n
+    if (k % 2 == 0) or (n % 2 == 0):
+        # Construct a regular graph with k degrees
+        offset = k // 2
+        for i in range(n):
+            for j in range(1, offset + 1):
+                H.add_edge(i, (i - j) % n)
+                H.add_edge(i, (i + j) % n)
+        if k & 1:
+            # odd degree; n must be even in this case
+            half = n // 2
+            for i in range(half):
+                # add edges diagonally
+                H.add_edge(i, i + half)
+    else:
+        # Construct a regular graph with (k - 1) degrees
+        offset = (k - 1) // 2
+        for i in range(n):
+            for j in range(1, offset + 1):
+                H.add_edge(i, (i - j) % n)
+                H.add_edge(i, (i + j) % n)
+        half = n // 2
+        for i in range(half + 1):
+            # add half+1 edges between i and i+half
+            H.add_edge(i, (i + half) % n)
+
+    return H
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/internet_as_graphs.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/internet_as_graphs.py
new file mode 100644
index 0000000000000000000000000000000000000000..449d54376af4981a94ba6324f6d373962a9daaaa
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/internet_as_graphs.py
@@ -0,0 +1,441 @@
+"""Generates graphs resembling the Internet Autonomous System network"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["random_internet_as_graph"]
+
+
+def uniform_int_from_avg(a, m, seed):
+    """Pick a random integer with uniform probability.
+
+    Returns a random integer uniformly taken from a distribution with
+    minimum value 'a' and average value 'm', X~U(a,b), E[X]=m, X in N where
+    b = 2*m - a.
+
+    Notes
+    -----
+    p = (b-floor(b))/2
+    X = X1 + X2; X1~U(a,floor(b)), X2~B(p)
+    E[X] = E[X1] + E[X2] = (floor(b)+a)/2 + (b-floor(b))/2 = (b+a)/2 = m
+    """
+
+    from math import floor
+
+    assert m >= a
+    b = 2 * m - a
+    p = (b - floor(b)) / 2
+    X1 = round(seed.random() * (floor(b) - a) + a)
+    if seed.random() < p:
+        X2 = 1
+    else:
+        X2 = 0
+    return X1 + X2
+
+
+def choose_pref_attach(degs, seed):
+    """Pick a random value, with a probability given by its weight.
+
+    Returns a random choice among degs keys, each of which has a
+    probability proportional to the corresponding dictionary value.
+
+    Parameters
+    ----------
+    degs: dictionary
+        It contains the possible values (keys) and the corresponding
+        probabilities (values)
+    seed: random state
+
+    Returns
+    -------
+    v: object
+        A key of degs or None if degs is empty
+    """
+
+    if len(degs) == 0:
+        return None
+    s = sum(degs.values())
+    if s == 0:
+        return seed.choice(list(degs.keys()))
+    v = seed.random() * s
+
+    nodes = list(degs.keys())
+    i = 0
+    acc = degs[nodes[i]]
+    while v > acc:
+        i += 1
+        acc += degs[nodes[i]]
+    return nodes[i]
+
+
+class AS_graph_generator:
+    """Generates random internet AS graphs."""
+
+    def __init__(self, n, seed):
+        """Initializes variables. Immediate numbers are taken from [1].
+
+        Parameters
+        ----------
+        n: integer
+            Number of graph nodes
+        seed: random state
+            Indicator of random number generation state.
+            See :ref:`Randomness<randomness>`.
+
+        Returns
+        -------
+        GG: AS_graph_generator object
+
+        References
+        ----------
+        [1] A. Elmokashfi, A. Kvalbein and C. Dovrolis, "On the Scalability of
+        BGP: The Role of Topology Growth," in IEEE Journal on Selected Areas
+        in Communications, vol. 28, no. 8, pp. 1250-1261, October 2010.
+        """
+
+        self.seed = seed
+        self.n_t = min(n, round(self.seed.random() * 2 + 4))  # num of T nodes
+        self.n_m = round(0.15 * n)  # number of M nodes
+        self.n_cp = round(0.05 * n)  # number of CP nodes
+        self.n_c = max(0, n - self.n_t - self.n_m - self.n_cp)  # number of C nodes
+
+        self.d_m = 2 + (2.5 * n) / 10000  # average multihoming degree for M nodes
+        self.d_cp = 2 + (1.5 * n) / 10000  # avg multihoming degree for CP nodes
+        self.d_c = 1 + (5 * n) / 100000  # average multihoming degree for C nodes
+
+        self.p_m_m = 1 + (2 * n) / 10000  # avg num of peer edges between M and M
+        self.p_cp_m = 0.2 + (2 * n) / 10000  # avg num of peer edges between CP, M
+        self.p_cp_cp = 0.05 + (2 * n) / 100000  # avg num of peer edges btwn CP, CP
+
+        self.t_m = 0.375  # probability M's provider is T
+        self.t_cp = 0.375  # probability CP's provider is T
+        self.t_c = 0.125  # probability C's provider is T
+
+    def t_graph(self):
+        """Generates the core mesh network of tier one nodes of a AS graph.
+
+        Returns
+        -------
+        G: Networkx Graph
+            Core network
+        """
+
+        self.G = nx.Graph()
+        for i in range(self.n_t):
+            self.G.add_node(i, type="T")
+            for r in self.regions:
+                self.regions[r].add(i)
+            for j in self.G.nodes():
+                if i != j:
+                    self.add_edge(i, j, "peer")
+            self.customers[i] = set()
+            self.providers[i] = set()
+        return self.G
+
+    def add_edge(self, i, j, kind):
+        if kind == "transit":
+            customer = str(i)
+        else:
+            customer = "none"
+        self.G.add_edge(i, j, type=kind, customer=customer)
+
+    def choose_peer_pref_attach(self, node_list):
+        """Pick a node with a probability weighted by its peer degree.
+
+        Pick a node from node_list with preferential attachment
+        computed only on their peer degree
+        """
+
+        d = {}
+        for n in node_list:
+            d[n] = self.G.nodes[n]["peers"]
+        return choose_pref_attach(d, self.seed)
+
+    def choose_node_pref_attach(self, node_list):
+        """Pick a node with a probability weighted by its degree.
+
+        Pick a node from node_list with preferential attachment
+        computed on their degree
+        """
+
+        degs = dict(self.G.degree(node_list))
+        return choose_pref_attach(degs, self.seed)
+
+    def add_customer(self, i, j):
+        """Keep the dictionaries 'customers' and 'providers' consistent."""
+
+        self.customers[j].add(i)
+        self.providers[i].add(j)
+        for z in self.providers[j]:
+            self.customers[z].add(i)
+            self.providers[i].add(z)
+
+    def add_node(self, i, kind, reg2prob, avg_deg, t_edge_prob):
+        """Add a node and its customer transit edges to the graph.
+
+        Parameters
+        ----------
+        i: object
+            Identifier of the new node
+        kind: string
+            Type of the new node. Options are: 'M' for middle node, 'CP' for
+            content provider and 'C' for customer.
+        reg2prob: float
+            Probability the new node can be in two different regions.
+        avg_deg: float
+            Average number of transit nodes of which node i is customer.
+        t_edge_prob: float
+            Probability node i establish a customer transit edge with a tier
+            one (T) node
+
+        Returns
+        -------
+        i: object
+            Identifier of the new node
+        """
+
+        regs = 1  # regions in which node resides
+        if self.seed.random() < reg2prob:  # node is in two regions
+            regs = 2
+        node_options = set()
+
+        self.G.add_node(i, type=kind, peers=0)
+        self.customers[i] = set()
+        self.providers[i] = set()
+        self.nodes[kind].add(i)
+        for r in self.seed.sample(list(self.regions), regs):
+            node_options = node_options.union(self.regions[r])
+            self.regions[r].add(i)
+
+        edge_num = uniform_int_from_avg(1, avg_deg, self.seed)
+
+        t_options = node_options.intersection(self.nodes["T"])
+        m_options = node_options.intersection(self.nodes["M"])
+        if i in m_options:
+            m_options.remove(i)
+        d = 0
+        while d < edge_num and (len(t_options) > 0 or len(m_options) > 0):
+            if len(m_options) == 0 or (
+                len(t_options) > 0 and self.seed.random() < t_edge_prob
+            ):  # add edge to a T node
+                j = self.choose_node_pref_attach(t_options)
+                t_options.remove(j)
+            else:
+                j = self.choose_node_pref_attach(m_options)
+                m_options.remove(j)
+            self.add_edge(i, j, "transit")
+            self.add_customer(i, j)
+            d += 1
+
+        return i
+
+    def add_m_peering_link(self, m, to_kind):
+        """Add a peering link between two middle tier (M) nodes.
+
+        Target node j is drawn considering a preferential attachment based on
+        other M node peering degree.
+
+        Parameters
+        ----------
+        m: object
+            Node identifier
+        to_kind: string
+            type for target node j (must be always M)
+
+        Returns
+        -------
+        success: boolean
+        """
+
+        # candidates are of type 'M' and are not customers of m
+        node_options = self.nodes["M"].difference(self.customers[m])
+        # candidates are not providers of m
+        node_options = node_options.difference(self.providers[m])
+        # remove self
+        if m in node_options:
+            node_options.remove(m)
+
+        # remove candidates we are already connected to
+        for j in self.G.neighbors(m):
+            if j in node_options:
+                node_options.remove(j)
+
+        if len(node_options) > 0:
+            j = self.choose_peer_pref_attach(node_options)
+            self.add_edge(m, j, "peer")
+            self.G.nodes[m]["peers"] += 1
+            self.G.nodes[j]["peers"] += 1
+            return True
+        else:
+            return False
+
+    def add_cp_peering_link(self, cp, to_kind):
+        """Add a peering link to a content provider (CP) node.
+
+        Target node j can be CP or M and it is drawn uniformly among the nodes
+        belonging to the same region as cp.
+
+        Parameters
+        ----------
+        cp: object
+            Node identifier
+        to_kind: string
+            type for target node j (must be M or CP)
+
+        Returns
+        -------
+        success: boolean
+        """
+
+        node_options = set()
+        for r in self.regions:  # options include nodes in the same region(s)
+            if cp in self.regions[r]:
+                node_options = node_options.union(self.regions[r])
+
+        # options are restricted to the indicated kind ('M' or 'CP')
+        node_options = self.nodes[to_kind].intersection(node_options)
+
+        # remove self
+        if cp in node_options:
+            node_options.remove(cp)
+
+        # remove nodes that are cp's providers
+        node_options = node_options.difference(self.providers[cp])
+
+        # remove nodes we are already connected to
+        for j in self.G.neighbors(cp):
+            if j in node_options:
+                node_options.remove(j)
+
+        if len(node_options) > 0:
+            j = self.seed.sample(list(node_options), 1)[0]
+            self.add_edge(cp, j, "peer")
+            self.G.nodes[cp]["peers"] += 1
+            self.G.nodes[j]["peers"] += 1
+            return True
+        else:
+            return False
+
+    def graph_regions(self, rn):
+        """Initializes AS network regions.
+
+        Parameters
+        ----------
+        rn: integer
+            Number of regions
+        """
+
+        self.regions = {}
+        for i in range(rn):
+            self.regions["REG" + str(i)] = set()
+
+    def add_peering_links(self, from_kind, to_kind):
+        """Utility function to add peering links among node groups."""
+        peer_link_method = None
+        if from_kind == "M":
+            peer_link_method = self.add_m_peering_link
+            m = self.p_m_m
+        if from_kind == "CP":
+            peer_link_method = self.add_cp_peering_link
+            if to_kind == "M":
+                m = self.p_cp_m
+            else:
+                m = self.p_cp_cp
+
+        for i in self.nodes[from_kind]:
+            num = uniform_int_from_avg(0, m, self.seed)
+            for _ in range(num):
+                peer_link_method(i, to_kind)
+
+    def generate(self):
+        """Generates a random AS network graph as described in [1].
+
+        Returns
+        -------
+        G: Graph object
+
+        Notes
+        -----
+        The process steps are the following: first we create the core network
+        of tier one nodes, then we add the middle tier (M), the content
+        provider (CP) and the customer (C) nodes along with their transit edges
+        (link i,j means i is customer of j). Finally we add peering links
+        between M nodes, between M and CP nodes and between CP node couples.
+        For a detailed description of the algorithm, please refer to [1].
+
+        References
+        ----------
+        [1] A. Elmokashfi, A. Kvalbein and C. Dovrolis, "On the Scalability of
+        BGP: The Role of Topology Growth," in IEEE Journal on Selected Areas
+        in Communications, vol. 28, no. 8, pp. 1250-1261, October 2010.
+        """
+
+        self.graph_regions(5)
+        self.customers = {}
+        self.providers = {}
+        self.nodes = {"T": set(), "M": set(), "CP": set(), "C": set()}
+
+        self.t_graph()
+        self.nodes["T"] = set(self.G.nodes())
+
+        i = len(self.nodes["T"])
+        for _ in range(self.n_m):
+            self.nodes["M"].add(self.add_node(i, "M", 0.2, self.d_m, self.t_m))
+            i += 1
+        for _ in range(self.n_cp):
+            self.nodes["CP"].add(self.add_node(i, "CP", 0.05, self.d_cp, self.t_cp))
+            i += 1
+        for _ in range(self.n_c):
+            self.nodes["C"].add(self.add_node(i, "C", 0, self.d_c, self.t_c))
+            i += 1
+
+        self.add_peering_links("M", "M")
+        self.add_peering_links("CP", "M")
+        self.add_peering_links("CP", "CP")
+
+        return self.G
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_internet_as_graph(n, seed=None):
+    """Generates a random undirected graph resembling the Internet AS network
+
+    Parameters
+    ----------
+    n: integer in [1000, 10000]
+        Number of graph nodes
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G: Networkx Graph object
+        A randomly generated undirected graph
+
+    Notes
+    -----
+    This algorithm returns an undirected graph resembling the Internet
+    Autonomous System (AS) network, it uses the approach by Elmokashfi et al.
+    [1]_ and it grants the properties described in the related paper [1]_.
+
+    Each node models an autonomous system, with an attribute 'type' specifying
+    its kind; tier-1 (T), mid-level (M), customer (C) or content-provider (CP).
+    Each edge models an ADV communication link (hence, bidirectional) with
+    attributes:
+
+      - type: transit|peer, the kind of commercial agreement between nodes;
+      - customer: <node id>, the identifier of the node acting as customer
+        ('none' if type is peer).
+
+    References
+    ----------
+    .. [1] A. Elmokashfi, A. Kvalbein and C. Dovrolis, "On the Scalability of
+       BGP: The Role of Topology Growth," in IEEE Journal on Selected Areas
+       in Communications, vol. 28, no. 8, pp. 1250-1261, October 2010.
+    """
+
+    GG = AS_graph_generator(n, seed)
+    G = GG.generate()
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/intersection.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/intersection.py
new file mode 100644
index 0000000000000000000000000000000000000000..e63af5be8eed2e65b9cced2cc50827ffe6a802ba
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/intersection.py
@@ -0,0 +1,125 @@
+"""
+Generators for random intersection graphs.
+"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "uniform_random_intersection_graph",
+    "k_random_intersection_graph",
+    "general_random_intersection_graph",
+]
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def uniform_random_intersection_graph(n, m, p, seed=None):
+    """Returns a uniform random intersection graph.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the first bipartite set (nodes)
+    m : int
+        The number of nodes in the second bipartite set (attributes)
+    p : float
+        Probability of connecting nodes between bipartite sets
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    See Also
+    --------
+    gnp_random_graph
+
+    References
+    ----------
+    .. [1] K.B. Singer-Cohen, Random Intersection Graphs, 1995,
+       PhD thesis, Johns Hopkins University
+    .. [2] Fill, J. A., Scheinerman, E. R., and Singer-Cohen, K. B.,
+       Random intersection graphs when m = !(n):
+       An equivalence theorem relating the evolution of the g(n, m, p)
+       and g(n, p) models. Random Struct. Algorithms 16, 2 (2000), 156–176.
+    """
+    from networkx.algorithms import bipartite
+
+    G = bipartite.random_graph(n, m, p, seed)
+    return nx.projected_graph(G, range(n))
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def k_random_intersection_graph(n, m, k, seed=None):
+    """Returns a intersection graph with randomly chosen attribute sets for
+    each node that are of equal size (k).
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the first bipartite set (nodes)
+    m : int
+        The number of nodes in the second bipartite set (attributes)
+    k : float
+        Size of attribute set to assign to each node.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    See Also
+    --------
+    gnp_random_graph, uniform_random_intersection_graph
+
+    References
+    ----------
+    .. [1] Godehardt, E., and Jaworski, J.
+       Two models of random intersection graphs and their applications.
+       Electronic Notes in Discrete Mathematics 10 (2001), 129--132.
+    """
+    G = nx.empty_graph(n + m)
+    mset = range(n, n + m)
+    for v in range(n):
+        targets = seed.sample(mset, k)
+        G.add_edges_from(zip([v] * len(targets), targets))
+    return nx.projected_graph(G, range(n))
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def general_random_intersection_graph(n, m, p, seed=None):
+    """Returns a random intersection graph with independent probabilities
+    for connections between node and attribute sets.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the first bipartite set (nodes)
+    m : int
+        The number of nodes in the second bipartite set (attributes)
+    p : list of floats of length m
+        Probabilities for connecting nodes to each attribute
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    See Also
+    --------
+    gnp_random_graph, uniform_random_intersection_graph
+
+    References
+    ----------
+    .. [1] Nikoletseas, S. E., Raptopoulos, C., and Spirakis, P. G.
+       The existence and efficient construction of large independent sets
+       in general random intersection graphs. In ICALP (2004), J. D´ıaz,
+       J. Karhum¨aki, A. Lepist¨o, and D. Sannella, Eds., vol. 3142
+       of Lecture Notes in Computer Science, Springer, pp. 1029–1040.
+    """
+    if len(p) != m:
+        raise ValueError("Probability list p must have m elements.")
+    G = nx.empty_graph(n + m)
+    mset = range(n, n + m)
+    for u in range(n):
+        for v, q in zip(mset, p):
+            if seed.random() < q:
+                G.add_edge(u, v)
+    return nx.projected_graph(G, range(n))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/interval_graph.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/interval_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a3fda45acec52af6a5f060b96d9af1067fc002b
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/interval_graph.py
@@ -0,0 +1,70 @@
+"""
+Generators for interval graph.
+"""
+
+from collections.abc import Sequence
+
+import networkx as nx
+
+__all__ = ["interval_graph"]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def interval_graph(intervals):
+    """Generates an interval graph for a list of intervals given.
+
+    In graph theory, an interval graph is an undirected graph formed from a set
+    of closed intervals on the real line, with a vertex for each interval
+    and an edge between vertices whose intervals intersect.
+    It is the intersection graph of the intervals.
+
+    More information can be found at:
+    https://en.wikipedia.org/wiki/Interval_graph
+
+    Parameters
+    ----------
+    intervals : a sequence of intervals, say (l, r) where l is the left end,
+    and r is the right end of the closed interval.
+
+    Returns
+    -------
+    G : networkx graph
+
+    Examples
+    --------
+    >>> intervals = [(-2, 3), [1, 4], (2, 3), (4, 6)]
+    >>> G = nx.interval_graph(intervals)
+    >>> sorted(G.edges)
+    [((-2, 3), (1, 4)), ((-2, 3), (2, 3)), ((1, 4), (2, 3)), ((1, 4), (4, 6))]
+
+    Raises
+    ------
+    :exc:`TypeError`
+        if `intervals` contains None or an element which is not
+        collections.abc.Sequence or not a length of 2.
+    :exc:`ValueError`
+        if `intervals` contains an interval such that min1 > max1
+        where min1,max1 = interval
+    """
+    intervals = list(intervals)
+    for interval in intervals:
+        if not (isinstance(interval, Sequence) and len(interval) == 2):
+            raise TypeError(
+                "Each interval must have length 2, and be a "
+                "collections.abc.Sequence such as tuple or list."
+            )
+        if interval[0] > interval[1]:
+            raise ValueError(f"Interval must have lower value first. Got {interval}")
+
+    graph = nx.Graph()
+
+    tupled_intervals = [tuple(interval) for interval in intervals]
+    graph.add_nodes_from(tupled_intervals)
+
+    while tupled_intervals:
+        min1, max1 = interval1 = tupled_intervals.pop()
+        for interval2 in tupled_intervals:
+            min2, max2 = interval2
+            if max1 >= min2 and max2 >= min1:
+                graph.add_edge(interval1, interval2)
+    return graph
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/joint_degree_seq.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/joint_degree_seq.py
new file mode 100644
index 0000000000000000000000000000000000000000..c426df944ad27aef4584371838a6ddb280b90dca
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/joint_degree_seq.py
@@ -0,0 +1,664 @@
+"""Generate graphs with a given joint degree and directed joint degree"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "is_valid_joint_degree",
+    "is_valid_directed_joint_degree",
+    "joint_degree_graph",
+    "directed_joint_degree_graph",
+]
+
+
+@nx._dispatchable(graphs=None)
+def is_valid_joint_degree(joint_degrees):
+    """Checks whether the given joint degree dictionary is realizable.
+
+    A *joint degree dictionary* is a dictionary of dictionaries, in
+    which entry ``joint_degrees[k][l]`` is an integer representing the
+    number of edges joining nodes of degree *k* with nodes of degree
+    *l*. Such a dictionary is realizable as a simple graph if and only
+    if the following conditions are satisfied.
+
+    - each entry must be an integer,
+    - the total number of nodes of degree *k*, computed by
+      ``sum(joint_degrees[k].values()) / k``, must be an integer,
+    - the total number of edges joining nodes of degree *k* with
+      nodes of degree *l* cannot exceed the total number of possible edges,
+    - each diagonal entry ``joint_degrees[k][k]`` must be even (this is
+      a convention assumed by the :func:`joint_degree_graph` function).
+
+
+    Parameters
+    ----------
+    joint_degrees :  dictionary of dictionary of integers
+        A joint degree dictionary in which entry ``joint_degrees[k][l]``
+        is the number of edges joining nodes of degree *k* with nodes of
+        degree *l*.
+
+    Returns
+    -------
+    bool
+        Whether the given joint degree dictionary is realizable as a
+        simple graph.
+
+    References
+    ----------
+    .. [1] M. Gjoka, M. Kurant, A. Markopoulou, "2.5K Graphs: from Sampling
+       to Generation", IEEE Infocom, 2013.
+    .. [2] I. Stanton, A. Pinar, "Constructing and sampling graphs with a
+       prescribed joint degree distribution", Journal of Experimental
+       Algorithmics, 2012.
+    """
+
+    degree_count = {}
+    for k in joint_degrees:
+        if k > 0:
+            k_size = sum(joint_degrees[k].values()) / k
+            if not k_size.is_integer():
+                return False
+            degree_count[k] = k_size
+
+    for k in joint_degrees:
+        for l in joint_degrees[k]:
+            if not float(joint_degrees[k][l]).is_integer():
+                return False
+
+            if (k != l) and (joint_degrees[k][l] > degree_count[k] * degree_count[l]):
+                return False
+            elif k == l:
+                if joint_degrees[k][k] > degree_count[k] * (degree_count[k] - 1):
+                    return False
+                if joint_degrees[k][k] % 2 != 0:
+                    return False
+
+    # if all above conditions have been satisfied then the input
+    # joint degree is realizable as a simple graph.
+    return True
+
+
+def _neighbor_switch(G, w, unsat, h_node_residual, avoid_node_id=None):
+    """Releases one free stub for ``w``, while preserving joint degree in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Graph in which the neighbor switch will take place.
+    w : integer
+        Node id for which we will execute this neighbor switch.
+    unsat : set of integers
+        Set of unsaturated node ids that have the same degree as w.
+    h_node_residual: dictionary of integers
+        Keeps track of the remaining stubs  for a given node.
+    avoid_node_id: integer
+        Node id to avoid when selecting w_prime.
+
+    Notes
+    -----
+    First, it selects *w_prime*, an  unsaturated node that has the same degree
+    as ``w``. Second, it selects *switch_node*, a neighbor node of ``w`` that
+    is not  connected to *w_prime*. Then it executes an edge swap i.e. removes
+    (``w``,*switch_node*) and adds (*w_prime*,*switch_node*). Gjoka et. al. [1]
+    prove that such an edge swap is always possible.
+
+    References
+    ----------
+    .. [1] M. Gjoka, B. Tillman, A. Markopoulou, "Construction of Simple
+       Graphs with a Target Joint Degree Matrix and Beyond", IEEE Infocom, '15
+    """
+
+    if (avoid_node_id is None) or (h_node_residual[avoid_node_id] > 1):
+        # select unsaturated node w_prime that has the same degree as w
+        w_prime = next(iter(unsat))
+    else:
+        # assume that the node pair (v,w) has been selected for connection. if
+        # - neighbor_switch is called for node w,
+        # - nodes v and w have the same degree,
+        # - node v=avoid_node_id has only one stub left,
+        # then prevent v=avoid_node_id from being selected as w_prime.
+
+        iter_var = iter(unsat)
+        while True:
+            w_prime = next(iter_var)
+            if w_prime != avoid_node_id:
+                break
+
+    # select switch_node, a neighbor of w, that is not connected to w_prime
+    w_prime_neighbs = G[w_prime]  # slightly faster declaring this variable
+    for v in G[w]:
+        if (v not in w_prime_neighbs) and (v != w_prime):
+            switch_node = v
+            break
+
+    # remove edge (w,switch_node), add edge (w_prime,switch_node) and update
+    # data structures
+    G.remove_edge(w, switch_node)
+    G.add_edge(w_prime, switch_node)
+    h_node_residual[w] += 1
+    h_node_residual[w_prime] -= 1
+    if h_node_residual[w_prime] == 0:
+        unsat.remove(w_prime)
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def joint_degree_graph(joint_degrees, seed=None):
+    """Generates a random simple graph with the given joint degree dictionary.
+
+    Parameters
+    ----------
+    joint_degrees :  dictionary of dictionary of integers
+        A joint degree dictionary in which entry ``joint_degrees[k][l]`` is the
+        number of edges joining nodes of degree *k* with nodes of degree *l*.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : Graph
+        A graph with the specified joint degree dictionary.
+
+    Raises
+    ------
+    NetworkXError
+        If *joint_degrees* dictionary is not realizable.
+
+    Notes
+    -----
+    In each iteration of the "while loop" the algorithm picks two disconnected
+    nodes *v* and *w*, of degree *k* and *l* correspondingly,  for which
+    ``joint_degrees[k][l]`` has not reached its target yet. It then adds
+    edge (*v*, *w*) and increases the number of edges in graph G by one.
+
+    The intelligence of the algorithm lies in the fact that  it is always
+    possible to add an edge between such disconnected nodes *v* and *w*,
+    even if one or both nodes do not have free stubs. That is made possible by
+    executing a "neighbor switch", an edge rewiring move that releases
+    a free stub while keeping the joint degree of G the same.
+
+    The algorithm continues for E (number of edges) iterations of
+    the "while loop", at the which point all entries of the given
+    ``joint_degrees[k][l]`` have reached their target values and the
+    construction is complete.
+
+    References
+    ----------
+    ..  [1] M. Gjoka, B. Tillman, A. Markopoulou, "Construction of Simple
+        Graphs with a Target Joint Degree Matrix and Beyond", IEEE Infocom, '15
+
+    Examples
+    --------
+    >>> joint_degrees = {
+    ...     1: {4: 1},
+    ...     2: {2: 2, 3: 2, 4: 2},
+    ...     3: {2: 2, 4: 1},
+    ...     4: {1: 1, 2: 2, 3: 1},
+    ... }
+    >>> G = nx.joint_degree_graph(joint_degrees)
+    >>>
+    """
+
+    if not is_valid_joint_degree(joint_degrees):
+        msg = "Input joint degree dict not realizable as a simple graph"
+        raise nx.NetworkXError(msg)
+
+    # compute degree count from joint_degrees
+    degree_count = {k: sum(l.values()) // k for k, l in joint_degrees.items() if k > 0}
+
+    # start with empty N-node graph
+    N = sum(degree_count.values())
+    G = nx.empty_graph(N)
+
+    # for a given degree group, keep the list of all node ids
+    h_degree_nodelist = {}
+
+    # for a given node, keep track of the remaining stubs
+    h_node_residual = {}
+
+    # populate h_degree_nodelist and h_node_residual
+    nodeid = 0
+    for degree, num_nodes in degree_count.items():
+        h_degree_nodelist[degree] = range(nodeid, nodeid + num_nodes)
+        for v in h_degree_nodelist[degree]:
+            h_node_residual[v] = degree
+        nodeid += int(num_nodes)
+
+    # iterate over every degree pair (k,l) and add the number of edges given
+    # for each pair
+    for k in joint_degrees:
+        for l in joint_degrees[k]:
+            # n_edges_add is the number of edges to add for the
+            # degree pair (k,l)
+            n_edges_add = joint_degrees[k][l]
+
+            if (n_edges_add > 0) and (k >= l):
+                # number of nodes with degree k and l
+                k_size = degree_count[k]
+                l_size = degree_count[l]
+
+                # k_nodes and l_nodes consist of all nodes of degree k and l
+                k_nodes = h_degree_nodelist[k]
+                l_nodes = h_degree_nodelist[l]
+
+                # k_unsat and l_unsat consist of nodes of degree k and l that
+                # are unsaturated (nodes that have at least 1 available stub)
+                k_unsat = {v for v in k_nodes if h_node_residual[v] > 0}
+
+                if k != l:
+                    l_unsat = {w for w in l_nodes if h_node_residual[w] > 0}
+                else:
+                    l_unsat = k_unsat
+                    n_edges_add = joint_degrees[k][l] // 2
+
+                while n_edges_add > 0:
+                    # randomly pick nodes v and w that have degrees k and l
+                    v = k_nodes[seed.randrange(k_size)]
+                    w = l_nodes[seed.randrange(l_size)]
+
+                    # if nodes v and w are disconnected then attempt to connect
+                    if not G.has_edge(v, w) and (v != w):
+                        # if node v has no free stubs then do neighbor switch
+                        if h_node_residual[v] == 0:
+                            _neighbor_switch(G, v, k_unsat, h_node_residual)
+
+                        # if node w has no free stubs then do neighbor switch
+                        if h_node_residual[w] == 0:
+                            if k != l:
+                                _neighbor_switch(G, w, l_unsat, h_node_residual)
+                            else:
+                                _neighbor_switch(
+                                    G, w, l_unsat, h_node_residual, avoid_node_id=v
+                                )
+
+                        # add edge (v, w) and update data structures
+                        G.add_edge(v, w)
+                        h_node_residual[v] -= 1
+                        h_node_residual[w] -= 1
+                        n_edges_add -= 1
+
+                        if h_node_residual[v] == 0:
+                            k_unsat.discard(v)
+                        if h_node_residual[w] == 0:
+                            l_unsat.discard(w)
+    return G
+
+
+@nx._dispatchable(graphs=None)
+def is_valid_directed_joint_degree(in_degrees, out_degrees, nkk):
+    """Checks whether the given directed joint degree input is realizable
+
+    Parameters
+    ----------
+    in_degrees :  list of integers
+        in degree sequence contains the in degrees of nodes.
+    out_degrees : list of integers
+        out degree sequence contains the out degrees of nodes.
+    nkk  :  dictionary of dictionary of integers
+        directed joint degree dictionary. for nodes of out degree k (first
+        level of dict) and nodes of in degree l (second level of dict)
+        describes the number of edges.
+
+    Returns
+    -------
+    boolean
+        returns true if given input is realizable, else returns false.
+
+    Notes
+    -----
+    Here is the list of conditions that the inputs (in/out degree sequences,
+    nkk) need to satisfy for simple directed graph realizability:
+
+    - Condition 0: in_degrees and out_degrees have the same length
+    - Condition 1: nkk[k][l]  is integer for all k,l
+    - Condition 2: sum(nkk[k])/k = number of nodes with partition id k, is an
+                   integer and matching degree sequence
+    - Condition 3: number of edges and non-chords between k and l cannot exceed
+                   maximum possible number of edges
+
+
+    References
+    ----------
+    [1] B. Tillman, A. Markopoulou, C. T. Butts & M. Gjoka,
+        "Construction of Directed 2K Graphs". In Proc. of KDD 2017.
+    """
+    V = {}  # number of nodes with in/out degree.
+    forbidden = {}
+    if len(in_degrees) != len(out_degrees):
+        return False
+
+    for idx in range(len(in_degrees)):
+        i = in_degrees[idx]
+        o = out_degrees[idx]
+        V[(i, 0)] = V.get((i, 0), 0) + 1
+        V[(o, 1)] = V.get((o, 1), 0) + 1
+
+        forbidden[(o, i)] = forbidden.get((o, i), 0) + 1
+
+    S = {}  # number of edges going from in/out degree nodes.
+    for k in nkk:
+        for l in nkk[k]:
+            val = nkk[k][l]
+            if not float(val).is_integer():  # condition 1
+                return False
+
+            if val > 0:
+                S[(k, 1)] = S.get((k, 1), 0) + val
+                S[(l, 0)] = S.get((l, 0), 0) + val
+                # condition 3
+                if val + forbidden.get((k, l), 0) > V[(k, 1)] * V[(l, 0)]:
+                    return False
+
+    return all(S[s] / s[0] == V[s] for s in S)
+
+
+def _directed_neighbor_switch(
+    G, w, unsat, h_node_residual_out, chords, h_partition_in, partition
+):
+    """Releases one free stub for node w, while preserving joint degree in G.
+
+    Parameters
+    ----------
+    G : networkx directed graph
+        graph within which the edge swap will take place.
+    w : integer
+        node id for which we need to perform a neighbor switch.
+    unsat: set of integers
+        set of node ids that have the same degree as w and are unsaturated.
+    h_node_residual_out: dict of integers
+        for a given node, keeps track of the remaining stubs to be added.
+    chords: set of tuples
+        keeps track of available positions to add edges.
+    h_partition_in: dict of integers
+        for a given node, keeps track of its partition id (in degree).
+    partition: integer
+        partition id to check if chords have to be updated.
+
+    Notes
+    -----
+    First, it selects node w_prime that (1) has the same degree as w and
+    (2) is unsaturated. Then, it selects node v, a neighbor of w, that is
+    not connected to w_prime and does an edge swap i.e. removes (w,v) and
+    adds (w_prime,v). If neighbor switch is not possible for w using
+    w_prime and v, then return w_prime; in [1] it's proven that
+    such unsaturated nodes can be used.
+
+    References
+    ----------
+    [1] B. Tillman, A. Markopoulou, C. T. Butts & M. Gjoka,
+        "Construction of Directed 2K Graphs". In Proc. of KDD 2017.
+    """
+    w_prime = unsat.pop()
+    unsat.add(w_prime)
+    # select node t, a neighbor of w, that is not connected to w_prime
+    w_neighbs = list(G.successors(w))
+    # slightly faster declaring this variable
+    w_prime_neighbs = list(G.successors(w_prime))
+
+    for v in w_neighbs:
+        if (v not in w_prime_neighbs) and w_prime != v:
+            # removes (w,v), add (w_prime,v)  and update data structures
+            G.remove_edge(w, v)
+            G.add_edge(w_prime, v)
+
+            if h_partition_in[v] == partition:
+                chords.add((w, v))
+                chords.discard((w_prime, v))
+
+            h_node_residual_out[w] += 1
+            h_node_residual_out[w_prime] -= 1
+            if h_node_residual_out[w_prime] == 0:
+                unsat.remove(w_prime)
+            return None
+
+    # If neighbor switch didn't work, use unsaturated node
+    return w_prime
+
+
+def _directed_neighbor_switch_rev(
+    G, w, unsat, h_node_residual_in, chords, h_partition_out, partition
+):
+    """The reverse of directed_neighbor_switch.
+
+    Parameters
+    ----------
+    G : networkx directed graph
+        graph within which the edge swap will take place.
+    w : integer
+        node id for which we need to perform a neighbor switch.
+    unsat: set of integers
+        set of node ids that have the same degree as w and are unsaturated.
+    h_node_residual_in: dict of integers
+        for a given node, keeps track of the remaining stubs to be added.
+    chords: set of tuples
+        keeps track of available positions to add edges.
+    h_partition_out: dict of integers
+        for a given node, keeps track of its partition id (out degree).
+    partition: integer
+        partition id to check if chords have to be updated.
+
+    Notes
+    -----
+    Same operation as directed_neighbor_switch except it handles this operation
+    for incoming edges instead of outgoing.
+    """
+    w_prime = unsat.pop()
+    unsat.add(w_prime)
+    # slightly faster declaring these as variables.
+    w_neighbs = list(G.predecessors(w))
+    w_prime_neighbs = list(G.predecessors(w_prime))
+    # select node v, a neighbor of w, that is not connected to w_prime.
+    for v in w_neighbs:
+        if (v not in w_prime_neighbs) and w_prime != v:
+            # removes (v,w), add (v,w_prime) and update data structures.
+            G.remove_edge(v, w)
+            G.add_edge(v, w_prime)
+            if h_partition_out[v] == partition:
+                chords.add((v, w))
+                chords.discard((v, w_prime))
+
+            h_node_residual_in[w] += 1
+            h_node_residual_in[w_prime] -= 1
+            if h_node_residual_in[w_prime] == 0:
+                unsat.remove(w_prime)
+            return None
+
+    # If neighbor switch didn't work, use the unsaturated node.
+    return w_prime
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def directed_joint_degree_graph(in_degrees, out_degrees, nkk, seed=None):
+    """Generates a random simple directed graph with the joint degree.
+
+    Parameters
+    ----------
+    degree_seq :  list of tuples (of size 3)
+        degree sequence contains tuples of nodes with node id, in degree and
+        out degree.
+    nkk  :  dictionary of dictionary of integers
+        directed joint degree dictionary, for nodes of out degree k (first
+        level of dict) and nodes of in degree l (second level of dict)
+        describes the number of edges.
+    seed : hashable object, optional
+        Seed for random number generator.
+
+    Returns
+    -------
+    G : Graph
+        A directed graph with the specified inputs.
+
+    Raises
+    ------
+    NetworkXError
+        If degree_seq and nkk are not realizable as a simple directed graph.
+
+
+    Notes
+    -----
+    Similarly to the undirected version:
+    In each iteration of the "while loop" the algorithm picks two disconnected
+    nodes v and w, of degree k and l correspondingly,  for which nkk[k][l] has
+    not reached its target yet i.e. (for given k,l): n_edges_add < nkk[k][l].
+    It then adds edge (v,w) and always increases the number of edges in graph G
+    by one.
+
+    The intelligence of the algorithm lies in the fact that  it is always
+    possible to add an edge between disconnected nodes v and w, for which
+    nkk[degree(v)][degree(w)] has not reached its target, even if one or both
+    nodes do not have free stubs. If either node v or w does not have a free
+    stub, we perform a "neighbor switch", an edge rewiring move that releases a
+    free stub while keeping nkk the same.
+
+    The difference for the directed version lies in the fact that neighbor
+    switches might not be able to rewire, but in these cases unsaturated nodes
+    can be reassigned to use instead, see [1] for detailed description and
+    proofs.
+
+    The algorithm continues for E (number of edges in the graph) iterations of
+    the "while loop", at which point all entries of the given nkk[k][l] have
+    reached their target values and the construction is complete.
+
+    References
+    ----------
+    [1] B. Tillman, A. Markopoulou, C. T. Butts & M. Gjoka,
+        "Construction of Directed 2K Graphs". In Proc. of KDD 2017.
+
+    Examples
+    --------
+    >>> in_degrees = [0, 1, 1, 2]
+    >>> out_degrees = [1, 1, 1, 1]
+    >>> nkk = {1: {1: 2, 2: 2}}
+    >>> G = nx.directed_joint_degree_graph(in_degrees, out_degrees, nkk)
+    >>>
+    """
+    if not is_valid_directed_joint_degree(in_degrees, out_degrees, nkk):
+        msg = "Input is not realizable as a simple graph"
+        raise nx.NetworkXError(msg)
+
+    # start with an empty directed graph.
+    G = nx.DiGraph()
+
+    # for a given group, keep the list of all node ids.
+    h_degree_nodelist_in = {}
+    h_degree_nodelist_out = {}
+    # for a given group, keep the list of all unsaturated node ids.
+    h_degree_nodelist_in_unsat = {}
+    h_degree_nodelist_out_unsat = {}
+    # for a given node, keep track of the remaining stubs to be added.
+    h_node_residual_out = {}
+    h_node_residual_in = {}
+    # for a given node, keep track of the partition id.
+    h_partition_out = {}
+    h_partition_in = {}
+    # keep track of non-chords between pairs of partition ids.
+    non_chords = {}
+
+    # populate data structures
+    for idx, i in enumerate(in_degrees):
+        idx = int(idx)
+        if i > 0:
+            h_degree_nodelist_in.setdefault(i, [])
+            h_degree_nodelist_in_unsat.setdefault(i, set())
+            h_degree_nodelist_in[i].append(idx)
+            h_degree_nodelist_in_unsat[i].add(idx)
+            h_node_residual_in[idx] = i
+            h_partition_in[idx] = i
+
+    for idx, o in enumerate(out_degrees):
+        o = out_degrees[idx]
+        non_chords[(o, in_degrees[idx])] = non_chords.get((o, in_degrees[idx]), 0) + 1
+        idx = int(idx)
+        if o > 0:
+            h_degree_nodelist_out.setdefault(o, [])
+            h_degree_nodelist_out_unsat.setdefault(o, set())
+            h_degree_nodelist_out[o].append(idx)
+            h_degree_nodelist_out_unsat[o].add(idx)
+            h_node_residual_out[idx] = o
+            h_partition_out[idx] = o
+
+        G.add_node(idx)
+
+    nk_in = {}
+    nk_out = {}
+    for p in h_degree_nodelist_in:
+        nk_in[p] = len(h_degree_nodelist_in[p])
+    for p in h_degree_nodelist_out:
+        nk_out[p] = len(h_degree_nodelist_out[p])
+
+    # iterate over every degree pair (k,l) and add the number of edges given
+    # for each pair.
+    for k in nkk:
+        for l in nkk[k]:
+            n_edges_add = nkk[k][l]
+
+            if n_edges_add > 0:
+                # chords contains a random set of potential edges.
+                chords = set()
+
+                k_len = nk_out[k]
+                l_len = nk_in[l]
+                chords_sample = seed.sample(
+                    range(k_len * l_len), n_edges_add + non_chords.get((k, l), 0)
+                )
+
+                num = 0
+                while len(chords) < n_edges_add:
+                    i = h_degree_nodelist_out[k][chords_sample[num] % k_len]
+                    j = h_degree_nodelist_in[l][chords_sample[num] // k_len]
+                    num += 1
+                    if i != j:
+                        chords.add((i, j))
+
+                # k_unsat and l_unsat consist of nodes of in/out degree k and l
+                # that are unsaturated i.e. those nodes that have at least one
+                # available stub
+                k_unsat = h_degree_nodelist_out_unsat[k]
+                l_unsat = h_degree_nodelist_in_unsat[l]
+
+                while n_edges_add > 0:
+                    v, w = chords.pop()
+                    chords.add((v, w))
+
+                    # if node v has no free stubs then do neighbor switch.
+                    if h_node_residual_out[v] == 0:
+                        _v = _directed_neighbor_switch(
+                            G,
+                            v,
+                            k_unsat,
+                            h_node_residual_out,
+                            chords,
+                            h_partition_in,
+                            l,
+                        )
+                        if _v is not None:
+                            v = _v
+
+                    # if node w has no free stubs then do neighbor switch.
+                    if h_node_residual_in[w] == 0:
+                        _w = _directed_neighbor_switch_rev(
+                            G,
+                            w,
+                            l_unsat,
+                            h_node_residual_in,
+                            chords,
+                            h_partition_out,
+                            k,
+                        )
+                        if _w is not None:
+                            w = _w
+
+                    # add edge (v,w) and update data structures.
+                    G.add_edge(v, w)
+                    h_node_residual_out[v] -= 1
+                    h_node_residual_in[w] -= 1
+                    n_edges_add -= 1
+                    chords.discard((v, w))
+
+                    if h_node_residual_out[v] == 0:
+                        k_unsat.discard(v)
+                    if h_node_residual_in[w] == 0:
+                        l_unsat.discard(w)
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/lattice.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/lattice.py
new file mode 100644
index 0000000000000000000000000000000000000000..95e520d2ce1fbbd16c10f42348807062f855e50b
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/lattice.py
@@ -0,0 +1,367 @@
+"""Functions for generating grid graphs and lattices
+
+The :func:`grid_2d_graph`, :func:`triangular_lattice_graph`, and
+:func:`hexagonal_lattice_graph` functions correspond to the three
+`regular tilings of the plane`_, the square, triangular, and hexagonal
+tilings, respectively. :func:`grid_graph` and :func:`hypercube_graph`
+are similar for arbitrary dimensions. Useful relevant discussion can
+be found about `Triangular Tiling`_, and `Square, Hex and Triangle Grids`_
+
+.. _regular tilings of the plane: https://en.wikipedia.org/wiki/List_of_regular_polytopes_and_compounds#Euclidean_tilings
+.. _Square, Hex and Triangle Grids: http://www-cs-students.stanford.edu/~amitp/game-programming/grids/
+.. _Triangular Tiling: https://en.wikipedia.org/wiki/Triangular_tiling
+
+"""
+
+from itertools import repeat
+from math import sqrt
+
+import networkx as nx
+from networkx.classes import set_node_attributes
+from networkx.exception import NetworkXError
+from networkx.generators.classic import cycle_graph, empty_graph, path_graph
+from networkx.relabel import relabel_nodes
+from networkx.utils import flatten, nodes_or_number, pairwise
+
+__all__ = [
+    "grid_2d_graph",
+    "grid_graph",
+    "hypercube_graph",
+    "triangular_lattice_graph",
+    "hexagonal_lattice_graph",
+]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number([0, 1])
+def grid_2d_graph(m, n, periodic=False, create_using=None):
+    """Returns the two-dimensional grid graph.
+
+    The grid graph has each node connected to its four nearest neighbors.
+
+    Parameters
+    ----------
+    m, n : int or iterable container of nodes
+        If an integer, nodes are from `range(n)`.
+        If a container, elements become the coordinate of the nodes.
+
+    periodic : bool or iterable
+        If `periodic` is True, both dimensions are periodic. If False, none
+        are periodic.  If `periodic` is iterable, it should yield 2 bool
+        values indicating whether the 1st and 2nd axes, respectively, are
+        periodic.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        The (possibly periodic) grid graph of the specified dimensions.
+
+    """
+    G = empty_graph(0, create_using)
+    row_name, rows = m
+    col_name, cols = n
+    G.add_nodes_from((i, j) for i in rows for j in cols)
+    G.add_edges_from(((i, j), (pi, j)) for pi, i in pairwise(rows) for j in cols)
+    G.add_edges_from(((i, j), (i, pj)) for i in rows for pj, j in pairwise(cols))
+
+    try:
+        periodic_r, periodic_c = periodic
+    except TypeError:
+        periodic_r = periodic_c = periodic
+
+    if periodic_r and len(rows) > 2:
+        first = rows[0]
+        last = rows[-1]
+        G.add_edges_from(((first, j), (last, j)) for j in cols)
+    if periodic_c and len(cols) > 2:
+        first = cols[0]
+        last = cols[-1]
+        G.add_edges_from(((i, first), (i, last)) for i in rows)
+    # both directions for directed
+    if G.is_directed():
+        G.add_edges_from((v, u) for u, v in G.edges())
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def grid_graph(dim, periodic=False):
+    """Returns the *n*-dimensional grid graph.
+
+    The dimension *n* is the length of the list `dim` and the size in
+    each dimension is the value of the corresponding list element.
+
+    Parameters
+    ----------
+    dim : list or tuple of numbers or iterables of nodes
+        'dim' is a tuple or list with, for each dimension, either a number
+        that is the size of that dimension or an iterable of nodes for
+        that dimension. The dimension of the grid_graph is the length
+        of `dim`.
+
+    periodic : bool or iterable
+        If `periodic` is True, all dimensions are periodic. If False all
+        dimensions are not periodic. If `periodic` is iterable, it should
+        yield `dim` bool values each of which indicates whether the
+        corresponding axis is periodic.
+
+    Returns
+    -------
+    NetworkX graph
+        The (possibly periodic) grid graph of the specified dimensions.
+
+    Examples
+    --------
+    To produce a 2 by 3 by 4 grid graph, a graph on 24 nodes:
+
+    >>> from networkx import grid_graph
+    >>> G = grid_graph(dim=(2, 3, 4))
+    >>> len(G)
+    24
+    >>> G = grid_graph(dim=(range(7, 9), range(3, 6)))
+    >>> len(G)
+    6
+    """
+    from networkx.algorithms.operators.product import cartesian_product
+
+    if not dim:
+        return empty_graph(0)
+
+    try:
+        func = (cycle_graph if p else path_graph for p in periodic)
+    except TypeError:
+        func = repeat(cycle_graph if periodic else path_graph)
+
+    G = next(func)(dim[0])
+    for current_dim in dim[1:]:
+        Gnew = next(func)(current_dim)
+        G = cartesian_product(Gnew, G)
+    # graph G is done but has labels of the form (1, (2, (3, 1))) so relabel
+    H = relabel_nodes(G, flatten)
+    return H
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hypercube_graph(n):
+    """Returns the *n*-dimensional hypercube graph.
+
+    The nodes are the integers between 0 and ``2 ** n - 1``, inclusive.
+
+    For more information on the hypercube graph, see the Wikipedia
+    article `Hypercube graph`_.
+
+    .. _Hypercube graph: https://en.wikipedia.org/wiki/Hypercube_graph
+
+    Parameters
+    ----------
+    n : int
+        The dimension of the hypercube.
+        The number of nodes in the graph will be ``2 ** n``.
+
+    Returns
+    -------
+    NetworkX graph
+        The hypercube graph of dimension *n*.
+    """
+    dim = n * [2]
+    G = grid_graph(dim)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def triangular_lattice_graph(
+    m, n, periodic=False, with_positions=True, create_using=None
+):
+    r"""Returns the $m$ by $n$ triangular lattice graph.
+
+    The `triangular lattice graph`_ is a two-dimensional `grid graph`_ in
+    which each square unit has a diagonal edge (each grid unit has a chord).
+
+    The returned graph has $m$ rows and $n$ columns of triangles. Rows and
+    columns include both triangles pointing up and down. Rows form a strip
+    of constant height. Columns form a series of diamond shapes, staggered
+    with the columns on either side. Another way to state the size is that
+    the nodes form a grid of `m+1` rows and `(n + 1) // 2` columns.
+    The odd row nodes are shifted horizontally relative to the even rows.
+
+    Directed graph types have edges pointed up or right.
+
+    Positions of nodes are computed by default or `with_positions is True`.
+    The position of each node (embedded in a euclidean plane) is stored in
+    the graph using equilateral triangles with sidelength 1.
+    The height between rows of nodes is thus $\sqrt(3)/2$.
+    Nodes lie in the first quadrant with the node $(0, 0)$ at the origin.
+
+    .. _triangular lattice graph: http://mathworld.wolfram.com/TriangularGrid.html
+    .. _grid graph: http://www-cs-students.stanford.edu/~amitp/game-programming/grids/
+    .. _Triangular Tiling: https://en.wikipedia.org/wiki/Triangular_tiling
+
+    Parameters
+    ----------
+    m : int
+        The number of rows in the lattice.
+
+    n : int
+        The number of columns in the lattice.
+
+    periodic : bool (default: False)
+        If True, join the boundary vertices of the grid using periodic
+        boundary conditions. The join between boundaries is the final row
+        and column of triangles. This means there is one row and one column
+        fewer nodes for the periodic lattice. Periodic lattices require
+        `m >= 3`, `n >= 5` and are allowed but misaligned if `m` or `n` are odd
+
+    with_positions : bool (default: True)
+        Store the coordinates of each node in the graph node attribute 'pos'.
+        The coordinates provide a lattice with equilateral triangles.
+        Periodic positions shift the nodes vertically in a nonlinear way so
+        the edges don't overlap so much.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        The *m* by *n* triangular lattice graph.
+    """
+    H = empty_graph(0, create_using)
+    if n == 0 or m == 0:
+        return H
+    if periodic:
+        if n < 5 or m < 3:
+            msg = f"m > 2 and n > 4 required for periodic. m={m}, n={n}"
+            raise NetworkXError(msg)
+
+    N = (n + 1) // 2  # number of nodes in row
+    rows = range(m + 1)
+    cols = range(N + 1)
+    # Make grid
+    H.add_edges_from(((i, j), (i + 1, j)) for j in rows for i in cols[:N])
+    H.add_edges_from(((i, j), (i, j + 1)) for j in rows[:m] for i in cols)
+    # add diagonals
+    H.add_edges_from(((i, j), (i + 1, j + 1)) for j in rows[1:m:2] for i in cols[:N])
+    H.add_edges_from(((i + 1, j), (i, j + 1)) for j in rows[:m:2] for i in cols[:N])
+    # identify boundary nodes if periodic
+    from networkx.algorithms.minors import contracted_nodes
+
+    if periodic is True:
+        for i in cols:
+            H = contracted_nodes(H, (i, 0), (i, m))
+        for j in rows[:m]:
+            H = contracted_nodes(H, (0, j), (N, j))
+    elif n % 2:
+        # remove extra nodes
+        H.remove_nodes_from((N, j) for j in rows[1::2])
+
+    # Add position node attributes
+    if with_positions:
+        ii = (i for i in cols for j in rows)
+        jj = (j for i in cols for j in rows)
+        xx = (0.5 * (j % 2) + i for i in cols for j in rows)
+        h = sqrt(3) / 2
+        if periodic:
+            yy = (h * j + 0.01 * i * i for i in cols for j in rows)
+        else:
+            yy = (h * j for i in cols for j in rows)
+        pos = {(i, j): (x, y) for i, j, x, y in zip(ii, jj, xx, yy) if (i, j) in H}
+        set_node_attributes(H, pos, "pos")
+    return H
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hexagonal_lattice_graph(
+    m, n, periodic=False, with_positions=True, create_using=None
+):
+    """Returns an `m` by `n` hexagonal lattice graph.
+
+    The *hexagonal lattice graph* is a graph whose nodes and edges are
+    the `hexagonal tiling`_ of the plane.
+
+    The returned graph will have `m` rows and `n` columns of hexagons.
+    `Odd numbered columns`_ are shifted up relative to even numbered columns.
+
+    Positions of nodes are computed by default or `with_positions is True`.
+    Node positions creating the standard embedding in the plane
+    with sidelength 1 and are stored in the node attribute 'pos'.
+    `pos = nx.get_node_attributes(G, 'pos')` creates a dict ready for drawing.
+
+    .. _hexagonal tiling: https://en.wikipedia.org/wiki/Hexagonal_tiling
+    .. _Odd numbered columns: http://www-cs-students.stanford.edu/~amitp/game-programming/grids/
+
+    Parameters
+    ----------
+    m : int
+        The number of rows of hexagons in the lattice.
+
+    n : int
+        The number of columns of hexagons in the lattice.
+
+    periodic : bool
+        Whether to make a periodic grid by joining the boundary vertices.
+        For this to work `n` must be even and both `n > 1` and `m > 1`.
+        The periodic connections create another row and column of hexagons
+        so these graphs have fewer nodes as boundary nodes are identified.
+
+    with_positions : bool (default: True)
+        Store the coordinates of each node in the graph node attribute 'pos'.
+        The coordinates provide a lattice with vertical columns of hexagons
+        offset to interleave and cover the plane.
+        Periodic positions shift the nodes vertically in a nonlinear way so
+        the edges don't overlap so much.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        If graph is directed, edges will point up or right.
+
+    Returns
+    -------
+    NetworkX graph
+        The *m* by *n* hexagonal lattice graph.
+    """
+    G = empty_graph(0, create_using)
+    if m == 0 or n == 0:
+        return G
+    if periodic and (n % 2 == 1 or m < 2 or n < 2):
+        msg = "periodic hexagonal lattice needs m > 1, n > 1 and even n"
+        raise NetworkXError(msg)
+
+    M = 2 * m  # twice as many nodes as hexagons vertically
+    rows = range(M + 2)
+    cols = range(n + 1)
+    # make lattice
+    col_edges = (((i, j), (i, j + 1)) for i in cols for j in rows[: M + 1])
+    row_edges = (((i, j), (i + 1, j)) for i in cols[:n] for j in rows if i % 2 == j % 2)
+    G.add_edges_from(col_edges)
+    G.add_edges_from(row_edges)
+    # Remove corner nodes with one edge
+    G.remove_node((0, M + 1))
+    G.remove_node((n, (M + 1) * (n % 2)))
+
+    # identify boundary nodes if periodic
+    from networkx.algorithms.minors import contracted_nodes
+
+    if periodic:
+        for i in cols[:n]:
+            G = contracted_nodes(G, (i, 0), (i, M))
+        for i in cols[1:]:
+            G = contracted_nodes(G, (i, 1), (i, M + 1))
+        for j in rows[1:M]:
+            G = contracted_nodes(G, (0, j), (n, j))
+        G.remove_node((n, M))
+
+    # calc position in embedded space
+    ii = (i for i in cols for j in rows)
+    jj = (j for i in cols for j in rows)
+    xx = (0.5 + i + i // 2 + (j % 2) * ((i % 2) - 0.5) for i in cols for j in rows)
+    h = sqrt(3) / 2
+    if periodic:
+        yy = (h * j + 0.01 * i * i for i in cols for j in rows)
+    else:
+        yy = (h * j for i in cols for j in rows)
+    # exclude nodes not in G
+    pos = {(i, j): (x, y) for i, j, x, y in zip(ii, jj, xx, yy) if (i, j) in G}
+    set_node_attributes(G, pos, "pos")
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/line.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/line.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c6274a40d04773725ede468c871652f40c9023a
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/line.py
@@ -0,0 +1,501 @@
+"""Functions for generating line graphs."""
+
+from collections import defaultdict
+from functools import partial
+from itertools import combinations
+
+import networkx as nx
+from networkx.utils import arbitrary_element
+from networkx.utils.decorators import not_implemented_for
+
+__all__ = ["line_graph", "inverse_line_graph"]
+
+
+@nx._dispatchable(returns_graph=True)
+def line_graph(G, create_using=None):
+    r"""Returns the line graph of the graph or digraph `G`.
+
+    The line graph of a graph `G` has a node for each edge in `G` and an
+    edge joining those nodes if the two edges in `G` share a common node. For
+    directed graphs, nodes are adjacent exactly when the edges they represent
+    form a directed path of length two.
+
+    The nodes of the line graph are 2-tuples of nodes in the original graph (or
+    3-tuples for multigraphs, with the key of the edge as the third element).
+
+    For information about self-loops and more discussion, see the **Notes**
+    section below.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX Graph, DiGraph, MultiGraph, or MultiDigraph.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    L : graph
+        The line graph of G.
+
+    Examples
+    --------
+    >>> G = nx.star_graph(3)
+    >>> L = nx.line_graph(G)
+    >>> print(sorted(map(sorted, L.edges())))  # makes a 3-clique, K3
+    [[(0, 1), (0, 2)], [(0, 1), (0, 3)], [(0, 2), (0, 3)]]
+
+    Edge attributes from `G` are not copied over as node attributes in `L`, but
+    attributes can be copied manually:
+
+    >>> G = nx.path_graph(4)
+    >>> G.add_edges_from((u, v, {"tot": u + v}) for u, v in G.edges)
+    >>> G.edges(data=True)
+    EdgeDataView([(0, 1, {'tot': 1}), (1, 2, {'tot': 3}), (2, 3, {'tot': 5})])
+    >>> H = nx.line_graph(G)
+    >>> H.add_nodes_from((node, G.edges[node]) for node in H)
+    >>> H.nodes(data=True)
+    NodeDataView({(0, 1): {'tot': 1}, (2, 3): {'tot': 5}, (1, 2): {'tot': 3}})
+
+    Notes
+    -----
+    Graph, node, and edge data are not propagated to the new graph. For
+    undirected graphs, the nodes in G must be sortable, otherwise the
+    constructed line graph may not be correct.
+
+    *Self-loops in undirected graphs*
+
+    For an undirected graph `G` without multiple edges, each edge can be
+    written as a set `\{u, v\}`.  Its line graph `L` has the edges of `G` as
+    its nodes. If `x` and `y` are two nodes in `L`, then `\{x, y\}` is an edge
+    in `L` if and only if the intersection of `x` and `y` is nonempty. Thus,
+    the set of all edges is determined by the set of all pairwise intersections
+    of edges in `G`.
+
+    Trivially, every edge in G would have a nonzero intersection with itself,
+    and so every node in `L` should have a self-loop. This is not so
+    interesting, and the original context of line graphs was with simple
+    graphs, which had no self-loops or multiple edges. The line graph was also
+    meant to be a simple graph and thus, self-loops in `L` are not part of the
+    standard definition of a line graph. In a pairwise intersection matrix,
+    this is analogous to excluding the diagonal entries from the line graph
+    definition.
+
+    Self-loops and multiple edges in `G` add nodes to `L` in a natural way, and
+    do not require any fundamental changes to the definition. It might be
+    argued that the self-loops we excluded before should now be included.
+    However, the self-loops are still "trivial" in some sense and thus, are
+    usually excluded.
+
+    *Self-loops in directed graphs*
+
+    For a directed graph `G` without multiple edges, each edge can be written
+    as a tuple `(u, v)`. Its line graph `L` has the edges of `G` as its
+    nodes. If `x` and `y` are two nodes in `L`, then `(x, y)` is an edge in `L`
+    if and only if the tail of `x` matches the head of `y`, for example, if `x
+    = (a, b)` and `y = (b, c)` for some vertices `a`, `b`, and `c` in `G`.
+
+    Due to the directed nature of the edges, it is no longer the case that
+    every edge in `G` should have a self-loop in `L`. Now, the only time
+    self-loops arise is if a node in `G` itself has a self-loop.  So such
+    self-loops are no longer "trivial" but instead, represent essential
+    features of the topology of `G`. For this reason, the historical
+    development of line digraphs is such that self-loops are included. When the
+    graph `G` has multiple edges, once again only superficial changes are
+    required to the definition.
+
+    References
+    ----------
+    * Harary, Frank, and Norman, Robert Z., "Some properties of line digraphs",
+      Rend. Circ. Mat. Palermo, II. Ser. 9 (1960), 161--168.
+    * Hemminger, R. L.; Beineke, L. W. (1978), "Line graphs and line digraphs",
+      in Beineke, L. W.; Wilson, R. J., Selected Topics in Graph Theory,
+      Academic Press Inc., pp. 271--305.
+
+    """
+    if G.is_directed():
+        L = _lg_directed(G, create_using=create_using)
+    else:
+        L = _lg_undirected(G, selfloops=False, create_using=create_using)
+    return L
+
+
+def _lg_directed(G, create_using=None):
+    """Returns the line graph L of the (multi)digraph G.
+
+    Edges in G appear as nodes in L, represented as tuples of the form (u,v)
+    or (u,v,key) if G is a multidigraph. A node in L corresponding to the edge
+    (u,v) is connected to every node corresponding to an edge (v,w).
+
+    Parameters
+    ----------
+    G : digraph
+        A directed graph or directed multigraph.
+    create_using : NetworkX graph constructor, optional
+       Graph type to create. If graph instance, then cleared before populated.
+       Default is to use the same graph class as `G`.
+
+    """
+    L = nx.empty_graph(0, create_using, default=G.__class__)
+
+    # Create a graph specific edge function.
+    get_edges = partial(G.edges, keys=True) if G.is_multigraph() else G.edges
+
+    for from_node in get_edges():
+        # from_node is: (u,v) or (u,v,key)
+        L.add_node(from_node)
+        for to_node in get_edges(from_node[1]):
+            L.add_edge(from_node, to_node)
+
+    return L
+
+
+def _lg_undirected(G, selfloops=False, create_using=None):
+    """Returns the line graph L of the (multi)graph G.
+
+    Edges in G appear as nodes in L, represented as sorted tuples of the form
+    (u,v), or (u,v,key) if G is a multigraph. A node in L corresponding to
+    the edge {u,v} is connected to every node corresponding to an edge that
+    involves u or v.
+
+    Parameters
+    ----------
+    G : graph
+        An undirected graph or multigraph.
+    selfloops : bool
+        If `True`, then self-loops are included in the line graph. If `False`,
+        they are excluded.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Notes
+    -----
+    The standard algorithm for line graphs of undirected graphs does not
+    produce self-loops.
+
+    """
+    L = nx.empty_graph(0, create_using, default=G.__class__)
+
+    # Graph specific functions for edges.
+    get_edges = partial(G.edges, keys=True) if G.is_multigraph() else G.edges
+
+    # Determine if we include self-loops or not.
+    shift = 0 if selfloops else 1
+
+    # Introduce numbering of nodes
+    node_index = {n: i for i, n in enumerate(G)}
+
+    # Lift canonical representation of nodes to edges in line graph
+    def edge_key_function(edge):
+        return node_index[edge[0]], node_index[edge[1]]
+
+    edges = set()
+    for u in G:
+        # Label nodes as a sorted tuple of nodes in original graph.
+        # Decide on representation of {u, v} as (u, v) or (v, u) depending on node_index.
+        # -> This ensures a canonical representation and avoids comparing values of different types.
+        nodes = [tuple(sorted(x[:2], key=node_index.get)) + x[2:] for x in get_edges(u)]
+
+        if len(nodes) == 1:
+            # Then the edge will be an isolated node in L.
+            L.add_node(nodes[0])
+
+        # Add a clique of `nodes` to graph. To prevent double adding edges,
+        # especially important for multigraphs, we store the edges in
+        # canonical form in a set.
+        for i, a in enumerate(nodes):
+            edges.update(
+                [
+                    tuple(sorted((a, b), key=edge_key_function))
+                    for b in nodes[i + shift :]
+                ]
+            )
+
+    L.add_edges_from(edges)
+    return L
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(returns_graph=True)
+def inverse_line_graph(G):
+    """Returns the inverse line graph of graph G.
+
+    If H is a graph, and G is the line graph of H, such that G = L(H).
+    Then H is the inverse line graph of G.
+
+    Not all graphs are line graphs and these do not have an inverse line graph.
+    In these cases this function raises a NetworkXError.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX Graph
+
+    Returns
+    -------
+    H : graph
+        The inverse line graph of G.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is directed or a multigraph
+
+    NetworkXError
+        If G is not a line graph
+
+    Notes
+    -----
+    This is an implementation of the Roussopoulos algorithm[1]_.
+
+    If G consists of multiple components, then the algorithm doesn't work.
+    You should invert every component separately:
+
+    >>> K5 = nx.complete_graph(5)
+    >>> P4 = nx.Graph([("a", "b"), ("b", "c"), ("c", "d")])
+    >>> G = nx.union(K5, P4)
+    >>> root_graphs = []
+    >>> for comp in nx.connected_components(G):
+    ...     root_graphs.append(nx.inverse_line_graph(G.subgraph(comp)))
+    >>> len(root_graphs)
+    2
+
+    References
+    ----------
+    .. [1] Roussopoulos, N.D. , "A max {m, n} algorithm for determining the graph H from
+       its line graph G", Information Processing Letters 2, (1973), 108--112, ISSN 0020-0190,
+       `DOI link <https://doi.org/10.1016/0020-0190(73)90029-X>`_
+
+    """
+    if G.number_of_nodes() == 0:
+        return nx.empty_graph(1)
+    elif G.number_of_nodes() == 1:
+        v = arbitrary_element(G)
+        a = (v, 0)
+        b = (v, 1)
+        H = nx.Graph([(a, b)])
+        return H
+    elif G.number_of_nodes() > 1 and G.number_of_edges() == 0:
+        msg = (
+            "inverse_line_graph() doesn't work on an edgeless graph. "
+            "Please use this function on each component separately."
+        )
+        raise nx.NetworkXError(msg)
+
+    if nx.number_of_selfloops(G) != 0:
+        msg = (
+            "A line graph as generated by NetworkX has no selfloops, so G has no "
+            "inverse line graph. Please remove the selfloops from G and try again."
+        )
+        raise nx.NetworkXError(msg)
+
+    starting_cell = _select_starting_cell(G)
+    P = _find_partition(G, starting_cell)
+    # count how many times each vertex appears in the partition set
+    P_count = dict.fromkeys(G.nodes, 0)
+    for p in P:
+        for u in p:
+            P_count[u] += 1
+
+    if max(P_count.values()) > 2:
+        msg = "G is not a line graph (vertex found in more than two partition cells)"
+        raise nx.NetworkXError(msg)
+    W = tuple((u,) for u in P_count if P_count[u] == 1)
+    H = nx.Graph()
+    H.add_nodes_from(P)
+    H.add_nodes_from(W)
+    for a, b in combinations(H.nodes, 2):
+        if any(a_bit in b for a_bit in a):
+            H.add_edge(a, b)
+    return H
+
+
+def _triangles(G, e):
+    """Return list of all triangles containing edge e"""
+    u, v = e
+    if u not in G:
+        raise nx.NetworkXError(f"Vertex {u} not in graph")
+    if v not in G[u]:
+        raise nx.NetworkXError(f"Edge ({u}, {v}) not in graph")
+    triangle_list = []
+    for x in G[u]:
+        if x in G[v]:
+            triangle_list.append((u, v, x))
+    return triangle_list
+
+
+def _odd_triangle(G, T):
+    """Test whether T is an odd triangle in G
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    T : 3-tuple of vertices forming triangle in G
+
+    Returns
+    -------
+    True is T is an odd triangle
+    False otherwise
+
+    Raises
+    ------
+    NetworkXError
+        T is not a triangle in G
+
+    Notes
+    -----
+    An odd triangle is one in which there exists another vertex in G which is
+    adjacent to either exactly one or exactly all three of the vertices in the
+    triangle.
+
+    """
+    for u in T:
+        if u not in G.nodes():
+            raise nx.NetworkXError(f"Vertex {u} not in graph")
+    for e in list(combinations(T, 2)):
+        if e[0] not in G[e[1]]:
+            raise nx.NetworkXError(f"Edge ({e[0]}, {e[1]}) not in graph")
+
+    T_nbrs = defaultdict(int)
+    for t in T:
+        for v in G[t]:
+            if v not in T:
+                T_nbrs[v] += 1
+    return any(T_nbrs[v] in [1, 3] for v in T_nbrs)
+
+
+def _find_partition(G, starting_cell):
+    """Find a partition of the vertices of G into cells of complete graphs
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    starting_cell : tuple of vertices in G which form a cell
+
+    Returns
+    -------
+    List of tuples of vertices of G
+
+    Raises
+    ------
+    NetworkXError
+        If a cell is not a complete subgraph then G is not a line graph
+    """
+    G_partition = G.copy()
+    P = [starting_cell]  # partition set
+    G_partition.remove_edges_from(list(combinations(starting_cell, 2)))
+    # keep list of partitioned nodes which might have an edge in G_partition
+    partitioned_vertices = list(starting_cell)
+    while G_partition.number_of_edges() > 0:
+        # there are still edges left and so more cells to be made
+        u = partitioned_vertices.pop()
+        deg_u = len(G_partition[u])
+        if deg_u != 0:
+            # if u still has edges then we need to find its other cell
+            # this other cell must be a complete subgraph or else G is
+            # not a line graph
+            new_cell = [u] + list(G_partition[u])
+            for u in new_cell:
+                for v in new_cell:
+                    if (u != v) and (v not in G_partition[u]):
+                        msg = (
+                            "G is not a line graph "
+                            "(partition cell not a complete subgraph)"
+                        )
+                        raise nx.NetworkXError(msg)
+            P.append(tuple(new_cell))
+            G_partition.remove_edges_from(list(combinations(new_cell, 2)))
+            partitioned_vertices += new_cell
+    return P
+
+
+def _select_starting_cell(G, starting_edge=None):
+    """Select a cell to initiate _find_partition
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    starting_edge: an edge to build the starting cell from
+
+    Returns
+    -------
+    Tuple of vertices in G
+
+    Raises
+    ------
+    NetworkXError
+        If it is determined that G is not a line graph
+
+    Notes
+    -----
+    If starting edge not specified then pick an arbitrary edge - doesn't
+    matter which. However, this function may call itself requiring a
+    specific starting edge. Note that the r, s notation for counting
+    triangles is the same as in the Roussopoulos paper cited above.
+    """
+    if starting_edge is None:
+        e = arbitrary_element(G.edges())
+    else:
+        e = starting_edge
+        if e[0] not in G.nodes():
+            raise nx.NetworkXError(f"Vertex {e[0]} not in graph")
+        if e[1] not in G[e[0]]:
+            msg = f"starting_edge ({e[0]}, {e[1]}) is not in the Graph"
+            raise nx.NetworkXError(msg)
+    e_triangles = _triangles(G, e)
+    r = len(e_triangles)
+    if r == 0:
+        # there are no triangles containing e, so the starting cell is just e
+        starting_cell = e
+    elif r == 1:
+        # there is exactly one triangle, T, containing e. If other 2 edges
+        # of T belong only to this triangle then T is starting cell
+        T = e_triangles[0]
+        a, b, c = T
+        # ab was original edge so check the other 2 edges
+        ac_edges = len(_triangles(G, (a, c)))
+        bc_edges = len(_triangles(G, (b, c)))
+        if ac_edges == 1:
+            if bc_edges == 1:
+                starting_cell = T
+            else:
+                return _select_starting_cell(G, starting_edge=(b, c))
+        else:
+            return _select_starting_cell(G, starting_edge=(a, c))
+    else:
+        # r >= 2 so we need to count the number of odd triangles, s
+        s = 0
+        odd_triangles = []
+        for T in e_triangles:
+            if _odd_triangle(G, T):
+                s += 1
+                odd_triangles.append(T)
+        if r == 2 and s == 0:
+            # in this case either triangle works, so just use T
+            starting_cell = T
+        elif r - 1 <= s <= r:
+            # check if odd triangles containing e form complete subgraph
+            triangle_nodes = set()
+            for T in odd_triangles:
+                for x in T:
+                    triangle_nodes.add(x)
+
+            for u in triangle_nodes:
+                for v in triangle_nodes:
+                    if u != v and (v not in G[u]):
+                        msg = (
+                            "G is not a line graph (odd triangles "
+                            "do not form complete subgraph)"
+                        )
+                        raise nx.NetworkXError(msg)
+            # otherwise then we can use this as the starting cell
+            starting_cell = tuple(triangle_nodes)
+
+        else:
+            msg = (
+                "G is not a line graph (incorrect number of "
+                "odd triangles around starting edge)"
+            )
+            raise nx.NetworkXError(msg)
+    return starting_cell
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/mycielski.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/mycielski.py
new file mode 100644
index 0000000000000000000000000000000000000000..804b903692853d3c45b3b1b20898efeee9b71a5e
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/mycielski.py
@@ -0,0 +1,110 @@
+"""Functions related to the Mycielski Operation and the Mycielskian family
+of graphs.
+
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["mycielskian", "mycielski_graph"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(returns_graph=True)
+def mycielskian(G, iterations=1):
+    r"""Returns the Mycielskian of a simple, undirected graph G
+
+    The Mycielskian of graph preserves a graph's triangle free
+    property while increasing the chromatic number by 1.
+
+    The Mycielski Operation on a graph, :math:`G=(V, E)`, constructs a new
+    graph with :math:`2|V| + 1` nodes and :math:`3|E| + |V|` edges.
+
+    The construction is as follows:
+
+    Let :math:`V = {0, ..., n-1}`. Construct another vertex set
+    :math:`U = {n, ..., 2n}` and a vertex, `w`.
+    Construct a new graph, `M`, with vertices :math:`U \bigcup V \bigcup w`.
+    For edges, :math:`(u, v) \in E` add edges :math:`(u, v), (u, v + n)`, and
+    :math:`(u + n, v)` to M. Finally, for all vertices :math:`u \in U`, add
+    edge :math:`(u, w)` to M.
+
+    The Mycielski Operation can be done multiple times by repeating the above
+    process iteratively.
+
+    More information can be found at https://en.wikipedia.org/wiki/Mycielskian
+
+    Parameters
+    ----------
+    G : graph
+        A simple, undirected NetworkX graph
+    iterations : int
+        The number of iterations of the Mycielski operation to
+        perform on G. Defaults to 1. Must be a non-negative integer.
+
+    Returns
+    -------
+    M : graph
+        The Mycielskian of G after the specified number of iterations.
+
+    Notes
+    -----
+    Graph, node, and edge data are not necessarily propagated to the new graph.
+
+    """
+
+    M = nx.convert_node_labels_to_integers(G)
+
+    for i in range(iterations):
+        n = M.number_of_nodes()
+        M.add_nodes_from(range(n, 2 * n))
+        old_edges = list(M.edges())
+        M.add_edges_from((u, v + n) for u, v in old_edges)
+        M.add_edges_from((u + n, v) for u, v in old_edges)
+        M.add_node(2 * n)
+        M.add_edges_from((u + n, 2 * n) for u in range(n))
+
+    return M
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def mycielski_graph(n):
+    """Generator for the n_th Mycielski Graph.
+
+    The Mycielski family of graphs is an infinite set of graphs.
+    :math:`M_1` is the singleton graph, :math:`M_2` is two vertices with an
+    edge, and, for :math:`i > 2`, :math:`M_i` is the Mycielskian of
+    :math:`M_{i-1}`.
+
+    More information can be found at
+    http://mathworld.wolfram.com/MycielskiGraph.html
+
+    Parameters
+    ----------
+    n : int
+        The desired Mycielski Graph.
+
+    Returns
+    -------
+    M : graph
+        The n_th Mycielski Graph
+
+    Notes
+    -----
+    The first graph in the Mycielski sequence is the singleton graph.
+    The Mycielskian of this graph is not the :math:`P_2` graph, but rather the
+    :math:`P_2` graph with an extra, isolated vertex. The second Mycielski
+    graph is the :math:`P_2` graph, so the first two are hard coded.
+    The remaining graphs are generated using the Mycielski operation.
+
+    """
+
+    if n < 1:
+        raise nx.NetworkXError("must satisfy n >= 1")
+
+    if n == 1:
+        return nx.empty_graph(1)
+
+    else:
+        return mycielskian(nx.path_graph(2), n - 2)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/nonisomorphic_trees.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/nonisomorphic_trees.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f19f9353f854f9d76dc1b3fe6a4e8e66745d675
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/nonisomorphic_trees.py
@@ -0,0 +1,209 @@
+"""
+Implementation of the Wright, Richmond, Odlyzko and McKay (WROM)
+algorithm for the enumeration of all non-isomorphic free trees of a
+given order.  Rooted trees are represented by level sequences, i.e.,
+lists in which the i-th element specifies the distance of vertex i to
+the root.
+
+"""
+
+__all__ = ["nonisomorphic_trees", "number_of_nonisomorphic_trees"]
+
+from functools import lru_cache
+
+import networkx as nx
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def nonisomorphic_trees(order):
+    """Generates lists of nonisomorphic trees
+
+    Parameters
+    ----------
+    order : int
+       order of the desired tree(s)
+
+    Yields
+    ------
+    list of `networkx.Graph` instances
+       A list of nonisomorphic trees
+    """
+
+    if order < 2:
+        raise ValueError
+    # start at the path graph rooted at its center
+    layout = list(range(order // 2 + 1)) + list(range(1, (order + 1) // 2))
+
+    while layout is not None:
+        layout = _next_tree(layout)
+        if layout is not None:
+            yield _layout_to_graph(layout)
+            layout = _next_rooted_tree(layout)
+
+
+@nx._dispatchable(graphs=None)
+def number_of_nonisomorphic_trees(order):
+    """Returns the number of nonisomorphic trees
+
+    Based on an algorithm by Alois P. Heinz in
+    `OEIS entry A000055 <https://oeis.org/A000055>`_. Complexity is ``O(n ** 3)``
+
+    Parameters
+    ----------
+    order : int
+      order of the desired tree(s)
+
+    Returns
+    -------
+    int
+       Number of nonisomorphic graphs for the given order
+    """
+    if order < 2:
+        raise ValueError
+    return _unlabeled_trees(order)
+
+
+@lru_cache(None)
+def _unlabeled_trees(n):
+    """Implements OEIS A000055 (number of unlabeled trees)."""
+
+    value = 0
+    for k in range(n + 1):
+        value += _rooted_trees(k) * _rooted_trees(n - k)
+    if n % 2 == 0:
+        value -= _rooted_trees(n // 2)
+    return _rooted_trees(n) - value // 2
+
+
+@lru_cache(None)
+def _rooted_trees(n):
+    """Implements OEIS A000081 (number of unlabeled rooted trees)."""
+
+    if n < 2:
+        return n
+    value = 0
+    for j in range(1, n):
+        for d in range(1, n):
+            if j % d == 0:
+                value += d * _rooted_trees(d) * _rooted_trees(n - j)
+    return value // (n - 1)
+
+
+def _next_rooted_tree(predecessor, p=None):
+    """One iteration of the Beyer-Hedetniemi algorithm."""
+
+    if p is None:
+        p = len(predecessor) - 1
+        while predecessor[p] == 1:
+            p -= 1
+    if p == 0:
+        return None
+
+    q = p - 1
+    while predecessor[q] != predecessor[p] - 1:
+        q -= 1
+    result = list(predecessor)
+    for i in range(p, len(result)):
+        result[i] = result[i - p + q]
+    return result
+
+
+def _next_tree(candidate):
+    """One iteration of the Wright, Richmond, Odlyzko and McKay
+    algorithm."""
+
+    # valid representation of a free tree if:
+    # there are at least two vertices at layer 1
+    # (this is always the case because we start at the path graph)
+    left, rest = _split_tree(candidate)
+
+    # and the left subtree of the root
+    # is less high than the tree with the left subtree removed
+    left_height = max(left)
+    rest_height = max(rest)
+    valid = rest_height >= left_height
+
+    if valid and rest_height == left_height:
+        # and, if left and rest are of the same height,
+        # if left does not encompass more vertices
+        if len(left) > len(rest):
+            valid = False
+        # and, if they have the same number or vertices,
+        # if left does not come after rest lexicographically
+        elif len(left) == len(rest) and left > rest:
+            valid = False
+
+    if valid:
+        return candidate
+    else:
+        # jump to the next valid free tree
+        p = len(left)
+        new_candidate = _next_rooted_tree(candidate, p)
+        if candidate[p] > 2:
+            new_left, new_rest = _split_tree(new_candidate)
+            new_left_height = max(new_left)
+            suffix = range(1, new_left_height + 2)
+            new_candidate[-len(suffix) :] = suffix
+        return new_candidate
+
+
+def _split_tree(layout):
+    """Returns a tuple of two layouts, one containing the left
+    subtree of the root vertex, and one containing the original tree
+    with the left subtree removed."""
+
+    one_found = False
+    m = None
+    for i in range(len(layout)):
+        if layout[i] == 1:
+            if one_found:
+                m = i
+                break
+            else:
+                one_found = True
+
+    if m is None:
+        m = len(layout)
+
+    left = [layout[i] - 1 for i in range(1, m)]
+    rest = [0] + [layout[i] for i in range(m, len(layout))]
+    return (left, rest)
+
+
+def _layout_to_matrix(layout):
+    """Create the adjacency matrix for the tree specified by the
+    given layout (level sequence)."""
+
+    result = [[0] * len(layout) for i in range(len(layout))]
+    stack = []
+    for i in range(len(layout)):
+        i_level = layout[i]
+        if stack:
+            j = stack[-1]
+            j_level = layout[j]
+            while j_level >= i_level:
+                stack.pop()
+                j = stack[-1]
+                j_level = layout[j]
+            result[i][j] = result[j][i] = 1
+        stack.append(i)
+    return result
+
+
+def _layout_to_graph(layout):
+    """Create a NetworkX Graph for the tree specified by the
+    given layout(level sequence)"""
+    G = nx.Graph()
+    stack = []
+    for i in range(len(layout)):
+        i_level = layout[i]
+        if stack:
+            j = stack[-1]
+            j_level = layout[j]
+            while j_level >= i_level:
+                stack.pop()
+                j = stack[-1]
+                j_level = layout[j]
+            G.add_edge(i, j)
+        stack.append(i)
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/random_clustered.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/random_clustered.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fbf855e672d3c50f1e74952cc2272143fbac57a
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/random_clustered.py
@@ -0,0 +1,117 @@
+"""Generate graphs with given degree and triangle sequence."""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["random_clustered_graph"]
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_clustered_graph(joint_degree_sequence, create_using=None, seed=None):
+    r"""Generate a random graph with the given joint independent edge degree and
+    triangle degree sequence.
+
+    This uses a configuration model-like approach to generate a random graph
+    (with parallel edges and self-loops) by randomly assigning edges to match
+    the given joint degree sequence.
+
+    The joint degree sequence is a list of pairs of integers of the form
+    $[(d_{1,i}, d_{1,t}), \dotsc, (d_{n,i}, d_{n,t})]$. According to this list,
+    vertex $u$ is a member of $d_{u,t}$ triangles and has $d_{u, i}$ other
+    edges. The number $d_{u,t}$ is the *triangle degree* of $u$ and the number
+    $d_{u,i}$ is the *independent edge degree*.
+
+    Parameters
+    ----------
+    joint_degree_sequence : list of integer pairs
+        Each list entry corresponds to the independent edge degree and
+        triangle degree of a node.
+    create_using : NetworkX graph constructor, optional (default MultiGraph)
+       Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : MultiGraph
+        A graph with the specified degree sequence. Nodes are labeled
+        starting at 0 with an index corresponding to the position in
+        deg_sequence.
+
+    Raises
+    ------
+    NetworkXError
+        If the independent edge degree sequence sum is not even
+        or the triangle degree sequence sum is not divisible by 3.
+
+    Notes
+    -----
+    As described by Miller [1]_ (see also Newman [2]_ for an equivalent
+    description).
+
+    A non-graphical degree sequence (not realizable by some simple
+    graph) is allowed since this function returns graphs with self
+    loops and parallel edges.  An exception is raised if the
+    independent degree sequence does not have an even sum or the
+    triangle degree sequence sum is not divisible by 3.
+
+    This configuration model-like construction process can lead to
+    duplicate edges and loops.  You can remove the self-loops and
+    parallel edges (see below) which will likely result in a graph
+    that doesn't have the exact degree sequence specified.  This
+    "finite-size effect" decreases as the size of the graph increases.
+
+    References
+    ----------
+    .. [1] Joel C. Miller. "Percolation and epidemics in random clustered
+           networks". In: Physical review. E, Statistical, nonlinear, and soft
+           matter physics 80 (2 Part 1 August 2009).
+    .. [2] M. E. J. Newman. "Random Graphs with Clustering".
+           In: Physical Review Letters 103 (5 July 2009)
+
+    Examples
+    --------
+    >>> deg = [(1, 0), (1, 0), (1, 0), (2, 0), (1, 0), (2, 1), (0, 1), (0, 1)]
+    >>> G = nx.random_clustered_graph(deg)
+
+    To remove parallel edges:
+
+    >>> G = nx.Graph(G)
+
+    To remove self loops:
+
+    >>> G.remove_edges_from(nx.selfloop_edges(G))
+
+    """
+    # In Python 3, zip() returns an iterator. Make this into a list.
+    joint_degree_sequence = list(joint_degree_sequence)
+
+    N = len(joint_degree_sequence)
+    G = nx.empty_graph(N, create_using, default=nx.MultiGraph)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    ilist = []
+    tlist = []
+    for n in G:
+        degrees = joint_degree_sequence[n]
+        for icount in range(degrees[0]):
+            ilist.append(n)
+        for tcount in range(degrees[1]):
+            tlist.append(n)
+
+    if len(ilist) % 2 != 0 or len(tlist) % 3 != 0:
+        raise nx.NetworkXError("Invalid degree sequence")
+
+    seed.shuffle(ilist)
+    seed.shuffle(tlist)
+    while ilist:
+        G.add_edge(ilist.pop(), ilist.pop())
+    while tlist:
+        n1 = tlist.pop()
+        n2 = tlist.pop()
+        n3 = tlist.pop()
+        G.add_edges_from([(n1, n2), (n1, n3), (n2, n3)])
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/random_graphs.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/random_graphs.py
new file mode 100644
index 0000000000000000000000000000000000000000..90ae0d973ac42550280bf74db0d2fef3117d58da
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/random_graphs.py
@@ -0,0 +1,1400 @@
+"""
+Generators for random graphs.
+
+"""
+
+import itertools
+import math
+from collections import defaultdict
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+from ..utils.misc import check_create_using
+from .classic import complete_graph, empty_graph, path_graph, star_graph
+from .degree_seq import degree_sequence_tree
+
+__all__ = [
+    "fast_gnp_random_graph",
+    "gnp_random_graph",
+    "dense_gnm_random_graph",
+    "gnm_random_graph",
+    "erdos_renyi_graph",
+    "binomial_graph",
+    "newman_watts_strogatz_graph",
+    "watts_strogatz_graph",
+    "connected_watts_strogatz_graph",
+    "random_regular_graph",
+    "barabasi_albert_graph",
+    "dual_barabasi_albert_graph",
+    "extended_barabasi_albert_graph",
+    "powerlaw_cluster_graph",
+    "random_lobster",
+    "random_shell_graph",
+    "random_powerlaw_tree",
+    "random_powerlaw_tree_sequence",
+    "random_kernel_graph",
+]
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def fast_gnp_random_graph(n, p, seed=None, directed=False, *, create_using=None):
+    """Returns a $G_{n,p}$ random graph, also known as an Erdős-Rényi graph or
+    a binomial graph.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    p : float
+        Probability for edge creation.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : bool, optional (default=False)
+        If True, this function returns a directed graph.
+    create_using : Graph constructor, optional (default=nx.Graph or nx.DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph types are not supported and raise a ``NetworkXError``.
+        By default NetworkX Graph or DiGraph are used depending on `directed`.
+
+    Notes
+    -----
+    The $G_{n,p}$ graph algorithm chooses each of the $[n (n - 1)] / 2$
+    (undirected) or $n (n - 1)$ (directed) possible edges with probability $p$.
+
+    This algorithm [1]_ runs in $O(n + m)$ time, where `m` is the expected number of
+    edges, which equals $p n (n - 1) / 2$. This should be faster than
+    :func:`gnp_random_graph` when $p$ is small and the expected number of edges
+    is small (that is, the graph is sparse).
+
+    See Also
+    --------
+    gnp_random_graph
+
+    References
+    ----------
+    .. [1] Vladimir Batagelj and Ulrik Brandes,
+       "Efficient generation of large random networks",
+       Phys. Rev. E, 71, 036113, 2005.
+    """
+    default = nx.DiGraph if directed else nx.Graph
+    create_using = check_create_using(
+        create_using, directed=directed, multigraph=False, default=default
+    )
+    if p <= 0 or p >= 1:
+        return nx.gnp_random_graph(
+            n, p, seed=seed, directed=directed, create_using=create_using
+        )
+
+    G = empty_graph(n, create_using=create_using)
+
+    lp = math.log(1.0 - p)
+
+    if directed:
+        v = 1
+        w = -1
+        while v < n:
+            lr = math.log(1.0 - seed.random())
+            w = w + 1 + int(lr / lp)
+            while w >= v and v < n:
+                w = w - v
+                v = v + 1
+            if v < n:
+                G.add_edge(w, v)
+
+    # Nodes in graph are from 0,n-1 (start with v as the second node index).
+    v = 1
+    w = -1
+    while v < n:
+        lr = math.log(1.0 - seed.random())
+        w = w + 1 + int(lr / lp)
+        while w >= v and v < n:
+            w = w - v
+            v = v + 1
+        if v < n:
+            G.add_edge(v, w)
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gnp_random_graph(n, p, seed=None, directed=False, *, create_using=None):
+    """Returns a $G_{n,p}$ random graph, also known as an Erdős-Rényi graph
+    or a binomial graph.
+
+    The $G_{n,p}$ model chooses each of the possible edges with probability $p$.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    p : float
+        Probability for edge creation.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : bool, optional (default=False)
+        If True, this function returns a directed graph.
+    create_using : Graph constructor, optional (default=nx.Graph or nx.DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph types are not supported and raise a ``NetworkXError``.
+        By default NetworkX Graph or DiGraph are used depending on `directed`.
+
+    See Also
+    --------
+    fast_gnp_random_graph
+
+    Notes
+    -----
+    This algorithm [2]_ runs in $O(n^2)$ time.  For sparse graphs (that is, for
+    small values of $p$), :func:`fast_gnp_random_graph` is a faster algorithm.
+
+    :func:`binomial_graph` and :func:`erdos_renyi_graph` are
+    aliases for :func:`gnp_random_graph`.
+
+    >>> nx.binomial_graph is nx.gnp_random_graph
+    True
+    >>> nx.erdos_renyi_graph is nx.gnp_random_graph
+    True
+
+    References
+    ----------
+    .. [1] P. Erdős and A. Rényi, On Random Graphs, Publ. Math. 6, 290 (1959).
+    .. [2] E. N. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).
+    """
+    default = nx.DiGraph if directed else nx.Graph
+    create_using = check_create_using(
+        create_using, directed=directed, multigraph=False, default=default
+    )
+    if p >= 1:
+        return complete_graph(n, create_using=create_using)
+
+    G = nx.empty_graph(n, create_using=create_using)
+    if p <= 0:
+        return G
+
+    edgetool = itertools.permutations if directed else itertools.combinations
+    for e in edgetool(range(n), 2):
+        if seed.random() < p:
+            G.add_edge(*e)
+    return G
+
+
+# add some aliases to common names
+binomial_graph = gnp_random_graph
+erdos_renyi_graph = gnp_random_graph
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def dense_gnm_random_graph(n, m, seed=None, *, create_using=None):
+    """Returns a $G_{n,m}$ random graph.
+
+    In the $G_{n,m}$ model, a graph is chosen uniformly at random from the set
+    of all graphs with $n$ nodes and $m$ edges.
+
+    This algorithm should be faster than :func:`gnm_random_graph` for dense
+    graphs.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    m : int
+        The number of edges.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    See Also
+    --------
+    gnm_random_graph
+
+    Notes
+    -----
+    Algorithm by Keith M. Briggs Mar 31, 2006.
+    Inspired by Knuth's Algorithm S (Selection sampling technique),
+    in section 3.4.2 of [1]_.
+
+    References
+    ----------
+    .. [1] Donald E. Knuth, The Art of Computer Programming,
+        Volume 2/Seminumerical algorithms, Third Edition, Addison-Wesley, 1997.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    mmax = n * (n - 1) // 2
+    if m >= mmax:
+        return complete_graph(n, create_using)
+    G = empty_graph(n, create_using)
+
+    if n == 1:
+        return G
+
+    u = 0
+    v = 1
+    t = 0
+    k = 0
+    while True:
+        if seed.randrange(mmax - t) < m - k:
+            G.add_edge(u, v)
+            k += 1
+            if k == m:
+                return G
+        t += 1
+        v += 1
+        if v == n:  # go to next row of adjacency matrix
+            u += 1
+            v = u + 1
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gnm_random_graph(n, m, seed=None, directed=False, *, create_using=None):
+    """Returns a $G_{n,m}$ random graph.
+
+    In the $G_{n,m}$ model, a graph is chosen uniformly at random from the set
+    of all graphs with $n$ nodes and $m$ edges.
+
+    This algorithm should be faster than :func:`dense_gnm_random_graph` for
+    sparse graphs.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    m : int
+        The number of edges.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    directed : bool, optional (default=False)
+        If True return a directed graph
+    create_using : Graph constructor, optional (default=nx.Graph or nx.DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph types are not supported and raise a ``NetworkXError``.
+        By default NetworkX Graph or DiGraph are used depending on `directed`.
+
+    See also
+    --------
+    dense_gnm_random_graph
+
+    """
+    default = nx.DiGraph if directed else nx.Graph
+    create_using = check_create_using(
+        create_using, directed=directed, multigraph=False, default=default
+    )
+    if n == 1:
+        return nx.empty_graph(n, create_using=create_using)
+    max_edges = n * (n - 1) if directed else n * (n - 1) / 2.0
+    if m >= max_edges:
+        return complete_graph(n, create_using=create_using)
+
+    G = nx.empty_graph(n, create_using=create_using)
+    nlist = list(G)
+    edge_count = 0
+    while edge_count < m:
+        # generate random edge,u,v
+        u = seed.choice(nlist)
+        v = seed.choice(nlist)
+        if u == v or G.has_edge(u, v):
+            continue
+        else:
+            G.add_edge(u, v)
+            edge_count = edge_count + 1
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def newman_watts_strogatz_graph(n, k, p, seed=None, *, create_using=None):
+    """Returns a Newman–Watts–Strogatz small-world graph.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    k : int
+        Each node is joined with its `k` nearest neighbors in a ring
+        topology.
+    p : float
+        The probability of adding a new edge for each edge.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    First create a ring over $n$ nodes [1]_.  Then each node in the ring is
+    connected with its $k$ nearest neighbors (or $k - 1$ neighbors if $k$
+    is odd).  Then shortcuts are created by adding new edges as follows: for
+    each edge $(u, v)$ in the underlying "$n$-ring with $k$ nearest
+    neighbors" with probability $p$ add a new edge $(u, w)$ with
+    randomly-chosen existing node $w$.  In contrast with
+    :func:`watts_strogatz_graph`, no edges are removed.
+
+    See Also
+    --------
+    watts_strogatz_graph
+
+    References
+    ----------
+    .. [1] M. E. J. Newman and D. J. Watts,
+       Renormalization group analysis of the small-world network model,
+       Physics Letters A, 263, 341, 1999.
+       https://doi.org/10.1016/S0375-9601(99)00757-4
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if k > n:
+        raise nx.NetworkXError("k>=n, choose smaller k or larger n")
+
+    # If k == n the graph return is a complete graph
+    if k == n:
+        return nx.complete_graph(n, create_using)
+
+    G = empty_graph(n, create_using)
+    nlist = list(G.nodes())
+    fromv = nlist
+    # connect the k/2 neighbors
+    for j in range(1, k // 2 + 1):
+        tov = fromv[j:] + fromv[0:j]  # the first j are now last
+        for i in range(len(fromv)):
+            G.add_edge(fromv[i], tov[i])
+    # for each edge u-v, with probability p, randomly select existing
+    # node w and add new edge u-w
+    e = list(G.edges())
+    for u, v in e:
+        if seed.random() < p:
+            w = seed.choice(nlist)
+            # no self-loops and reject if edge u-w exists
+            # is that the correct NWS model?
+            while w == u or G.has_edge(u, w):
+                w = seed.choice(nlist)
+                if G.degree(u) >= n - 1:
+                    break  # skip this rewiring
+            else:
+                G.add_edge(u, w)
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def watts_strogatz_graph(n, k, p, seed=None, *, create_using=None):
+    """Returns a Watts–Strogatz small-world graph.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    k : int
+        Each node is joined with its `k` nearest neighbors in a ring
+        topology.
+    p : float
+        The probability of rewiring each edge
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    See Also
+    --------
+    newman_watts_strogatz_graph
+    connected_watts_strogatz_graph
+
+    Notes
+    -----
+    First create a ring over $n$ nodes [1]_.  Then each node in the ring is joined
+    to its $k$ nearest neighbors (or $k - 1$ neighbors if $k$ is odd).
+    Then shortcuts are created by replacing some edges as follows: for each
+    edge $(u, v)$ in the underlying "$n$-ring with $k$ nearest neighbors"
+    with probability $p$ replace it with a new edge $(u, w)$ with uniformly
+    random choice of existing node $w$.
+
+    In contrast with :func:`newman_watts_strogatz_graph`, the random rewiring
+    does not increase the number of edges. The rewired graph is not guaranteed
+    to be connected as in :func:`connected_watts_strogatz_graph`.
+
+    References
+    ----------
+    .. [1] Duncan J. Watts and Steven H. Strogatz,
+       Collective dynamics of small-world networks,
+       Nature, 393, pp. 440--442, 1998.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if k > n:
+        raise nx.NetworkXError("k>n, choose smaller k or larger n")
+
+    # If k == n, the graph is complete not Watts-Strogatz
+    if k == n:
+        G = nx.complete_graph(n, create_using)
+        return G
+
+    G = nx.empty_graph(n, create_using=create_using)
+    nodes = list(range(n))  # nodes are labeled 0 to n-1
+    # connect each node to k/2 neighbors
+    for j in range(1, k // 2 + 1):
+        targets = nodes[j:] + nodes[0:j]  # first j nodes are now last in list
+        G.add_edges_from(zip(nodes, targets))
+    # rewire edges from each node
+    # loop over all nodes in order (label) and neighbors in order (distance)
+    # no self loops or multiple edges allowed
+    for j in range(1, k // 2 + 1):  # outer loop is neighbors
+        targets = nodes[j:] + nodes[0:j]  # first j nodes are now last in list
+        # inner loop in node order
+        for u, v in zip(nodes, targets):
+            if seed.random() < p:
+                w = seed.choice(nodes)
+                # Enforce no self-loops or multiple edges
+                while w == u or G.has_edge(u, w):
+                    w = seed.choice(nodes)
+                    if G.degree(u) >= n - 1:
+                        break  # skip this rewiring
+                else:
+                    G.remove_edge(u, v)
+                    G.add_edge(u, w)
+    return G
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def connected_watts_strogatz_graph(n, k, p, tries=100, seed=None, *, create_using=None):
+    """Returns a connected Watts–Strogatz small-world graph.
+
+    Attempts to generate a connected graph by repeated generation of
+    Watts–Strogatz small-world graphs.  An exception is raised if the maximum
+    number of tries is exceeded.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    k : int
+        Each node is joined with its `k` nearest neighbors in a ring
+        topology.
+    p : float
+        The probability of rewiring each edge
+    tries : int
+        Number of attempts to generate a connected graph.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    First create a ring over $n$ nodes [1]_.  Then each node in the ring is joined
+    to its $k$ nearest neighbors (or $k - 1$ neighbors if $k$ is odd).
+    Then shortcuts are created by replacing some edges as follows: for each
+    edge $(u, v)$ in the underlying "$n$-ring with $k$ nearest neighbors"
+    with probability $p$ replace it with a new edge $(u, w)$ with uniformly
+    random choice of existing node $w$.
+    The entire process is repeated until a connected graph results.
+
+    See Also
+    --------
+    newman_watts_strogatz_graph
+    watts_strogatz_graph
+
+    References
+    ----------
+    .. [1] Duncan J. Watts and Steven H. Strogatz,
+       Collective dynamics of small-world networks,
+       Nature, 393, pp. 440--442, 1998.
+    """
+    for i in range(tries):
+        # seed is an RNG so should change sequence each call
+        G = watts_strogatz_graph(n, k, p, seed, create_using=create_using)
+        if nx.is_connected(G):
+            return G
+    raise nx.NetworkXError("Maximum number of tries exceeded")
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_regular_graph(d, n, seed=None, *, create_using=None):
+    r"""Returns a random $d$-regular graph on $n$ nodes.
+
+    A regular graph is a graph where each node has the same number of neighbors.
+
+    The resulting graph has no self-loops or parallel edges.
+
+    Parameters
+    ----------
+    d : int
+      The degree of each node.
+    n : integer
+      The number of nodes. The value of $n \times d$ must be even.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    The nodes are numbered from $0$ to $n - 1$.
+
+    Kim and Vu's paper [2]_ shows that this algorithm samples in an
+    asymptotically uniform way from the space of random graphs when
+    $d = O(n^{1 / 3 - \epsilon})$.
+
+    Raises
+    ------
+
+    NetworkXError
+        If $n \times d$ is odd or $d$ is greater than or equal to $n$.
+
+    References
+    ----------
+    .. [1] A. Steger and N. Wormald,
+       Generating random regular graphs quickly,
+       Probability and Computing 8 (1999), 377-396, 1999.
+       https://doi.org/10.1017/S0963548399003867
+
+    .. [2] Jeong Han Kim and Van H. Vu,
+       Generating random regular graphs,
+       Proceedings of the thirty-fifth ACM symposium on Theory of computing,
+       San Diego, CA, USA, pp 213--222, 2003.
+       http://portal.acm.org/citation.cfm?id=780542.780576
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if (n * d) % 2 != 0:
+        raise nx.NetworkXError("n * d must be even")
+
+    if not 0 <= d < n:
+        raise nx.NetworkXError("the 0 <= d < n inequality must be satisfied")
+
+    G = nx.empty_graph(n, create_using=create_using)
+
+    if d == 0:
+        return G
+
+    def _suitable(edges, potential_edges):
+        # Helper subroutine to check if there are suitable edges remaining
+        # If False, the generation of the graph has failed
+        if not potential_edges:
+            return True
+        for s1 in potential_edges:
+            for s2 in potential_edges:
+                # Two iterators on the same dictionary are guaranteed
+                # to visit it in the same order if there are no
+                # intervening modifications.
+                if s1 == s2:
+                    # Only need to consider s1-s2 pair one time
+                    break
+                if s1 > s2:
+                    s1, s2 = s2, s1
+                if (s1, s2) not in edges:
+                    return True
+        return False
+
+    def _try_creation():
+        # Attempt to create an edge set
+
+        edges = set()
+        stubs = list(range(n)) * d
+
+        while stubs:
+            potential_edges = defaultdict(lambda: 0)
+            seed.shuffle(stubs)
+            stubiter = iter(stubs)
+            for s1, s2 in zip(stubiter, stubiter):
+                if s1 > s2:
+                    s1, s2 = s2, s1
+                if s1 != s2 and ((s1, s2) not in edges):
+                    edges.add((s1, s2))
+                else:
+                    potential_edges[s1] += 1
+                    potential_edges[s2] += 1
+
+            if not _suitable(edges, potential_edges):
+                return None  # failed to find suitable edge set
+
+            stubs = [
+                node
+                for node, potential in potential_edges.items()
+                for _ in range(potential)
+            ]
+        return edges
+
+    # Even though a suitable edge set exists,
+    # the generation of such a set is not guaranteed.
+    # Try repeatedly to find one.
+    edges = _try_creation()
+    while edges is None:
+        edges = _try_creation()
+    G.add_edges_from(edges)
+
+    return G
+
+
+def _random_subset(seq, m, rng):
+    """Return m unique elements from seq.
+
+    This differs from random.sample which can return repeated
+    elements if seq holds repeated elements.
+
+    Note: rng is a random.Random or numpy.random.RandomState instance.
+    """
+    targets = set()
+    while len(targets) < m:
+        x = rng.choice(seq)
+        targets.add(x)
+    return targets
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def barabasi_albert_graph(n, m, seed=None, initial_graph=None, *, create_using=None):
+    """Returns a random graph using Barabási–Albert preferential attachment
+
+    A graph of $n$ nodes is grown by attaching new nodes each with $m$
+    edges that are preferentially attached to existing nodes with high degree.
+
+    Parameters
+    ----------
+    n : int
+        Number of nodes
+    m : int
+        Number of edges to attach from a new node to existing nodes
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    initial_graph : Graph or None (default)
+        Initial network for Barabási–Albert algorithm.
+        It should be a connected graph for most use cases.
+        A copy of `initial_graph` is used.
+        If None, starts from a star graph on (m+1) nodes.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `m` does not satisfy ``1 <= m < n``, or
+        the initial graph number of nodes m0 does not satisfy ``m <= m0 <= n``.
+
+    References
+    ----------
+    .. [1] A. L. Barabási and R. Albert "Emergence of scaling in
+       random networks", Science 286, pp 509-512, 1999.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if m < 1 or m >= n:
+        raise nx.NetworkXError(
+            f"Barabási–Albert network must have m >= 1 and m < n, m = {m}, n = {n}"
+        )
+
+    if initial_graph is None:
+        # Default initial graph : star graph on (m + 1) nodes
+        G = star_graph(m, create_using)
+    else:
+        if len(initial_graph) < m or len(initial_graph) > n:
+            raise nx.NetworkXError(
+                f"Barabási–Albert initial graph needs between m={m} and n={n} nodes"
+            )
+        G = initial_graph.copy()
+
+    # List of existing nodes, with nodes repeated once for each adjacent edge
+    repeated_nodes = [n for n, d in G.degree() for _ in range(d)]
+    # Start adding the other n - m0 nodes.
+    source = len(G)
+    while source < n:
+        # Now choose m unique nodes from the existing nodes
+        # Pick uniformly from repeated_nodes (preferential attachment)
+        targets = _random_subset(repeated_nodes, m, seed)
+        # Add edges to m nodes from the source.
+        G.add_edges_from(zip([source] * m, targets))
+        # Add one node to the list for each new edge just created.
+        repeated_nodes.extend(targets)
+        # And the new node "source" has m edges to add to the list.
+        repeated_nodes.extend([source] * m)
+
+        source += 1
+    return G
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def dual_barabasi_albert_graph(
+    n, m1, m2, p, seed=None, initial_graph=None, *, create_using=None
+):
+    """Returns a random graph using dual Barabási–Albert preferential attachment
+
+    A graph of $n$ nodes is grown by attaching new nodes each with either $m_1$
+    edges (with probability $p$) or $m_2$ edges (with probability $1-p$) that
+    are preferentially attached to existing nodes with high degree.
+
+    Parameters
+    ----------
+    n : int
+        Number of nodes
+    m1 : int
+        Number of edges to link each new node to existing nodes with probability $p$
+    m2 : int
+        Number of edges to link each new node to existing nodes with probability $1-p$
+    p : float
+        The probability of attaching $m_1$ edges (as opposed to $m_2$ edges)
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    initial_graph : Graph or None (default)
+        Initial network for Barabási–Albert algorithm.
+        A copy of `initial_graph` is used.
+        It should be connected for most use cases.
+        If None, starts from an star graph on max(m1, m2) + 1 nodes.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `m1` and `m2` do not satisfy ``1 <= m1,m2 < n``, or
+        `p` does not satisfy ``0 <= p <= 1``, or
+        the initial graph number of nodes m0 does not satisfy m1, m2 <= m0 <= n.
+
+    References
+    ----------
+    .. [1] N. Moshiri "The dual-Barabasi-Albert model", arXiv:1810.10538.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if m1 < 1 or m1 >= n:
+        raise nx.NetworkXError(
+            f"Dual Barabási–Albert must have m1 >= 1 and m1 < n, m1 = {m1}, n = {n}"
+        )
+    if m2 < 1 or m2 >= n:
+        raise nx.NetworkXError(
+            f"Dual Barabási–Albert must have m2 >= 1 and m2 < n, m2 = {m2}, n = {n}"
+        )
+    if p < 0 or p > 1:
+        raise nx.NetworkXError(
+            f"Dual Barabási–Albert network must have 0 <= p <= 1, p = {p}"
+        )
+
+    # For simplicity, if p == 0 or 1, just return BA
+    if p == 1:
+        return barabasi_albert_graph(n, m1, seed, create_using=create_using)
+    elif p == 0:
+        return barabasi_albert_graph(n, m2, seed, create_using=create_using)
+
+    if initial_graph is None:
+        # Default initial graph : star graph on max(m1, m2) nodes
+        G = star_graph(max(m1, m2), create_using)
+    else:
+        if len(initial_graph) < max(m1, m2) or len(initial_graph) > n:
+            raise nx.NetworkXError(
+                f"Barabási–Albert initial graph must have between "
+                f"max(m1, m2) = {max(m1, m2)} and n = {n} nodes"
+            )
+        G = initial_graph.copy()
+
+    # Target nodes for new edges
+    targets = list(G)
+    # List of existing nodes, with nodes repeated once for each adjacent edge
+    repeated_nodes = [n for n, d in G.degree() for _ in range(d)]
+    # Start adding the remaining nodes.
+    source = len(G)
+    while source < n:
+        # Pick which m to use (m1 or m2)
+        if seed.random() < p:
+            m = m1
+        else:
+            m = m2
+        # Now choose m unique nodes from the existing nodes
+        # Pick uniformly from repeated_nodes (preferential attachment)
+        targets = _random_subset(repeated_nodes, m, seed)
+        # Add edges to m nodes from the source.
+        G.add_edges_from(zip([source] * m, targets))
+        # Add one node to the list for each new edge just created.
+        repeated_nodes.extend(targets)
+        # And the new node "source" has m edges to add to the list.
+        repeated_nodes.extend([source] * m)
+
+        source += 1
+    return G
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def extended_barabasi_albert_graph(n, m, p, q, seed=None, *, create_using=None):
+    """Returns an extended Barabási–Albert model graph.
+
+    An extended Barabási–Albert model graph is a random graph constructed
+    using preferential attachment. The extended model allows new edges,
+    rewired edges or new nodes. Based on the probabilities $p$ and $q$
+    with $p + q < 1$, the growing behavior of the graph is determined as:
+
+    1) With $p$ probability, $m$ new edges are added to the graph,
+    starting from randomly chosen existing nodes and attached preferentially at the
+    other end.
+
+    2) With $q$ probability, $m$ existing edges are rewired
+    by randomly choosing an edge and rewiring one end to a preferentially chosen node.
+
+    3) With $(1 - p - q)$ probability, $m$ new nodes are added to the graph
+    with edges attached preferentially.
+
+    When $p = q = 0$, the model behaves just like the Barabási–Alber model.
+
+    Parameters
+    ----------
+    n : int
+        Number of nodes
+    m : int
+        Number of edges with which a new node attaches to existing nodes
+    p : float
+        Probability value for adding an edge between existing nodes. p + q < 1
+    q : float
+        Probability value of rewiring of existing edges. p + q < 1
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `m` does not satisfy ``1 <= m < n`` or ``1 >= p + q``
+
+    References
+    ----------
+    .. [1] Albert, R., & Barabási, A. L. (2000)
+       Topology of evolving networks: local events and universality
+       Physical review letters, 85(24), 5234.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if m < 1 or m >= n:
+        msg = f"Extended Barabasi-Albert network needs m>=1 and m<n, m={m}, n={n}"
+        raise nx.NetworkXError(msg)
+    if p + q >= 1:
+        msg = f"Extended Barabasi-Albert network needs p + q <= 1, p={p}, q={q}"
+        raise nx.NetworkXError(msg)
+
+    # Add m initial nodes (m0 in barabasi-speak)
+    G = empty_graph(m, create_using)
+
+    # List of nodes to represent the preferential attachment random selection.
+    # At the creation of the graph, all nodes are added to the list
+    # so that even nodes that are not connected have a chance to get selected,
+    # for rewiring and adding of edges.
+    # With each new edge, nodes at the ends of the edge are added to the list.
+    attachment_preference = []
+    attachment_preference.extend(range(m))
+
+    # Start adding the other n-m nodes. The first node is m.
+    new_node = m
+    while new_node < n:
+        a_probability = seed.random()
+
+        # Total number of edges of a Clique of all the nodes
+        clique_degree = len(G) - 1
+        clique_size = (len(G) * clique_degree) / 2
+
+        # Adding m new edges, if there is room to add them
+        if a_probability < p and G.size() <= clique_size - m:
+            # Select the nodes where an edge can be added
+            eligible_nodes = [nd for nd, deg in G.degree() if deg < clique_degree]
+            for i in range(m):
+                # Choosing a random source node from eligible_nodes
+                src_node = seed.choice(eligible_nodes)
+
+                # Picking a possible node that is not 'src_node' or
+                # neighbor with 'src_node', with preferential attachment
+                prohibited_nodes = list(G[src_node])
+                prohibited_nodes.append(src_node)
+                # This will raise an exception if the sequence is empty
+                dest_node = seed.choice(
+                    [nd for nd in attachment_preference if nd not in prohibited_nodes]
+                )
+                # Adding the new edge
+                G.add_edge(src_node, dest_node)
+
+                # Appending both nodes to add to their preferential attachment
+                attachment_preference.append(src_node)
+                attachment_preference.append(dest_node)
+
+                # Adjusting the eligible nodes. Degree may be saturated.
+                if G.degree(src_node) == clique_degree:
+                    eligible_nodes.remove(src_node)
+                if G.degree(dest_node) == clique_degree and dest_node in eligible_nodes:
+                    eligible_nodes.remove(dest_node)
+
+        # Rewiring m edges, if there are enough edges
+        elif p <= a_probability < (p + q) and m <= G.size() < clique_size:
+            # Selecting nodes that have at least 1 edge but that are not
+            # fully connected to ALL other nodes (center of star).
+            # These nodes are the pivot nodes of the edges to rewire
+            eligible_nodes = [nd for nd, deg in G.degree() if 0 < deg < clique_degree]
+            for i in range(m):
+                # Choosing a random source node
+                node = seed.choice(eligible_nodes)
+
+                # The available nodes do have a neighbor at least.
+                nbr_nodes = list(G[node])
+
+                # Choosing the other end that will get detached
+                src_node = seed.choice(nbr_nodes)
+
+                # Picking a target node that is not 'node' or
+                # neighbor with 'node', with preferential attachment
+                nbr_nodes.append(node)
+                dest_node = seed.choice(
+                    [nd for nd in attachment_preference if nd not in nbr_nodes]
+                )
+                # Rewire
+                G.remove_edge(node, src_node)
+                G.add_edge(node, dest_node)
+
+                # Adjusting the preferential attachment list
+                attachment_preference.remove(src_node)
+                attachment_preference.append(dest_node)
+
+                # Adjusting the eligible nodes.
+                # nodes may be saturated or isolated.
+                if G.degree(src_node) == 0 and src_node in eligible_nodes:
+                    eligible_nodes.remove(src_node)
+                if dest_node in eligible_nodes:
+                    if G.degree(dest_node) == clique_degree:
+                        eligible_nodes.remove(dest_node)
+                else:
+                    if G.degree(dest_node) == 1:
+                        eligible_nodes.append(dest_node)
+
+        # Adding new node with m edges
+        else:
+            # Select the edges' nodes by preferential attachment
+            targets = _random_subset(attachment_preference, m, seed)
+            G.add_edges_from(zip([new_node] * m, targets))
+
+            # Add one node to the list for each new edge just created.
+            attachment_preference.extend(targets)
+            # The new node has m edges to it, plus itself: m + 1
+            attachment_preference.extend([new_node] * (m + 1))
+            new_node += 1
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def powerlaw_cluster_graph(n, m, p, seed=None, *, create_using=None):
+    """Holme and Kim algorithm for growing graphs with powerlaw
+    degree distribution and approximate average clustering.
+
+    Parameters
+    ----------
+    n : int
+        the number of nodes
+    m : int
+        the number of random edges to add for each new node
+    p : float,
+        Probability of adding a triangle after adding a random edge
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    The average clustering has a hard time getting above a certain
+    cutoff that depends on `m`.  This cutoff is often quite low.  The
+    transitivity (fraction of triangles to possible triangles) seems to
+    decrease with network size.
+
+    It is essentially the Barabási–Albert (BA) growth model with an
+    extra step that each random edge is followed by a chance of
+    making an edge to one of its neighbors too (and thus a triangle).
+
+    This algorithm improves on BA in the sense that it enables a
+    higher average clustering to be attained if desired.
+
+    It seems possible to have a disconnected graph with this algorithm
+    since the initial `m` nodes may not be all linked to a new node
+    on the first iteration like the BA model.
+
+    Raises
+    ------
+    NetworkXError
+        If `m` does not satisfy ``1 <= m <= n`` or `p` does not
+        satisfy ``0 <= p <= 1``.
+
+    References
+    ----------
+    .. [1] P. Holme and B. J. Kim,
+       "Growing scale-free networks with tunable clustering",
+       Phys. Rev. E, 65, 026107, 2002.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if m < 1 or n < m:
+        raise nx.NetworkXError(f"NetworkXError must have m>1 and m<n, m={m},n={n}")
+
+    if p > 1 or p < 0:
+        raise nx.NetworkXError(f"NetworkXError p must be in [0,1], p={p}")
+
+    G = empty_graph(m, create_using)  # add m initial nodes (m0 in barabasi-speak)
+    repeated_nodes = list(G)  # list of existing nodes to sample from
+    # with nodes repeated once for each adjacent edge
+    source = m  # next node is m
+    while source < n:  # Now add the other n-1 nodes
+        possible_targets = _random_subset(repeated_nodes, m, seed)
+        # do one preferential attachment for new node
+        target = possible_targets.pop()
+        G.add_edge(source, target)
+        repeated_nodes.append(target)  # add one node to list for each new link
+        count = 1
+        while count < m:  # add m-1 more new links
+            if seed.random() < p:  # clustering step: add triangle
+                neighborhood = [
+                    nbr
+                    for nbr in G.neighbors(target)
+                    if not G.has_edge(source, nbr) and nbr != source
+                ]
+                if neighborhood:  # if there is a neighbor without a link
+                    nbr = seed.choice(neighborhood)
+                    G.add_edge(source, nbr)  # add triangle
+                    repeated_nodes.append(nbr)
+                    count = count + 1
+                    continue  # go to top of while loop
+            # else do preferential attachment step if above fails
+            target = possible_targets.pop()
+            G.add_edge(source, target)
+            repeated_nodes.append(target)
+            count = count + 1
+
+        repeated_nodes.extend([source] * m)  # add source node to list m times
+        source += 1
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_lobster(n, p1, p2, seed=None, *, create_using=None):
+    """Returns a random lobster graph.
+
+    A lobster is a tree that reduces to a caterpillar when pruning all
+    leaf nodes. A caterpillar is a tree that reduces to a path graph
+    when pruning all leaf nodes; setting `p2` to zero produces a caterpillar.
+
+    This implementation iterates on the probabilities `p1` and `p2` to add
+    edges at levels 1 and 2, respectively. Graphs are therefore constructed
+    iteratively with uniform randomness at each level rather than being selected
+    uniformly at random from the set of all possible lobsters.
+
+    Parameters
+    ----------
+    n : int
+        The expected number of nodes in the backbone
+    p1 : float
+        Probability of adding an edge to the backbone
+    p2 : float
+        Probability of adding an edge one level beyond backbone
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Grap)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Raises
+    ------
+    NetworkXError
+        If `p1` or `p2` parameters are >= 1 because the while loops would never finish.
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    p1, p2 = abs(p1), abs(p2)
+    if any(p >= 1 for p in [p1, p2]):
+        raise nx.NetworkXError("Probability values for `p1` and `p2` must both be < 1.")
+
+    # a necessary ingredient in any self-respecting graph library
+    llen = int(2 * seed.random() * n + 0.5)
+    L = path_graph(llen, create_using)
+    # build caterpillar: add edges to path graph with probability p1
+    current_node = llen - 1
+    for n in range(llen):
+        while seed.random() < p1:  # add fuzzy caterpillar parts
+            current_node += 1
+            L.add_edge(n, current_node)
+            cat_node = current_node
+            while seed.random() < p2:  # add crunchy lobster bits
+                current_node += 1
+                L.add_edge(cat_node, current_node)
+    return L  # voila, un lobster!
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_shell_graph(constructor, seed=None, *, create_using=None):
+    """Returns a random shell graph for the constructor given.
+
+    Parameters
+    ----------
+    constructor : list of three-tuples
+        Represents the parameters for a shell, starting at the center
+        shell.  Each element of the list must be of the form `(n, m,
+        d)`, where `n` is the number of nodes in the shell, `m` is
+        the number of edges in the shell, and `d` is the ratio of
+        inter-shell (next) edges to intra-shell edges. If `d` is zero,
+        there will be no intra-shell edges, and if `d` is one there
+        will be all possible intra-shell edges.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. Graph instances are not supported.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Examples
+    --------
+    >>> constructor = [(10, 20, 0.8), (20, 40, 0.8)]
+    >>> G = nx.random_shell_graph(constructor)
+
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    G = empty_graph(0, create_using)
+
+    glist = []
+    intra_edges = []
+    nnodes = 0
+    # create gnm graphs for each shell
+    for n, m, d in constructor:
+        inter_edges = int(m * d)
+        intra_edges.append(m - inter_edges)
+        g = nx.convert_node_labels_to_integers(
+            gnm_random_graph(n, inter_edges, seed=seed, create_using=G.__class__),
+            first_label=nnodes,
+        )
+        glist.append(g)
+        nnodes += n
+        G = nx.operators.union(G, g)
+
+    # connect the shells randomly
+    for gi in range(len(glist) - 1):
+        nlist1 = list(glist[gi])
+        nlist2 = list(glist[gi + 1])
+        total_edges = intra_edges[gi]
+        edge_count = 0
+        while edge_count < total_edges:
+            u = seed.choice(nlist1)
+            v = seed.choice(nlist2)
+            if u == v or G.has_edge(u, v):
+                continue
+            else:
+                G.add_edge(u, v)
+                edge_count = edge_count + 1
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_powerlaw_tree(n, gamma=3, seed=None, tries=100, *, create_using=None):
+    """Returns a tree with a power law degree distribution.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    gamma : float
+        Exponent of the power law.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    tries : int
+        Number of attempts to adjust the sequence to make it a tree.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Raises
+    ------
+    NetworkXError
+        If no valid sequence is found within the maximum number of
+        attempts.
+
+    Notes
+    -----
+    A trial power law degree sequence is chosen and then elements are
+    swapped with new elements from a powerlaw distribution until the
+    sequence makes a tree (by checking, for example, that the number of
+    edges is one smaller than the number of nodes).
+
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    # This call may raise a NetworkXError if the number of tries is succeeded.
+    seq = random_powerlaw_tree_sequence(n, gamma=gamma, seed=seed, tries=tries)
+    G = degree_sequence_tree(seq, create_using)
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None)
+def random_powerlaw_tree_sequence(n, gamma=3, seed=None, tries=100):
+    """Returns a degree sequence for a tree with a power law distribution.
+
+    Parameters
+    ----------
+    n : int,
+        The number of nodes.
+    gamma : float
+        Exponent of the power law.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    tries : int
+        Number of attempts to adjust the sequence to make it a tree.
+
+    Raises
+    ------
+    NetworkXError
+        If no valid sequence is found within the maximum number of
+        attempts.
+
+    Notes
+    -----
+    A trial power law degree sequence is chosen and then elements are
+    swapped with new elements from a power law distribution until
+    the sequence makes a tree (by checking, for example, that the number of
+    edges is one smaller than the number of nodes).
+
+    """
+    # get trial sequence
+    z = nx.utils.powerlaw_sequence(n, exponent=gamma, seed=seed)
+    # round to integer values in the range [0,n]
+    zseq = [min(n, max(round(s), 0)) for s in z]
+
+    # another sequence to swap values from
+    z = nx.utils.powerlaw_sequence(tries, exponent=gamma, seed=seed)
+    # round to integer values in the range [0,n]
+    swap = [min(n, max(round(s), 0)) for s in z]
+
+    for deg in swap:
+        # If this degree sequence can be the degree sequence of a tree, return
+        # it. It can be a tree if the number of edges is one fewer than the
+        # number of nodes, or in other words, `n - sum(zseq) / 2 == 1`. We
+        # use an equivalent condition below that avoids floating point
+        # operations.
+        if 2 * n - sum(zseq) == 2:
+            return zseq
+        index = seed.randint(0, n - 1)
+        zseq[index] = swap.pop()
+
+    raise nx.NetworkXError(
+        f"Exceeded max ({tries}) attempts for a valid tree sequence."
+    )
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_kernel_graph(
+    n, kernel_integral, kernel_root=None, seed=None, *, create_using=None
+):
+    r"""Returns an random graph based on the specified kernel.
+
+    The algorithm chooses each of the $[n(n-1)]/2$ possible edges with
+    probability specified by a kernel $\kappa(x,y)$ [1]_.  The kernel
+    $\kappa(x,y)$ must be a symmetric (in $x,y$), non-negative,
+    bounded function.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    kernel_integral : function
+        Function that returns the definite integral of the kernel $\kappa(x,y)$,
+        $F(y,a,b) := \int_a^b \kappa(x,y)dx$
+    kernel_root: function (optional)
+        Function that returns the root $b$ of the equation $F(y,a,b) = r$.
+        If None, the root is found using :func:`scipy.optimize.brentq`
+        (this requires SciPy).
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    Notes
+    -----
+    The kernel is specified through its definite integral which must be
+    provided as one of the arguments. If the integral and root of the
+    kernel integral can be found in $O(1)$ time then this algorithm runs in
+    time $O(n+m)$ where m is the expected number of edges [2]_.
+
+    The nodes are set to integers from $0$ to $n-1$.
+
+    Examples
+    --------
+    Generate an Erdős–Rényi random graph $G(n,c/n)$, with kernel
+    $\kappa(x,y)=c$ where $c$ is the mean expected degree.
+
+    >>> def integral(u, w, z):
+    ...     return c * (z - w)
+    >>> def root(u, w, r):
+    ...     return r / c + w
+    >>> c = 1
+    >>> graph = nx.random_kernel_graph(1000, integral, root)
+
+    See Also
+    --------
+    gnp_random_graph
+    expected_degree_graph
+
+    References
+    ----------
+    .. [1] Bollobás, Béla,  Janson, S. and Riordan, O.
+       "The phase transition in inhomogeneous random graphs",
+       *Random Structures Algorithms*, 31, 3--122, 2007.
+
+    .. [2] Hagberg A, Lemons N (2015),
+       "Fast Generation of Sparse Random Kernel Graphs".
+       PLoS ONE 10(9): e0135177, 2015. doi:10.1371/journal.pone.0135177
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    if kernel_root is None:
+        import scipy as sp
+
+        def kernel_root(y, a, r):
+            def my_function(b):
+                return kernel_integral(y, a, b) - r
+
+            return sp.optimize.brentq(my_function, a, 1)
+
+    graph = nx.empty_graph(create_using=create_using)
+    graph.add_nodes_from(range(n))
+    (i, j) = (1, 1)
+    while i < n:
+        r = -math.log(1 - seed.random())  # (1-seed.random()) in (0, 1]
+        if kernel_integral(i / n, j / n, 1) <= r:
+            i, j = i + 1, i + 1
+        else:
+            j = math.ceil(n * kernel_root(i / n, j / n, r))
+            graph.add_edge(i - 1, j - 1)
+    return graph
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/small.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/small.py
new file mode 100644
index 0000000000000000000000000000000000000000..719cf49cf9e3d5be715313bb3e531f235b8ce292
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/small.py
@@ -0,0 +1,996 @@
+"""
+Various small and named graphs, together with some compact generators.
+
+"""
+
+__all__ = [
+    "LCF_graph",
+    "bull_graph",
+    "chvatal_graph",
+    "cubical_graph",
+    "desargues_graph",
+    "diamond_graph",
+    "dodecahedral_graph",
+    "frucht_graph",
+    "heawood_graph",
+    "hoffman_singleton_graph",
+    "house_graph",
+    "house_x_graph",
+    "icosahedral_graph",
+    "krackhardt_kite_graph",
+    "moebius_kantor_graph",
+    "octahedral_graph",
+    "pappus_graph",
+    "petersen_graph",
+    "sedgewick_maze_graph",
+    "tetrahedral_graph",
+    "truncated_cube_graph",
+    "truncated_tetrahedron_graph",
+    "tutte_graph",
+]
+
+from functools import wraps
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.generators.classic import (
+    complete_graph,
+    cycle_graph,
+    empty_graph,
+    path_graph,
+)
+
+
+def _raise_on_directed(func):
+    """
+    A decorator which inspects the `create_using` argument and raises a
+    NetworkX exception when `create_using` is a DiGraph (class or instance) for
+    graph generators that do not support directed outputs.
+
+    `create_using` may be a keyword argument or the first positional argument.
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        create_using = args[0] if args else kwargs.get("create_using")
+        if create_using is not None:
+            G = nx.empty_graph(create_using=create_using)
+            if G.is_directed():
+                raise NetworkXError("Directed Graph not supported")
+        return func(*args, **kwargs)
+
+    return wrapper
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def LCF_graph(n, shift_list, repeats, create_using=None):
+    """
+    Return the cubic graph specified in LCF notation.
+
+    LCF (Lederberg-Coxeter-Fruchte) notation[1]_ is a compressed
+    notation used in the generation of various cubic Hamiltonian
+    graphs of high symmetry. See, for example, `dodecahedral_graph`,
+    `desargues_graph`, `heawood_graph` and `pappus_graph`.
+
+    Nodes are drawn from ``range(n)``. Each node ``n_i`` is connected with
+    node ``n_i + shift % n`` where ``shift`` is given by cycling through
+    the input `shift_list` `repeat` s times.
+
+    Parameters
+    ----------
+    n : int
+       The starting graph is the `n`-cycle with nodes ``0, ..., n-1``.
+       The null graph is returned if `n` < 1.
+
+    shift_list : list
+       A list of integer shifts mod `n`, ``[s1, s2, .., sk]``
+
+    repeats : int
+       Integer specifying the number of times that shifts in `shift_list`
+       are successively applied to each current node in the n-cycle
+       to generate an edge between ``n_current`` and ``n_current + shift mod n``.
+
+    Returns
+    -------
+    G : Graph
+       A graph instance created from the specified LCF notation.
+
+    Examples
+    --------
+    The utility graph $K_{3,3}$
+
+    >>> G = nx.LCF_graph(6, [3, -3], 3)
+    >>> G.edges()
+    EdgeView([(0, 1), (0, 5), (0, 3), (1, 2), (1, 4), (2, 3), (2, 5), (3, 4), (4, 5)])
+
+    The Heawood graph:
+
+    >>> G = nx.LCF_graph(14, [5, -5], 7)
+    >>> nx.is_isomorphic(G, nx.heawood_graph())
+    True
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/LCF_notation
+
+    """
+    if n <= 0:
+        return empty_graph(0, create_using)
+
+    # start with the n-cycle
+    G = cycle_graph(n, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+    G.name = "LCF_graph"
+    nodes = sorted(G)
+
+    n_extra_edges = repeats * len(shift_list)
+    # edges are added n_extra_edges times
+    # (not all of these need be new)
+    if n_extra_edges < 1:
+        return G
+
+    for i in range(n_extra_edges):
+        shift = shift_list[i % len(shift_list)]  # cycle through shift_list
+        v1 = nodes[i % n]  # cycle repeatedly through nodes
+        v2 = nodes[(i + shift) % n]
+        G.add_edge(v1, v2)
+    return G
+
+
+# -------------------------------------------------------------------------------
+#   Various small and named graphs
+# -------------------------------------------------------------------------------
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def bull_graph(create_using=None):
+    """
+    Returns the Bull Graph
+
+    The Bull Graph has 5 nodes and 5 edges. It is a planar undirected
+    graph in the form of a triangle with two disjoint pendant edges [1]_
+    The name comes from the triangle and pendant edges representing
+    respectively the body and legs of a bull.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        A bull graph with 5 nodes
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Bull_graph.
+
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2], 1: [0, 2, 3], 2: [0, 1, 4], 3: [1], 4: [2]},
+        create_using=create_using,
+    )
+    G.name = "Bull Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def chvatal_graph(create_using=None):
+    """
+    Returns the Chvátal Graph
+
+    The Chvátal Graph is an undirected graph with 12 nodes and 24 edges [1]_.
+    It has 370 distinct (directed) Hamiltonian cycles, giving a unique generalized
+    LCF notation of order 4, two of order 6 , and 43 of order 1 [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        The Chvátal graph with 12 nodes and 24 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Chv%C3%A1tal_graph
+    .. [2] https://mathworld.wolfram.com/ChvatalGraph.html
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 4, 6, 9],
+            1: [2, 5, 7],
+            2: [3, 6, 8],
+            3: [4, 7, 9],
+            4: [5, 8],
+            5: [10, 11],
+            6: [10, 11],
+            7: [8, 11],
+            8: [10],
+            9: [10, 11],
+        },
+        create_using=create_using,
+    )
+    G.name = "Chvatal Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def cubical_graph(create_using=None):
+    """
+    Returns the 3-regular Platonic Cubical Graph
+
+    The skeleton of the cube (the nodes and edges) form a graph, with 8
+    nodes, and 12 edges. It is a special case of the hypercube graph.
+    It is one of 5 Platonic graphs, each a skeleton of its
+    Platonic solid [1]_.
+    Such graphs arise in parallel processing in computers.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        A cubical graph with 8 nodes and 12 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Cube#Cubical_graph
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 3, 4],
+            1: [0, 2, 7],
+            2: [1, 3, 6],
+            3: [0, 2, 5],
+            4: [0, 5, 7],
+            5: [3, 4, 6],
+            6: [2, 5, 7],
+            7: [1, 4, 6],
+        },
+        create_using=create_using,
+    )
+    G.name = "Platonic Cubical Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def desargues_graph(create_using=None):
+    """
+    Returns the Desargues Graph
+
+    The Desargues Graph is a non-planar, distance-transitive cubic graph
+    with 20 nodes and 30 edges [1]_.
+    It is a symmetric graph. It can be represented in LCF notation
+    as [5,-5,9,-9]^5 [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Desargues Graph with 20 nodes and 30 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Desargues_graph
+    .. [2] https://mathworld.wolfram.com/DesarguesGraph.html
+    """
+    G = LCF_graph(20, [5, -5, 9, -9], 5, create_using)
+    G.name = "Desargues Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def diamond_graph(create_using=None):
+    """
+    Returns the Diamond graph
+
+    The Diamond Graph is  planar undirected graph with 4 nodes and 5 edges.
+    It is also sometimes known as the double triangle graph or kite graph [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Diamond Graph with 4 nodes and 5 edges
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/DiamondGraph.html
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2], 1: [0, 2, 3], 2: [0, 1, 3], 3: [1, 2]}, create_using=create_using
+    )
+    G.name = "Diamond Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def dodecahedral_graph(create_using=None):
+    """
+    Returns the Platonic Dodecahedral graph.
+
+    The dodecahedral graph has 20 nodes and 30 edges. The skeleton of the
+    dodecahedron forms a graph. It is one of 5 Platonic graphs [1]_.
+    It can be described in LCF notation as:
+    ``[10, 7, 4, -4, -7, 10, -4, 7, -7, 4]^2`` [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Dodecahedral Graph with 20 nodes and 30 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Regular_dodecahedron#Dodecahedral_graph
+    .. [2] https://mathworld.wolfram.com/DodecahedralGraph.html
+
+    """
+    G = LCF_graph(20, [10, 7, 4, -4, -7, 10, -4, 7, -7, 4], 2, create_using)
+    G.name = "Dodecahedral Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def frucht_graph(create_using=None):
+    """
+    Returns the Frucht Graph.
+
+    The Frucht Graph is the smallest cubical graph whose
+    automorphism group consists only of the identity element [1]_.
+    It has 12 nodes and 18 edges and no nontrivial symmetries.
+    It is planar and Hamiltonian [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Frucht Graph with 12 nodes and 18 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Frucht_graph
+    .. [2] https://mathworld.wolfram.com/FruchtGraph.html
+
+    """
+    G = cycle_graph(7, create_using)
+    G.add_edges_from(
+        [
+            [0, 7],
+            [1, 7],
+            [2, 8],
+            [3, 9],
+            [4, 9],
+            [5, 10],
+            [6, 10],
+            [7, 11],
+            [8, 11],
+            [8, 9],
+            [10, 11],
+        ]
+    )
+
+    G.name = "Frucht Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def heawood_graph(create_using=None):
+    """
+    Returns the Heawood Graph, a (3,6) cage.
+
+    The Heawood Graph is an undirected graph with 14 nodes and 21 edges,
+    named after Percy John Heawood [1]_.
+    It is cubic symmetric, nonplanar, Hamiltonian, and can be represented
+    in LCF notation as ``[5,-5]^7`` [2]_.
+    It is the unique (3,6)-cage: the regular cubic graph of girth 6 with
+    minimal number of vertices [3]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Heawood Graph with 14 nodes and 21 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Heawood_graph
+    .. [2] https://mathworld.wolfram.com/HeawoodGraph.html
+    .. [3] https://www.win.tue.nl/~aeb/graphs/Heawood.html
+
+    """
+    G = LCF_graph(14, [5, -5], 7, create_using)
+    G.name = "Heawood Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hoffman_singleton_graph():
+    """
+    Returns the Hoffman-Singleton Graph.
+
+    The Hoffman–Singleton graph is a symmetrical undirected graph
+    with 50 nodes and 175 edges.
+    All indices lie in ``Z % 5``: that is, the integers mod 5 [1]_.
+    It is the only regular graph of vertex degree 7, diameter 2, and girth 5.
+    It is the unique (7,5)-cage graph and Moore graph, and contains many
+    copies of the Petersen graph [2]_.
+
+    Returns
+    -------
+    G : networkx Graph
+        Hoffman–Singleton Graph with 50 nodes and 175 edges
+
+    Notes
+    -----
+    Constructed from pentagon and pentagram as follows: Take five pentagons $P_h$
+    and five pentagrams $Q_i$ . Join vertex $j$ of $P_h$ to vertex $h·i+j$ of $Q_i$ [3]_.
+
+    References
+    ----------
+    .. [1] https://blogs.ams.org/visualinsight/2016/02/01/hoffman-singleton-graph/
+    .. [2] https://mathworld.wolfram.com/Hoffman-SingletonGraph.html
+    .. [3] https://en.wikipedia.org/wiki/Hoffman%E2%80%93Singleton_graph
+
+    """
+    G = nx.Graph()
+    for i in range(5):
+        for j in range(5):
+            G.add_edge(("pentagon", i, j), ("pentagon", i, (j - 1) % 5))
+            G.add_edge(("pentagon", i, j), ("pentagon", i, (j + 1) % 5))
+            G.add_edge(("pentagram", i, j), ("pentagram", i, (j - 2) % 5))
+            G.add_edge(("pentagram", i, j), ("pentagram", i, (j + 2) % 5))
+            for k in range(5):
+                G.add_edge(("pentagon", i, j), ("pentagram", k, (i * k + j) % 5))
+    G = nx.convert_node_labels_to_integers(G)
+    G.name = "Hoffman-Singleton Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def house_graph(create_using=None):
+    """
+    Returns the House graph (square with triangle on top)
+
+    The house graph is a simple undirected graph with
+    5 nodes and 6 edges [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        House graph in the form of a square with a triangle on top
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/HouseGraph.html
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2], 1: [0, 3], 2: [0, 3, 4], 3: [1, 2, 4], 4: [2, 3]},
+        create_using=create_using,
+    )
+    G.name = "House Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def house_x_graph(create_using=None):
+    """
+    Returns the House graph with a cross inside the house square.
+
+    The House X-graph is the House graph plus the two edges connecting diagonally
+    opposite vertices of the square base. It is also one of the two graphs
+    obtained by removing two edges from the pentatope graph [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        House graph with diagonal vertices connected
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/HouseGraph.html
+    """
+    G = house_graph(create_using)
+    G.add_edges_from([(0, 3), (1, 2)])
+    G.name = "House-with-X-inside Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def icosahedral_graph(create_using=None):
+    """
+    Returns the Platonic Icosahedral graph.
+
+    The icosahedral graph has 12 nodes and 30 edges. It is a Platonic graph
+    whose nodes have the connectivity of the icosahedron. It is undirected,
+    regular and Hamiltonian [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Icosahedral graph with 12 nodes and 30 edges.
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/IcosahedralGraph.html
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 5, 7, 8, 11],
+            1: [2, 5, 6, 8],
+            2: [3, 6, 8, 9],
+            3: [4, 6, 9, 10],
+            4: [5, 6, 10, 11],
+            5: [6, 11],
+            7: [8, 9, 10, 11],
+            8: [9],
+            9: [10],
+            10: [11],
+        },
+        create_using=create_using,
+    )
+    G.name = "Platonic Icosahedral Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def krackhardt_kite_graph(create_using=None):
+    """
+    Returns the Krackhardt Kite Social Network.
+
+    A 10 actor social network introduced by David Krackhardt
+    to illustrate different centrality measures [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Krackhardt Kite graph with 10 nodes and 18 edges
+
+    Notes
+    -----
+    The traditional labeling is:
+    Andre=1, Beverley=2, Carol=3, Diane=4,
+    Ed=5, Fernando=6, Garth=7, Heather=8, Ike=9, Jane=10.
+
+    References
+    ----------
+    .. [1] Krackhardt, David. "Assessing the Political Landscape: Structure,
+       Cognition, and Power in Organizations". Administrative Science Quarterly.
+       35 (2): 342–369. doi:10.2307/2393394. JSTOR 2393394. June 1990.
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 2, 3, 5],
+            1: [0, 3, 4, 6],
+            2: [0, 3, 5],
+            3: [0, 1, 2, 4, 5, 6],
+            4: [1, 3, 6],
+            5: [0, 2, 3, 6, 7],
+            6: [1, 3, 4, 5, 7],
+            7: [5, 6, 8],
+            8: [7, 9],
+            9: [8],
+        },
+        create_using=create_using,
+    )
+    G.name = "Krackhardt Kite Social Network"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def moebius_kantor_graph(create_using=None):
+    """
+    Returns the Moebius-Kantor graph.
+
+    The Möbius-Kantor graph is the cubic symmetric graph on 16 nodes.
+    Its LCF notation is [5,-5]^8, and it is isomorphic to the generalized
+    Petersen graph [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Moebius-Kantor graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/M%C3%B6bius%E2%80%93Kantor_graph
+
+    """
+    G = LCF_graph(16, [5, -5], 8, create_using)
+    G.name = "Moebius-Kantor Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def octahedral_graph(create_using=None):
+    """
+    Returns the Platonic Octahedral graph.
+
+    The octahedral graph is the 6-node 12-edge Platonic graph having the
+    connectivity of the octahedron [1]_. If 6 couples go to a party,
+    and each person shakes hands with every person except his or her partner,
+    then this graph describes the set of handshakes that take place;
+    for this reason it is also called the cocktail party graph [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Octahedral graph
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/OctahedralGraph.html
+    .. [2] https://en.wikipedia.org/wiki/Tur%C3%A1n_graph#Special_cases
+
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2, 3, 4], 1: [2, 3, 5], 2: [4, 5], 3: [4, 5], 4: [5]},
+        create_using=create_using,
+    )
+    G.name = "Platonic Octahedral Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def pappus_graph():
+    """
+    Returns the Pappus graph.
+
+    The Pappus graph is a cubic symmetric distance-regular graph with 18 nodes
+    and 27 edges. It is Hamiltonian and can be represented in LCF notation as
+    [5,7,-7,7,-7,-5]^3 [1]_.
+
+    Returns
+    -------
+    G : networkx Graph
+        Pappus graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Pappus_graph
+    """
+    G = LCF_graph(18, [5, 7, -7, 7, -7, -5], 3)
+    G.name = "Pappus Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def petersen_graph(create_using=None):
+    """
+    Returns the Petersen graph.
+
+    The Peterson graph is a cubic, undirected graph with 10 nodes and 15 edges [1]_.
+    Julius Petersen constructed the graph as the smallest counterexample
+    against the claim that a connected bridgeless cubic graph
+    has an edge colouring with three colours [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Petersen graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Petersen_graph
+    .. [2] https://www.win.tue.nl/~aeb/drg/graphs/Petersen.html
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 4, 5],
+            1: [0, 2, 6],
+            2: [1, 3, 7],
+            3: [2, 4, 8],
+            4: [3, 0, 9],
+            5: [0, 7, 8],
+            6: [1, 8, 9],
+            7: [2, 5, 9],
+            8: [3, 5, 6],
+            9: [4, 6, 7],
+        },
+        create_using=create_using,
+    )
+    G.name = "Petersen Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def sedgewick_maze_graph(create_using=None):
+    """
+    Return a small maze with a cycle.
+
+    This is the maze used in Sedgewick, 3rd Edition, Part 5, Graph
+    Algorithms, Chapter 18, e.g. Figure 18.2 and following [1]_.
+    Nodes are numbered 0,..,7
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Small maze with a cycle
+
+    References
+    ----------
+    .. [1] Figure 18.2, Chapter 18, Graph Algorithms (3rd Ed), Sedgewick
+    """
+    G = empty_graph(0, create_using)
+    G.add_nodes_from(range(8))
+    G.add_edges_from([[0, 2], [0, 7], [0, 5]])
+    G.add_edges_from([[1, 7], [2, 6]])
+    G.add_edges_from([[3, 4], [3, 5]])
+    G.add_edges_from([[4, 5], [4, 7], [4, 6]])
+    G.name = "Sedgewick Maze"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def tetrahedral_graph(create_using=None):
+    """
+    Returns the 3-regular Platonic Tetrahedral graph.
+
+    Tetrahedral graph has 4 nodes and 6 edges. It is a
+    special case of the complete graph, K4, and wheel graph, W4.
+    It is one of the 5 platonic graphs [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Tetrahedral Graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Tetrahedron#Tetrahedral_graph
+
+    """
+    G = complete_graph(4, create_using)
+    G.name = "Platonic Tetrahedral Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def truncated_cube_graph(create_using=None):
+    """
+    Returns the skeleton of the truncated cube.
+
+    The truncated cube is an Archimedean solid with 14 regular
+    faces (6 octagonal and 8 triangular), 36 edges and 24 nodes [1]_.
+    The truncated cube is created by truncating (cutting off) the tips
+    of the cube one third of the way into each edge [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Skeleton of the truncated cube
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Truncated_cube
+    .. [2] https://www.coolmath.com/reference/polyhedra-truncated-cube
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 2, 4],
+            1: [11, 14],
+            2: [3, 4],
+            3: [6, 8],
+            4: [5],
+            5: [16, 18],
+            6: [7, 8],
+            7: [10, 12],
+            8: [9],
+            9: [17, 20],
+            10: [11, 12],
+            11: [14],
+            12: [13],
+            13: [21, 22],
+            14: [15],
+            15: [19, 23],
+            16: [17, 18],
+            17: [20],
+            18: [19],
+            19: [23],
+            20: [21],
+            21: [22],
+            22: [23],
+        },
+        create_using=create_using,
+    )
+    G.name = "Truncated Cube Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def truncated_tetrahedron_graph(create_using=None):
+    """
+    Returns the skeleton of the truncated Platonic tetrahedron.
+
+    The truncated tetrahedron is an Archimedean solid with 4 regular hexagonal faces,
+    4 equilateral triangle faces, 12 nodes and 18 edges. It can be constructed by truncating
+    all 4 vertices of a regular tetrahedron at one third of the original edge length [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Skeleton of the truncated tetrahedron
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Truncated_tetrahedron
+
+    """
+    G = path_graph(12, create_using)
+    G.add_edges_from([(0, 2), (0, 9), (1, 6), (3, 11), (4, 11), (5, 7), (8, 10)])
+    G.name = "Truncated Tetrahedron Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def tutte_graph(create_using=None):
+    """
+    Returns the Tutte graph.
+
+    The Tutte graph is a cubic polyhedral, non-Hamiltonian graph. It has
+    46 nodes and 69 edges.
+    It is a counterexample to Tait's conjecture that every 3-regular polyhedron
+    has a Hamiltonian cycle.
+    It can be realized geometrically from a tetrahedron by multiply truncating
+    three of its vertices [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Tutte graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Tutte_graph
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 2, 3],
+            1: [4, 26],
+            2: [10, 11],
+            3: [18, 19],
+            4: [5, 33],
+            5: [6, 29],
+            6: [7, 27],
+            7: [8, 14],
+            8: [9, 38],
+            9: [10, 37],
+            10: [39],
+            11: [12, 39],
+            12: [13, 35],
+            13: [14, 15],
+            14: [34],
+            15: [16, 22],
+            16: [17, 44],
+            17: [18, 43],
+            18: [45],
+            19: [20, 45],
+            20: [21, 41],
+            21: [22, 23],
+            22: [40],
+            23: [24, 27],
+            24: [25, 32],
+            25: [26, 31],
+            26: [33],
+            27: [28],
+            28: [29, 32],
+            29: [30],
+            30: [31, 33],
+            31: [32],
+            34: [35, 38],
+            35: [36],
+            36: [37, 39],
+            37: [38],
+            40: [41, 44],
+            41: [42],
+            42: [43, 45],
+            43: [44],
+        },
+        create_using=create_using,
+    )
+    G.name = "Tutte's Graph"
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/social.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/social.py
new file mode 100644
index 0000000000000000000000000000000000000000..f41b2d88b4bc09f4e45967731f1dc8acfb48420b
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/social.py
@@ -0,0 +1,554 @@
+"""
+Famous social networks.
+"""
+
+import networkx as nx
+
+__all__ = [
+    "karate_club_graph",
+    "davis_southern_women_graph",
+    "florentine_families_graph",
+    "les_miserables_graph",
+]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def karate_club_graph():
+    """Returns Zachary's Karate Club graph.
+
+    Each node in the returned graph has a node attribute 'club' that
+    indicates the name of the club to which the member represented by that node
+    belongs, either 'Mr. Hi' or 'Officer'. Each edge has a weight based on the
+    number of contexts in which that edge's incident node members interacted.
+
+    The dataset is derived from the 'Club After Split From Data' column of Table 3 in [1]_.
+    This was in turn derived from the 'Club After Fission' column of Table 1 in the
+    same paper. Note that the nodes are 0-indexed in NetworkX, but 1-indexed in the
+    paper (the 'Individual Number in Matrix C' column of Table 3 starts at 1). This
+    means, for example, that ``G.nodes[9]["club"]`` returns 'Officer', which
+    corresponds to row 10 of Table 3 in the paper.
+
+    Examples
+    --------
+    To get the name of the club to which a node belongs::
+
+        >>> G = nx.karate_club_graph()
+        >>> G.nodes[5]["club"]
+        'Mr. Hi'
+        >>> G.nodes[9]["club"]
+        'Officer'
+
+    References
+    ----------
+    .. [1] Zachary, Wayne W.
+       "An Information Flow Model for Conflict and Fission in Small Groups."
+       *Journal of Anthropological Research*, 33, 452--473, (1977).
+    """
+    # Create the set of all members, and the members of each club.
+    all_members = set(range(34))
+    club1 = {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 16, 17, 19, 21}
+    # club2 = all_members - club1
+
+    G = nx.Graph()
+    G.add_nodes_from(all_members)
+    G.name = "Zachary's Karate Club"
+
+    zacharydat = """\
+0 4 5 3 3 3 3 2 2 0 2 3 2 3 0 0 0 2 0 2 0 2 0 0 0 0 0 0 0 0 0 2 0 0
+4 0 6 3 0 0 0 4 0 0 0 0 0 5 0 0 0 1 0 2 0 2 0 0 0 0 0 0 0 0 2 0 0 0
+5 6 0 3 0 0 0 4 5 1 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 3 0
+3 3 3 0 0 0 0 3 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 0 0 0 0 0 2 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 0 0 0 0 0 5 0 0 0 3 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 0 0 0 2 5 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+2 4 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+2 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 4 3
+0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
+2 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+1 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 5 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 4
+0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2
+2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1
+2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 4 0 2 0 0 5 4
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 3 0 0 0 2 0 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 2 0 0 0 0 0 0 7 0 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 2
+0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 0 0 0 0 4
+0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 4 0 0 0 0 0 3 2
+0 2 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3
+2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 7 0 0 2 0 0 0 4 4
+0 0 2 0 0 0 0 0 3 0 0 0 0 0 3 3 0 0 1 0 3 0 2 5 0 0 0 0 0 4 3 4 0 5
+0 0 0 0 0 0 0 0 4 2 0 0 0 3 2 4 0 0 2 1 1 0 3 4 0 0 2 4 2 2 3 4 5 0"""
+
+    for row, line in enumerate(zacharydat.split("\n")):
+        thisrow = [int(b) for b in line.split()]
+        for col, entry in enumerate(thisrow):
+            if entry >= 1:
+                G.add_edge(row, col, weight=entry)
+
+    # Add the name of each member's club as a node attribute.
+    for v in G:
+        G.nodes[v]["club"] = "Mr. Hi" if v in club1 else "Officer"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def davis_southern_women_graph():
+    """Returns Davis Southern women social network.
+
+    This is a bipartite graph.
+
+    References
+    ----------
+    .. [1] A. Davis, Gardner, B. B., Gardner, M. R., 1941. Deep South.
+        University of Chicago Press, Chicago, IL.
+    """
+    G = nx.Graph()
+    # Top nodes
+    women = [
+        "Evelyn Jefferson",
+        "Laura Mandeville",
+        "Theresa Anderson",
+        "Brenda Rogers",
+        "Charlotte McDowd",
+        "Frances Anderson",
+        "Eleanor Nye",
+        "Pearl Oglethorpe",
+        "Ruth DeSand",
+        "Verne Sanderson",
+        "Myra Liddel",
+        "Katherina Rogers",
+        "Sylvia Avondale",
+        "Nora Fayette",
+        "Helen Lloyd",
+        "Dorothy Murchison",
+        "Olivia Carleton",
+        "Flora Price",
+    ]
+    G.add_nodes_from(women, bipartite=0)
+    # Bottom nodes
+    events = [
+        "E1",
+        "E2",
+        "E3",
+        "E4",
+        "E5",
+        "E6",
+        "E7",
+        "E8",
+        "E9",
+        "E10",
+        "E11",
+        "E12",
+        "E13",
+        "E14",
+    ]
+    G.add_nodes_from(events, bipartite=1)
+
+    G.add_edges_from(
+        [
+            ("Evelyn Jefferson", "E1"),
+            ("Evelyn Jefferson", "E2"),
+            ("Evelyn Jefferson", "E3"),
+            ("Evelyn Jefferson", "E4"),
+            ("Evelyn Jefferson", "E5"),
+            ("Evelyn Jefferson", "E6"),
+            ("Evelyn Jefferson", "E8"),
+            ("Evelyn Jefferson", "E9"),
+            ("Laura Mandeville", "E1"),
+            ("Laura Mandeville", "E2"),
+            ("Laura Mandeville", "E3"),
+            ("Laura Mandeville", "E5"),
+            ("Laura Mandeville", "E6"),
+            ("Laura Mandeville", "E7"),
+            ("Laura Mandeville", "E8"),
+            ("Theresa Anderson", "E2"),
+            ("Theresa Anderson", "E3"),
+            ("Theresa Anderson", "E4"),
+            ("Theresa Anderson", "E5"),
+            ("Theresa Anderson", "E6"),
+            ("Theresa Anderson", "E7"),
+            ("Theresa Anderson", "E8"),
+            ("Theresa Anderson", "E9"),
+            ("Brenda Rogers", "E1"),
+            ("Brenda Rogers", "E3"),
+            ("Brenda Rogers", "E4"),
+            ("Brenda Rogers", "E5"),
+            ("Brenda Rogers", "E6"),
+            ("Brenda Rogers", "E7"),
+            ("Brenda Rogers", "E8"),
+            ("Charlotte McDowd", "E3"),
+            ("Charlotte McDowd", "E4"),
+            ("Charlotte McDowd", "E5"),
+            ("Charlotte McDowd", "E7"),
+            ("Frances Anderson", "E3"),
+            ("Frances Anderson", "E5"),
+            ("Frances Anderson", "E6"),
+            ("Frances Anderson", "E8"),
+            ("Eleanor Nye", "E5"),
+            ("Eleanor Nye", "E6"),
+            ("Eleanor Nye", "E7"),
+            ("Eleanor Nye", "E8"),
+            ("Pearl Oglethorpe", "E6"),
+            ("Pearl Oglethorpe", "E8"),
+            ("Pearl Oglethorpe", "E9"),
+            ("Ruth DeSand", "E5"),
+            ("Ruth DeSand", "E7"),
+            ("Ruth DeSand", "E8"),
+            ("Ruth DeSand", "E9"),
+            ("Verne Sanderson", "E7"),
+            ("Verne Sanderson", "E8"),
+            ("Verne Sanderson", "E9"),
+            ("Verne Sanderson", "E12"),
+            ("Myra Liddel", "E8"),
+            ("Myra Liddel", "E9"),
+            ("Myra Liddel", "E10"),
+            ("Myra Liddel", "E12"),
+            ("Katherina Rogers", "E8"),
+            ("Katherina Rogers", "E9"),
+            ("Katherina Rogers", "E10"),
+            ("Katherina Rogers", "E12"),
+            ("Katherina Rogers", "E13"),
+            ("Katherina Rogers", "E14"),
+            ("Sylvia Avondale", "E7"),
+            ("Sylvia Avondale", "E8"),
+            ("Sylvia Avondale", "E9"),
+            ("Sylvia Avondale", "E10"),
+            ("Sylvia Avondale", "E12"),
+            ("Sylvia Avondale", "E13"),
+            ("Sylvia Avondale", "E14"),
+            ("Nora Fayette", "E6"),
+            ("Nora Fayette", "E7"),
+            ("Nora Fayette", "E9"),
+            ("Nora Fayette", "E10"),
+            ("Nora Fayette", "E11"),
+            ("Nora Fayette", "E12"),
+            ("Nora Fayette", "E13"),
+            ("Nora Fayette", "E14"),
+            ("Helen Lloyd", "E7"),
+            ("Helen Lloyd", "E8"),
+            ("Helen Lloyd", "E10"),
+            ("Helen Lloyd", "E11"),
+            ("Helen Lloyd", "E12"),
+            ("Dorothy Murchison", "E8"),
+            ("Dorothy Murchison", "E9"),
+            ("Olivia Carleton", "E9"),
+            ("Olivia Carleton", "E11"),
+            ("Flora Price", "E9"),
+            ("Flora Price", "E11"),
+        ]
+    )
+    G.graph["top"] = women
+    G.graph["bottom"] = events
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def florentine_families_graph():
+    """Returns Florentine families graph.
+
+    References
+    ----------
+    .. [1] Ronald L. Breiger and Philippa E. Pattison
+       Cumulated social roles: The duality of persons and their algebras,1
+       Social Networks, Volume 8, Issue 3, September 1986, Pages 215-256
+    """
+    G = nx.Graph()
+    G.add_edge("Acciaiuoli", "Medici")
+    G.add_edge("Castellani", "Peruzzi")
+    G.add_edge("Castellani", "Strozzi")
+    G.add_edge("Castellani", "Barbadori")
+    G.add_edge("Medici", "Barbadori")
+    G.add_edge("Medici", "Ridolfi")
+    G.add_edge("Medici", "Tornabuoni")
+    G.add_edge("Medici", "Albizzi")
+    G.add_edge("Medici", "Salviati")
+    G.add_edge("Salviati", "Pazzi")
+    G.add_edge("Peruzzi", "Strozzi")
+    G.add_edge("Peruzzi", "Bischeri")
+    G.add_edge("Strozzi", "Ridolfi")
+    G.add_edge("Strozzi", "Bischeri")
+    G.add_edge("Ridolfi", "Tornabuoni")
+    G.add_edge("Tornabuoni", "Guadagni")
+    G.add_edge("Albizzi", "Ginori")
+    G.add_edge("Albizzi", "Guadagni")
+    G.add_edge("Bischeri", "Guadagni")
+    G.add_edge("Guadagni", "Lamberteschi")
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def les_miserables_graph():
+    """Returns coappearance network of characters in the novel Les Miserables.
+
+    References
+    ----------
+    .. [1] D. E. Knuth, 1993.
+       The Stanford GraphBase: a platform for combinatorial computing,
+       pp. 74-87. New York: AcM Press.
+    """
+    G = nx.Graph()
+    G.add_edge("Napoleon", "Myriel", weight=1)
+    G.add_edge("MlleBaptistine", "Myriel", weight=8)
+    G.add_edge("MmeMagloire", "Myriel", weight=10)
+    G.add_edge("MmeMagloire", "MlleBaptistine", weight=6)
+    G.add_edge("CountessDeLo", "Myriel", weight=1)
+    G.add_edge("Geborand", "Myriel", weight=1)
+    G.add_edge("Champtercier", "Myriel", weight=1)
+    G.add_edge("Cravatte", "Myriel", weight=1)
+    G.add_edge("Count", "Myriel", weight=2)
+    G.add_edge("OldMan", "Myriel", weight=1)
+    G.add_edge("Valjean", "Labarre", weight=1)
+    G.add_edge("Valjean", "MmeMagloire", weight=3)
+    G.add_edge("Valjean", "MlleBaptistine", weight=3)
+    G.add_edge("Valjean", "Myriel", weight=5)
+    G.add_edge("Marguerite", "Valjean", weight=1)
+    G.add_edge("MmeDeR", "Valjean", weight=1)
+    G.add_edge("Isabeau", "Valjean", weight=1)
+    G.add_edge("Gervais", "Valjean", weight=1)
+    G.add_edge("Listolier", "Tholomyes", weight=4)
+    G.add_edge("Fameuil", "Tholomyes", weight=4)
+    G.add_edge("Fameuil", "Listolier", weight=4)
+    G.add_edge("Blacheville", "Tholomyes", weight=4)
+    G.add_edge("Blacheville", "Listolier", weight=4)
+    G.add_edge("Blacheville", "Fameuil", weight=4)
+    G.add_edge("Favourite", "Tholomyes", weight=3)
+    G.add_edge("Favourite", "Listolier", weight=3)
+    G.add_edge("Favourite", "Fameuil", weight=3)
+    G.add_edge("Favourite", "Blacheville", weight=4)
+    G.add_edge("Dahlia", "Tholomyes", weight=3)
+    G.add_edge("Dahlia", "Listolier", weight=3)
+    G.add_edge("Dahlia", "Fameuil", weight=3)
+    G.add_edge("Dahlia", "Blacheville", weight=3)
+    G.add_edge("Dahlia", "Favourite", weight=5)
+    G.add_edge("Zephine", "Tholomyes", weight=3)
+    G.add_edge("Zephine", "Listolier", weight=3)
+    G.add_edge("Zephine", "Fameuil", weight=3)
+    G.add_edge("Zephine", "Blacheville", weight=3)
+    G.add_edge("Zephine", "Favourite", weight=4)
+    G.add_edge("Zephine", "Dahlia", weight=4)
+    G.add_edge("Fantine", "Tholomyes", weight=3)
+    G.add_edge("Fantine", "Listolier", weight=3)
+    G.add_edge("Fantine", "Fameuil", weight=3)
+    G.add_edge("Fantine", "Blacheville", weight=3)
+    G.add_edge("Fantine", "Favourite", weight=4)
+    G.add_edge("Fantine", "Dahlia", weight=4)
+    G.add_edge("Fantine", "Zephine", weight=4)
+    G.add_edge("Fantine", "Marguerite", weight=2)
+    G.add_edge("Fantine", "Valjean", weight=9)
+    G.add_edge("MmeThenardier", "Fantine", weight=2)
+    G.add_edge("MmeThenardier", "Valjean", weight=7)
+    G.add_edge("Thenardier", "MmeThenardier", weight=13)
+    G.add_edge("Thenardier", "Fantine", weight=1)
+    G.add_edge("Thenardier", "Valjean", weight=12)
+    G.add_edge("Cosette", "MmeThenardier", weight=4)
+    G.add_edge("Cosette", "Valjean", weight=31)
+    G.add_edge("Cosette", "Tholomyes", weight=1)
+    G.add_edge("Cosette", "Thenardier", weight=1)
+    G.add_edge("Javert", "Valjean", weight=17)
+    G.add_edge("Javert", "Fantine", weight=5)
+    G.add_edge("Javert", "Thenardier", weight=5)
+    G.add_edge("Javert", "MmeThenardier", weight=1)
+    G.add_edge("Javert", "Cosette", weight=1)
+    G.add_edge("Fauchelevent", "Valjean", weight=8)
+    G.add_edge("Fauchelevent", "Javert", weight=1)
+    G.add_edge("Bamatabois", "Fantine", weight=1)
+    G.add_edge("Bamatabois", "Javert", weight=1)
+    G.add_edge("Bamatabois", "Valjean", weight=2)
+    G.add_edge("Perpetue", "Fantine", weight=1)
+    G.add_edge("Simplice", "Perpetue", weight=2)
+    G.add_edge("Simplice", "Valjean", weight=3)
+    G.add_edge("Simplice", "Fantine", weight=2)
+    G.add_edge("Simplice", "Javert", weight=1)
+    G.add_edge("Scaufflaire", "Valjean", weight=1)
+    G.add_edge("Woman1", "Valjean", weight=2)
+    G.add_edge("Woman1", "Javert", weight=1)
+    G.add_edge("Judge", "Valjean", weight=3)
+    G.add_edge("Judge", "Bamatabois", weight=2)
+    G.add_edge("Champmathieu", "Valjean", weight=3)
+    G.add_edge("Champmathieu", "Judge", weight=3)
+    G.add_edge("Champmathieu", "Bamatabois", weight=2)
+    G.add_edge("Brevet", "Judge", weight=2)
+    G.add_edge("Brevet", "Champmathieu", weight=2)
+    G.add_edge("Brevet", "Valjean", weight=2)
+    G.add_edge("Brevet", "Bamatabois", weight=1)
+    G.add_edge("Chenildieu", "Judge", weight=2)
+    G.add_edge("Chenildieu", "Champmathieu", weight=2)
+    G.add_edge("Chenildieu", "Brevet", weight=2)
+    G.add_edge("Chenildieu", "Valjean", weight=2)
+    G.add_edge("Chenildieu", "Bamatabois", weight=1)
+    G.add_edge("Cochepaille", "Judge", weight=2)
+    G.add_edge("Cochepaille", "Champmathieu", weight=2)
+    G.add_edge("Cochepaille", "Brevet", weight=2)
+    G.add_edge("Cochepaille", "Chenildieu", weight=2)
+    G.add_edge("Cochepaille", "Valjean", weight=2)
+    G.add_edge("Cochepaille", "Bamatabois", weight=1)
+    G.add_edge("Pontmercy", "Thenardier", weight=1)
+    G.add_edge("Boulatruelle", "Thenardier", weight=1)
+    G.add_edge("Eponine", "MmeThenardier", weight=2)
+    G.add_edge("Eponine", "Thenardier", weight=3)
+    G.add_edge("Anzelma", "Eponine", weight=2)
+    G.add_edge("Anzelma", "Thenardier", weight=2)
+    G.add_edge("Anzelma", "MmeThenardier", weight=1)
+    G.add_edge("Woman2", "Valjean", weight=3)
+    G.add_edge("Woman2", "Cosette", weight=1)
+    G.add_edge("Woman2", "Javert", weight=1)
+    G.add_edge("MotherInnocent", "Fauchelevent", weight=3)
+    G.add_edge("MotherInnocent", "Valjean", weight=1)
+    G.add_edge("Gribier", "Fauchelevent", weight=2)
+    G.add_edge("MmeBurgon", "Jondrette", weight=1)
+    G.add_edge("Gavroche", "MmeBurgon", weight=2)
+    G.add_edge("Gavroche", "Thenardier", weight=1)
+    G.add_edge("Gavroche", "Javert", weight=1)
+    G.add_edge("Gavroche", "Valjean", weight=1)
+    G.add_edge("Gillenormand", "Cosette", weight=3)
+    G.add_edge("Gillenormand", "Valjean", weight=2)
+    G.add_edge("Magnon", "Gillenormand", weight=1)
+    G.add_edge("Magnon", "MmeThenardier", weight=1)
+    G.add_edge("MlleGillenormand", "Gillenormand", weight=9)
+    G.add_edge("MlleGillenormand", "Cosette", weight=2)
+    G.add_edge("MlleGillenormand", "Valjean", weight=2)
+    G.add_edge("MmePontmercy", "MlleGillenormand", weight=1)
+    G.add_edge("MmePontmercy", "Pontmercy", weight=1)
+    G.add_edge("MlleVaubois", "MlleGillenormand", weight=1)
+    G.add_edge("LtGillenormand", "MlleGillenormand", weight=2)
+    G.add_edge("LtGillenormand", "Gillenormand", weight=1)
+    G.add_edge("LtGillenormand", "Cosette", weight=1)
+    G.add_edge("Marius", "MlleGillenormand", weight=6)
+    G.add_edge("Marius", "Gillenormand", weight=12)
+    G.add_edge("Marius", "Pontmercy", weight=1)
+    G.add_edge("Marius", "LtGillenormand", weight=1)
+    G.add_edge("Marius", "Cosette", weight=21)
+    G.add_edge("Marius", "Valjean", weight=19)
+    G.add_edge("Marius", "Tholomyes", weight=1)
+    G.add_edge("Marius", "Thenardier", weight=2)
+    G.add_edge("Marius", "Eponine", weight=5)
+    G.add_edge("Marius", "Gavroche", weight=4)
+    G.add_edge("BaronessT", "Gillenormand", weight=1)
+    G.add_edge("BaronessT", "Marius", weight=1)
+    G.add_edge("Mabeuf", "Marius", weight=1)
+    G.add_edge("Mabeuf", "Eponine", weight=1)
+    G.add_edge("Mabeuf", "Gavroche", weight=1)
+    G.add_edge("Enjolras", "Marius", weight=7)
+    G.add_edge("Enjolras", "Gavroche", weight=7)
+    G.add_edge("Enjolras", "Javert", weight=6)
+    G.add_edge("Enjolras", "Mabeuf", weight=1)
+    G.add_edge("Enjolras", "Valjean", weight=4)
+    G.add_edge("Combeferre", "Enjolras", weight=15)
+    G.add_edge("Combeferre", "Marius", weight=5)
+    G.add_edge("Combeferre", "Gavroche", weight=6)
+    G.add_edge("Combeferre", "Mabeuf", weight=2)
+    G.add_edge("Prouvaire", "Gavroche", weight=1)
+    G.add_edge("Prouvaire", "Enjolras", weight=4)
+    G.add_edge("Prouvaire", "Combeferre", weight=2)
+    G.add_edge("Feuilly", "Gavroche", weight=2)
+    G.add_edge("Feuilly", "Enjolras", weight=6)
+    G.add_edge("Feuilly", "Prouvaire", weight=2)
+    G.add_edge("Feuilly", "Combeferre", weight=5)
+    G.add_edge("Feuilly", "Mabeuf", weight=1)
+    G.add_edge("Feuilly", "Marius", weight=1)
+    G.add_edge("Courfeyrac", "Marius", weight=9)
+    G.add_edge("Courfeyrac", "Enjolras", weight=17)
+    G.add_edge("Courfeyrac", "Combeferre", weight=13)
+    G.add_edge("Courfeyrac", "Gavroche", weight=7)
+    G.add_edge("Courfeyrac", "Mabeuf", weight=2)
+    G.add_edge("Courfeyrac", "Eponine", weight=1)
+    G.add_edge("Courfeyrac", "Feuilly", weight=6)
+    G.add_edge("Courfeyrac", "Prouvaire", weight=3)
+    G.add_edge("Bahorel", "Combeferre", weight=5)
+    G.add_edge("Bahorel", "Gavroche", weight=5)
+    G.add_edge("Bahorel", "Courfeyrac", weight=6)
+    G.add_edge("Bahorel", "Mabeuf", weight=2)
+    G.add_edge("Bahorel", "Enjolras", weight=4)
+    G.add_edge("Bahorel", "Feuilly", weight=3)
+    G.add_edge("Bahorel", "Prouvaire", weight=2)
+    G.add_edge("Bahorel", "Marius", weight=1)
+    G.add_edge("Bossuet", "Marius", weight=5)
+    G.add_edge("Bossuet", "Courfeyrac", weight=12)
+    G.add_edge("Bossuet", "Gavroche", weight=5)
+    G.add_edge("Bossuet", "Bahorel", weight=4)
+    G.add_edge("Bossuet", "Enjolras", weight=10)
+    G.add_edge("Bossuet", "Feuilly", weight=6)
+    G.add_edge("Bossuet", "Prouvaire", weight=2)
+    G.add_edge("Bossuet", "Combeferre", weight=9)
+    G.add_edge("Bossuet", "Mabeuf", weight=1)
+    G.add_edge("Bossuet", "Valjean", weight=1)
+    G.add_edge("Joly", "Bahorel", weight=5)
+    G.add_edge("Joly", "Bossuet", weight=7)
+    G.add_edge("Joly", "Gavroche", weight=3)
+    G.add_edge("Joly", "Courfeyrac", weight=5)
+    G.add_edge("Joly", "Enjolras", weight=5)
+    G.add_edge("Joly", "Feuilly", weight=5)
+    G.add_edge("Joly", "Prouvaire", weight=2)
+    G.add_edge("Joly", "Combeferre", weight=5)
+    G.add_edge("Joly", "Mabeuf", weight=1)
+    G.add_edge("Joly", "Marius", weight=2)
+    G.add_edge("Grantaire", "Bossuet", weight=3)
+    G.add_edge("Grantaire", "Enjolras", weight=3)
+    G.add_edge("Grantaire", "Combeferre", weight=1)
+    G.add_edge("Grantaire", "Courfeyrac", weight=2)
+    G.add_edge("Grantaire", "Joly", weight=2)
+    G.add_edge("Grantaire", "Gavroche", weight=1)
+    G.add_edge("Grantaire", "Bahorel", weight=1)
+    G.add_edge("Grantaire", "Feuilly", weight=1)
+    G.add_edge("Grantaire", "Prouvaire", weight=1)
+    G.add_edge("MotherPlutarch", "Mabeuf", weight=3)
+    G.add_edge("Gueulemer", "Thenardier", weight=5)
+    G.add_edge("Gueulemer", "Valjean", weight=1)
+    G.add_edge("Gueulemer", "MmeThenardier", weight=1)
+    G.add_edge("Gueulemer", "Javert", weight=1)
+    G.add_edge("Gueulemer", "Gavroche", weight=1)
+    G.add_edge("Gueulemer", "Eponine", weight=1)
+    G.add_edge("Babet", "Thenardier", weight=6)
+    G.add_edge("Babet", "Gueulemer", weight=6)
+    G.add_edge("Babet", "Valjean", weight=1)
+    G.add_edge("Babet", "MmeThenardier", weight=1)
+    G.add_edge("Babet", "Javert", weight=2)
+    G.add_edge("Babet", "Gavroche", weight=1)
+    G.add_edge("Babet", "Eponine", weight=1)
+    G.add_edge("Claquesous", "Thenardier", weight=4)
+    G.add_edge("Claquesous", "Babet", weight=4)
+    G.add_edge("Claquesous", "Gueulemer", weight=4)
+    G.add_edge("Claquesous", "Valjean", weight=1)
+    G.add_edge("Claquesous", "MmeThenardier", weight=1)
+    G.add_edge("Claquesous", "Javert", weight=1)
+    G.add_edge("Claquesous", "Eponine", weight=1)
+    G.add_edge("Claquesous", "Enjolras", weight=1)
+    G.add_edge("Montparnasse", "Javert", weight=1)
+    G.add_edge("Montparnasse", "Babet", weight=2)
+    G.add_edge("Montparnasse", "Gueulemer", weight=2)
+    G.add_edge("Montparnasse", "Claquesous", weight=2)
+    G.add_edge("Montparnasse", "Valjean", weight=1)
+    G.add_edge("Montparnasse", "Gavroche", weight=1)
+    G.add_edge("Montparnasse", "Eponine", weight=1)
+    G.add_edge("Montparnasse", "Thenardier", weight=1)
+    G.add_edge("Toussaint", "Cosette", weight=2)
+    G.add_edge("Toussaint", "Javert", weight=1)
+    G.add_edge("Toussaint", "Valjean", weight=1)
+    G.add_edge("Child1", "Gavroche", weight=2)
+    G.add_edge("Child2", "Gavroche", weight=2)
+    G.add_edge("Child2", "Child1", weight=3)
+    G.add_edge("Brujon", "Babet", weight=3)
+    G.add_edge("Brujon", "Gueulemer", weight=3)
+    G.add_edge("Brujon", "Thenardier", weight=3)
+    G.add_edge("Brujon", "Gavroche", weight=1)
+    G.add_edge("Brujon", "Eponine", weight=1)
+    G.add_edge("Brujon", "Claquesous", weight=1)
+    G.add_edge("Brujon", "Montparnasse", weight=1)
+    G.add_edge("MmeHucheloup", "Bossuet", weight=1)
+    G.add_edge("MmeHucheloup", "Joly", weight=1)
+    G.add_edge("MmeHucheloup", "Grantaire", weight=1)
+    G.add_edge("MmeHucheloup", "Bahorel", weight=1)
+    G.add_edge("MmeHucheloup", "Courfeyrac", weight=1)
+    G.add_edge("MmeHucheloup", "Gavroche", weight=1)
+    G.add_edge("MmeHucheloup", "Enjolras", weight=1)
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/spectral_graph_forge.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/spectral_graph_forge.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa8c9194bb31ce15434c728c607bfe0172402406
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/spectral_graph_forge.py
@@ -0,0 +1,120 @@
+"""Generates graphs with a given eigenvector structure"""
+
+import networkx as nx
+from networkx.utils import np_random_state
+
+__all__ = ["spectral_graph_forge"]
+
+
+@np_random_state(3)
+@nx._dispatchable(preserve_edge_attrs={"G": {"weight": 1}}, returns_graph=True)
+def spectral_graph_forge(G, alpha, transformation="identity", seed=None):
+    """Returns a random simple graph with spectrum resembling that of `G`
+
+    This algorithm, called Spectral Graph Forge (SGF), computes the
+    eigenvectors of a given graph adjacency matrix, filters them and
+    builds a random graph with a similar eigenstructure.
+    SGF has been proved to be particularly useful for synthesizing
+    realistic social networks and it can also be used to anonymize
+    graph sensitive data.
+
+    Parameters
+    ----------
+    G : Graph
+    alpha :  float
+        Ratio representing the percentage of eigenvectors of G to consider,
+        values in [0,1].
+    transformation : string, optional
+        Represents the intended matrix linear transformation, possible values
+        are 'identity' and 'modularity'
+    seed : integer, random_state, or None (default)
+        Indicator of numpy random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    H : Graph
+        A graph with a similar eigenvector structure of the input one.
+
+    Raises
+    ------
+    NetworkXError
+        If transformation has a value different from 'identity' or 'modularity'
+
+    Notes
+    -----
+    Spectral Graph Forge (SGF) generates a random simple graph resembling the
+    global properties of the given one.
+    It leverages the low-rank approximation of the associated adjacency matrix
+    driven by the *alpha* precision parameter.
+    SGF preserves the number of nodes of the input graph and their ordering.
+    This way, nodes of output graphs resemble the properties of the input one
+    and attributes can be directly mapped.
+
+    It considers the graph adjacency matrices which can optionally be
+    transformed to other symmetric real matrices (currently transformation
+    options include *identity* and *modularity*).
+    The *modularity* transformation, in the sense of Newman's modularity matrix
+    allows the focusing on community structure related properties of the graph.
+
+    SGF applies a low-rank approximation whose fixed rank is computed from the
+    ratio *alpha* of the input graph adjacency matrix dimension.
+    This step performs a filtering on the input eigenvectors similar to the low
+    pass filtering common in telecommunications.
+
+    The filtered values (after truncation) are used as input to a Bernoulli
+    sampling for constructing a random adjacency matrix.
+
+    References
+    ----------
+    ..  [1] L. Baldesi, C. T. Butts, A. Markopoulou, "Spectral Graph Forge:
+        Graph Generation Targeting Modularity", IEEE Infocom, '18.
+        https://arxiv.org/abs/1801.01715
+    ..  [2] M. Newman, "Networks: an introduction", Oxford university press,
+        2010
+
+    Examples
+    --------
+    >>> G = nx.karate_club_graph()
+    >>> H = nx.spectral_graph_forge(G, 0.3)
+    >>>
+    """
+    import numpy as np
+    import scipy as sp
+
+    available_transformations = ["identity", "modularity"]
+    alpha = np.clip(alpha, 0, 1)
+    A = nx.to_numpy_array(G)
+    n = A.shape[1]
+    level = round(n * alpha)
+
+    if transformation not in available_transformations:
+        msg = f"{transformation!r} is not a valid transformation. "
+        msg += f"Transformations: {available_transformations}"
+        raise nx.NetworkXError(msg)
+
+    K = np.ones((1, n)) @ A
+
+    B = A
+    if transformation == "modularity":
+        B -= K.T @ K / K.sum()
+
+    # Compute low-rank approximation of B
+    evals, evecs = np.linalg.eigh(B)
+    k = np.argsort(np.abs(evals))[::-1]  # indices of evals in descending order
+    evecs[:, k[np.arange(level, n)]] = 0  # set smallest eigenvectors to 0
+    B = evecs @ np.diag(evals) @ evecs.T
+
+    if transformation == "modularity":
+        B += K.T @ K / K.sum()
+
+    B = np.clip(B, 0, 1)
+    np.fill_diagonal(B, 0)
+
+    for i in range(n - 1):
+        B[i, i + 1 :] = sp.stats.bernoulli.rvs(B[i, i + 1 :], random_state=seed)
+        B[i + 1 :, i] = np.transpose(B[i, i + 1 :])
+
+    H = nx.from_numpy_array(B)
+
+    return H
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/stochastic.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/stochastic.py
new file mode 100644
index 0000000000000000000000000000000000000000..f53e2315470f8ffcdea0380026a933e06ddf6ea7
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/stochastic.py
@@ -0,0 +1,54 @@
+"""Functions for generating stochastic graphs from a given weighted directed
+graph.
+
+"""
+
+import networkx as nx
+from networkx.classes import DiGraph, MultiDiGraph
+from networkx.utils import not_implemented_for
+
+__all__ = ["stochastic_graph"]
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable(
+    edge_attrs="weight", mutates_input={"not copy": 1}, returns_graph=True
+)
+def stochastic_graph(G, copy=True, weight="weight"):
+    """Returns a right-stochastic representation of directed graph `G`.
+
+    A right-stochastic graph is a weighted digraph in which for each
+    node, the sum of the weights of all the out-edges of that node is
+    1. If the graph is already weighted (for example, via a 'weight'
+    edge attribute), the reweighting takes that into account.
+
+    Parameters
+    ----------
+    G : directed graph
+        A :class:`~networkx.DiGraph` or :class:`~networkx.MultiDiGraph`.
+
+    copy : boolean, optional
+        If this is True, then this function returns a new graph with
+        the stochastic reweighting. Otherwise, the original graph is
+        modified in-place (and also returned, for convenience).
+
+    weight : edge attribute key (optional, default='weight')
+        Edge attribute key used for reading the existing weight and
+        setting the new weight.  If no attribute with this key is found
+        for an edge, then the edge weight is assumed to be 1. If an edge
+        has a weight, it must be a positive number.
+
+    """
+    if copy:
+        G = MultiDiGraph(G) if G.is_multigraph() else DiGraph(G)
+    # There is a tradeoff here: the dictionary of node degrees may
+    # require a lot of memory, whereas making a call to `G.out_degree`
+    # inside the loop may be costly in computation time.
+    degree = dict(G.out_degree(weight=weight))
+    for u, v, d in G.edges(data=True):
+        if degree[u] == 0:
+            d[weight] = 0
+        else:
+            d[weight] = d.get(weight, 1) / degree[u]
+    nx._clear_cache(G)
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/sudoku.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/sudoku.py
new file mode 100644
index 0000000000000000000000000000000000000000..f288ed24d1f189588de7e1e0bba61f50bbad0003
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/sudoku.py
@@ -0,0 +1,131 @@
+"""Generator for Sudoku graphs
+
+This module gives a generator for n-Sudoku graphs. It can be used to develop
+algorithms for solving or generating Sudoku puzzles.
+
+A completed Sudoku grid is a 9x9 array of integers between 1 and 9, with no
+number appearing twice in the same row, column, or 3x3 box.
+
++---------+---------+---------+
+| | 8 6 4 | | 3 7 1 | | 2 5 9 |
+| | 3 2 5 | | 8 4 9 | | 7 6 1 |
+| | 9 7 1 | | 2 6 5 | | 8 4 3 |
++---------+---------+---------+
+| | 4 3 6 | | 1 9 2 | | 5 8 7 |
+| | 1 9 8 | | 6 5 7 | | 4 3 2 |
+| | 2 5 7 | | 4 8 3 | | 9 1 6 |
++---------+---------+---------+
+| | 6 8 9 | | 7 3 4 | | 1 2 5 |
+| | 7 1 3 | | 5 2 8 | | 6 9 4 |
+| | 5 4 2 | | 9 1 6 | | 3 7 8 |
++---------+---------+---------+
+
+
+The Sudoku graph is an undirected graph with 81 vertices, corresponding to
+the cells of a Sudoku grid. It is a regular graph of degree 20. Two distinct
+vertices are adjacent if and only if the corresponding cells belong to the
+same row, column, or box. A completed Sudoku grid corresponds to a vertex
+coloring of the Sudoku graph with nine colors.
+
+More generally, the n-Sudoku graph is a graph with n^4 vertices, corresponding
+to the cells of an n^2 by n^2 grid. Two distinct vertices are adjacent if and
+only if they belong to the same row, column, or n by n box.
+
+References
+----------
+.. [1] Herzberg, A. M., & Murty, M. R. (2007). Sudoku squares and chromatic
+    polynomials. Notices of the AMS, 54(6), 708-717.
+.. [2] Sander, Torsten (2009), "Sudoku graphs are integral",
+    Electronic Journal of Combinatorics, 16 (1): Note 25, 7pp, MR 2529816
+.. [3] Wikipedia contributors. "Glossary of Sudoku." Wikipedia, The Free
+    Encyclopedia, 3 Dec. 2019. Web. 22 Dec. 2019.
+"""
+
+import networkx as nx
+from networkx.exception import NetworkXError
+
+__all__ = ["sudoku_graph"]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def sudoku_graph(n=3):
+    """Returns the n-Sudoku graph. The default value of n is 3.
+
+    The n-Sudoku graph is a graph with n^4 vertices, corresponding to the
+    cells of an n^2 by n^2 grid. Two distinct vertices are adjacent if and
+    only if they belong to the same row, column, or n-by-n box.
+
+    Parameters
+    ----------
+    n: integer
+       The order of the Sudoku graph, equal to the square root of the
+       number of rows. The default is 3.
+
+    Returns
+    -------
+    NetworkX graph
+        The n-Sudoku graph Sud(n).
+
+    Examples
+    --------
+    >>> G = nx.sudoku_graph()
+    >>> G.number_of_nodes()
+    81
+    >>> G.number_of_edges()
+    810
+    >>> sorted(G.neighbors(42))
+    [6, 15, 24, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 51, 52, 53, 60, 69, 78]
+    >>> G = nx.sudoku_graph(2)
+    >>> G.number_of_nodes()
+    16
+    >>> G.number_of_edges()
+    56
+
+    References
+    ----------
+    .. [1] Herzberg, A. M., & Murty, M. R. (2007). Sudoku squares and chromatic
+       polynomials. Notices of the AMS, 54(6), 708-717.
+    .. [2] Sander, Torsten (2009), "Sudoku graphs are integral",
+       Electronic Journal of Combinatorics, 16 (1): Note 25, 7pp, MR 2529816
+    .. [3] Wikipedia contributors. "Glossary of Sudoku." Wikipedia, The Free
+       Encyclopedia, 3 Dec. 2019. Web. 22 Dec. 2019.
+    """
+
+    if n < 0:
+        raise NetworkXError("The order must be greater than or equal to zero.")
+
+    n2 = n * n
+    n3 = n2 * n
+    n4 = n3 * n
+
+    # Construct an empty graph with n^4 nodes
+    G = nx.empty_graph(n4)
+
+    # A Sudoku graph of order 0 or 1 has no edges
+    if n < 2:
+        return G
+
+    # Add edges for cells in the same row
+    for row_no in range(n2):
+        row_start = row_no * n2
+        for j in range(1, n2):
+            for i in range(j):
+                G.add_edge(row_start + i, row_start + j)
+
+    # Add edges for cells in the same column
+    for col_no in range(n2):
+        for j in range(col_no, n4, n2):
+            for i in range(col_no, j, n2):
+                G.add_edge(i, j)
+
+    # Add edges for cells in the same box
+    for band_no in range(n):
+        for stack_no in range(n):
+            box_start = n3 * band_no + n * stack_no
+            for j in range(1, n2):
+                for i in range(j):
+                    u = box_start + (i % n) + n2 * (i // n)
+                    v = box_start + (j % n) + n2 * (j // n)
+                    G.add_edge(u, v)
+
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/time_series.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/time_series.py
new file mode 100644
index 0000000000000000000000000000000000000000..592d7734a408bf33e58aad20cb117be674558ad2
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/time_series.py
@@ -0,0 +1,74 @@
+"""
+Time Series Graphs
+"""
+
+import itertools
+
+import networkx as nx
+
+__all__ = ["visibility_graph"]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def visibility_graph(series):
+    """
+    Return a Visibility Graph of an input Time Series.
+
+    A visibility graph converts a time series into a graph. The constructed graph
+    uses integer nodes to indicate which event in the series the node represents.
+    Edges are formed as follows: consider a bar plot of the series and view that
+    as a side view of a landscape with a node at the top of each bar. An edge
+    means that the nodes can be connected by a straight "line-of-sight" without
+    being obscured by any bars between the nodes.
+
+    The resulting graph inherits several properties of the series in its structure.
+    Thereby, periodic series convert into regular graphs, random series convert
+    into random graphs, and fractal series convert into scale-free networks [1]_.
+
+    Parameters
+    ----------
+    series : Sequence[Number]
+       A Time Series sequence (iterable and sliceable) of numeric values
+       representing times.
+
+    Returns
+    -------
+    NetworkX Graph
+        The Visibility Graph of the input series
+
+    Examples
+    --------
+    >>> series_list = [range(10), [2, 1, 3, 2, 1, 3, 2, 1, 3, 2, 1, 3]]
+    >>> for s in series_list:
+    ...     g = nx.visibility_graph(s)
+    ...     print(g)
+    Graph with 10 nodes and 9 edges
+    Graph with 12 nodes and 18 edges
+
+    References
+    ----------
+    .. [1] Lacasa, Lucas, Bartolo Luque, Fernando Ballesteros, Jordi Luque, and Juan Carlos Nuno.
+           "From time series to complex networks: The visibility graph." Proceedings of the
+           National Academy of Sciences 105, no. 13 (2008): 4972-4975.
+           https://www.pnas.org/doi/10.1073/pnas.0709247105
+    """
+
+    # Sequential values are always connected
+    G = nx.path_graph(len(series))
+    nx.set_node_attributes(G, dict(enumerate(series)), "value")
+
+    # Check all combinations of nodes n series
+    for (n1, t1), (n2, t2) in itertools.combinations(enumerate(series), 2):
+        # check if any value between obstructs line of sight
+        slope = (t2 - t1) / (n2 - n1)
+        offset = t2 - slope * n2
+
+        obstructed = any(
+            t >= slope * n + offset
+            for n, t in enumerate(series[n1 + 1 : n2], start=n1 + 1)
+        )
+
+        if not obstructed:
+            G.add_edge(n1, n2)
+
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/trees.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/trees.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8b24fad27c59a85daa3b31ecb2e3ddd6fe7875f
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/trees.py
@@ -0,0 +1,1070 @@
+"""Functions for generating trees.
+
+The functions sampling trees at random in this module come
+in two variants: labeled and unlabeled. The labeled variants
+sample from every possible tree with the given number of nodes
+uniformly at random. The unlabeled variants sample from every
+possible *isomorphism class* of trees with the given number
+of nodes uniformly at random.
+
+To understand the difference, consider the following example.
+There are two isomorphism classes of trees with four nodes.
+One is that of the path graph, the other is that of the
+star graph. The unlabeled variant will return a line graph or
+a star graph with probability 1/2.
+
+The labeled variant will return the line graph
+with probability 3/4 and the star graph with probability 1/4,
+because there are more labeled variants of the line graph
+than of the star graph. More precisely, the line graph has
+an automorphism group of order 2, whereas the star graph has
+an automorphism group of order 6, so the line graph has three
+times as many labeled variants as the star graph, and thus
+three more chances to be drawn.
+
+Additionally, some functions in this module can sample rooted
+trees and forests uniformly at random. A rooted tree is a tree
+with a designated root node. A rooted forest is a disjoint union
+of rooted trees.
+"""
+
+from collections import Counter, defaultdict
+from math import comb, factorial
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "prefix_tree",
+    "prefix_tree_recursive",
+    "random_labeled_tree",
+    "random_labeled_rooted_tree",
+    "random_labeled_rooted_forest",
+    "random_unlabeled_tree",
+    "random_unlabeled_rooted_tree",
+    "random_unlabeled_rooted_forest",
+]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def prefix_tree(paths):
+    """Creates a directed prefix tree from a list of paths.
+
+    Usually the paths are described as strings or lists of integers.
+
+    A "prefix tree" represents the prefix structure of the strings.
+    Each node represents a prefix of some string. The root represents
+    the empty prefix with children for the single letter prefixes which
+    in turn have children for each double letter prefix starting with
+    the single letter corresponding to the parent node, and so on.
+
+    More generally the prefixes do not need to be strings. A prefix refers
+    to the start of a sequence. The root has children for each one element
+    prefix and they have children for each two element prefix that starts
+    with the one element sequence of the parent, and so on.
+
+    Note that this implementation uses integer nodes with an attribute.
+    Each node has an attribute "source" whose value is the original element
+    of the path to which this node corresponds. For example, suppose `paths`
+    consists of one path: "can". Then the nodes `[1, 2, 3]` which represent
+    this path have "source" values "c", "a" and "n".
+
+    All the descendants of a node have a common prefix in the sequence/path
+    associated with that node. From the returned tree, the prefix for each
+    node can be constructed by traversing the tree up to the root and
+    accumulating the "source" values along the way.
+
+    The root node is always `0` and has "source" attribute `None`.
+    The root is the only node with in-degree zero.
+    The nil node is always `-1` and has "source" attribute `"NIL"`.
+    The nil node is the only node with out-degree zero.
+
+
+    Parameters
+    ----------
+    paths: iterable of paths
+        An iterable of paths which are themselves sequences.
+        Matching prefixes among these sequences are identified with
+        nodes of the prefix tree. One leaf of the tree is associated
+        with each path. (Identical paths are associated with the same
+        leaf of the tree.)
+
+
+    Returns
+    -------
+    tree: DiGraph
+        A directed graph representing an arborescence consisting of the
+        prefix tree generated by `paths`. Nodes are directed "downward",
+        from parent to child. A special "synthetic" root node is added
+        to be the parent of the first node in each path. A special
+        "synthetic" leaf node, the "nil" node `-1`, is added to be the child
+        of all nodes representing the last element in a path. (The
+        addition of this nil node technically makes this not an
+        arborescence but a directed acyclic graph; removing the nil node
+        makes it an arborescence.)
+
+
+    Notes
+    -----
+    The prefix tree is also known as a *trie*.
+
+
+    Examples
+    --------
+    Create a prefix tree from a list of strings with common prefixes::
+
+        >>> paths = ["ab", "abs", "ad"]
+        >>> T = nx.prefix_tree(paths)
+        >>> list(T.edges)
+        [(0, 1), (1, 2), (1, 4), (2, -1), (2, 3), (3, -1), (4, -1)]
+
+    The leaf nodes can be obtained as predecessors of the nil node::
+
+        >>> root, NIL = 0, -1
+        >>> list(T.predecessors(NIL))
+        [2, 3, 4]
+
+    To recover the original paths that generated the prefix tree,
+    traverse up the tree from the node `-1` to the node `0`::
+
+        >>> recovered = []
+        >>> for v in T.predecessors(NIL):
+        ...     prefix = ""
+        ...     while v != root:
+        ...         prefix = str(T.nodes[v]["source"]) + prefix
+        ...         v = next(T.predecessors(v))  # only one predecessor
+        ...     recovered.append(prefix)
+        >>> sorted(recovered)
+        ['ab', 'abs', 'ad']
+    """
+
+    def get_children(parent, paths):
+        children = defaultdict(list)
+        # Populate dictionary with key(s) as the child/children of the root and
+        # value(s) as the remaining paths of the corresponding child/children
+        for path in paths:
+            # If path is empty, we add an edge to the NIL node.
+            if not path:
+                tree.add_edge(parent, NIL)
+                continue
+            child, *rest = path
+            # `child` may exist as the head of more than one path in `paths`.
+            children[child].append(rest)
+        return children
+
+    # Initialize the prefix tree with a root node and a nil node.
+    tree = nx.DiGraph()
+    root = 0
+    tree.add_node(root, source=None)
+    NIL = -1
+    tree.add_node(NIL, source="NIL")
+    children = get_children(root, paths)
+    stack = [(root, iter(children.items()))]
+    while stack:
+        parent, remaining_children = stack[-1]
+        try:
+            child, remaining_paths = next(remaining_children)
+        # Pop item off stack if there are no remaining children
+        except StopIteration:
+            stack.pop()
+            continue
+        # We relabel each child with an unused name.
+        new_name = len(tree) - 1
+        # The "source" node attribute stores the original node name.
+        tree.add_node(new_name, source=child)
+        tree.add_edge(parent, new_name)
+        children = get_children(new_name, remaining_paths)
+        stack.append((new_name, iter(children.items())))
+
+    return tree
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def prefix_tree_recursive(paths):
+    """Recursively creates a directed prefix tree from a list of paths.
+
+    The original recursive version of prefix_tree for comparison. It is
+    the same algorithm but the recursion is unrolled onto a stack.
+
+    Usually the paths are described as strings or lists of integers.
+
+    A "prefix tree" represents the prefix structure of the strings.
+    Each node represents a prefix of some string. The root represents
+    the empty prefix with children for the single letter prefixes which
+    in turn have children for each double letter prefix starting with
+    the single letter corresponding to the parent node, and so on.
+
+    More generally the prefixes do not need to be strings. A prefix refers
+    to the start of a sequence. The root has children for each one element
+    prefix and they have children for each two element prefix that starts
+    with the one element sequence of the parent, and so on.
+
+    Note that this implementation uses integer nodes with an attribute.
+    Each node has an attribute "source" whose value is the original element
+    of the path to which this node corresponds. For example, suppose `paths`
+    consists of one path: "can". Then the nodes `[1, 2, 3]` which represent
+    this path have "source" values "c", "a" and "n".
+
+    All the descendants of a node have a common prefix in the sequence/path
+    associated with that node. From the returned tree, ehe prefix for each
+    node can be constructed by traversing the tree up to the root and
+    accumulating the "source" values along the way.
+
+    The root node is always `0` and has "source" attribute `None`.
+    The root is the only node with in-degree zero.
+    The nil node is always `-1` and has "source" attribute `"NIL"`.
+    The nil node is the only node with out-degree zero.
+
+
+    Parameters
+    ----------
+    paths: iterable of paths
+        An iterable of paths which are themselves sequences.
+        Matching prefixes among these sequences are identified with
+        nodes of the prefix tree. One leaf of the tree is associated
+        with each path. (Identical paths are associated with the same
+        leaf of the tree.)
+
+
+    Returns
+    -------
+    tree: DiGraph
+        A directed graph representing an arborescence consisting of the
+        prefix tree generated by `paths`. Nodes are directed "downward",
+        from parent to child. A special "synthetic" root node is added
+        to be the parent of the first node in each path. A special
+        "synthetic" leaf node, the "nil" node `-1`, is added to be the child
+        of all nodes representing the last element in a path. (The
+        addition of this nil node technically makes this not an
+        arborescence but a directed acyclic graph; removing the nil node
+        makes it an arborescence.)
+
+
+    Notes
+    -----
+    The prefix tree is also known as a *trie*.
+
+
+    Examples
+    --------
+    Create a prefix tree from a list of strings with common prefixes::
+
+        >>> paths = ["ab", "abs", "ad"]
+        >>> T = nx.prefix_tree(paths)
+        >>> list(T.edges)
+        [(0, 1), (1, 2), (1, 4), (2, -1), (2, 3), (3, -1), (4, -1)]
+
+    The leaf nodes can be obtained as predecessors of the nil node.
+
+        >>> root, NIL = 0, -1
+        >>> list(T.predecessors(NIL))
+        [2, 3, 4]
+
+    To recover the original paths that generated the prefix tree,
+    traverse up the tree from the node `-1` to the node `0`::
+
+        >>> recovered = []
+        >>> for v in T.predecessors(NIL):
+        ...     prefix = ""
+        ...     while v != root:
+        ...         prefix = str(T.nodes[v]["source"]) + prefix
+        ...         v = next(T.predecessors(v))  # only one predecessor
+        ...     recovered.append(prefix)
+        >>> sorted(recovered)
+        ['ab', 'abs', 'ad']
+    """
+
+    def _helper(paths, root, tree):
+        """Recursively create a trie from the given list of paths.
+
+        `paths` is a list of paths, each of which is itself a list of
+        nodes, relative to the given `root` (but not including it). This
+        list of paths will be interpreted as a tree-like structure, in
+        which two paths that share a prefix represent two branches of
+        the tree with the same initial segment.
+
+        `root` is the parent of the node at index 0 in each path.
+
+        `tree` is the "accumulator", the :class:`networkx.DiGraph`
+        representing the branching to which the new nodes and edges will
+        be added.
+
+        """
+        # For each path, remove the first node and make it a child of root.
+        # Any remaining paths then get processed recursively.
+        children = defaultdict(list)
+        for path in paths:
+            # If path is empty, we add an edge to the NIL node.
+            if not path:
+                tree.add_edge(root, NIL)
+                continue
+            child, *rest = path
+            # `child` may exist as the head of more than one path in `paths`.
+            children[child].append(rest)
+        # Add a node for each child, connect root, recurse to remaining paths
+        for child, remaining_paths in children.items():
+            # We relabel each child with an unused name.
+            new_name = len(tree) - 1
+            # The "source" node attribute stores the original node name.
+            tree.add_node(new_name, source=child)
+            tree.add_edge(root, new_name)
+            _helper(remaining_paths, new_name, tree)
+
+    # Initialize the prefix tree with a root node and a nil node.
+    tree = nx.DiGraph()
+    root = 0
+    tree.add_node(root, source=None)
+    NIL = -1
+    tree.add_node(NIL, source="NIL")
+    # Populate the tree.
+    _helper(paths, root, tree)
+    return tree
+
+
+@py_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_labeled_tree(n, *, seed=None):
+    """Returns a labeled tree on `n` nodes chosen uniformly at random.
+
+    Generating uniformly distributed random Prüfer sequences and
+    converting them into the corresponding trees is a straightforward
+    method of generating uniformly distributed random labeled trees.
+    This function implements this method.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes, greater than zero.
+    seed : random_state
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`
+
+    Returns
+    -------
+     :class:`networkx.Graph`
+        A `networkx.Graph` with nodes in the set {0, …, *n* - 1}.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `n` is zero (because the null graph is not a tree).
+
+    Examples
+    --------
+    >>> G = nx.random_labeled_tree(5, seed=42)
+    >>> nx.is_tree(G)
+    True
+    >>> G.edges
+    EdgeView([(0, 1), (0, 3), (0, 2), (2, 4)])
+
+    A tree with *arbitrarily directed* edges can be created by assigning
+    generated edges to a ``DiGraph``:
+
+    >>> DG = nx.DiGraph()
+    >>> DG.add_edges_from(G.edges)
+    >>> nx.is_tree(DG)
+    True
+    >>> DG.edges
+    OutEdgeView([(0, 1), (0, 3), (0, 2), (2, 4)])
+    """
+    # Cannot create a Prüfer sequence unless `n` is at least two.
+    if n == 0:
+        raise nx.NetworkXPointlessConcept("the null graph is not a tree")
+    if n == 1:
+        return nx.empty_graph(1)
+    return nx.from_prufer_sequence([seed.choice(range(n)) for i in range(n - 2)])
+
+
+@py_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_labeled_rooted_tree(n, *, seed=None):
+    """Returns a labeled rooted tree with `n` nodes.
+
+    The returned tree is chosen uniformly at random from all labeled rooted trees.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    :class:`networkx.Graph`
+        A `networkx.Graph` with integer nodes 0 <= node <= `n` - 1.
+        The root of the tree is selected uniformly from the nodes.
+        The "root" graph attribute identifies the root of the tree.
+
+    Notes
+    -----
+    This function returns the result of :func:`random_labeled_tree`
+    with a randomly selected root.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `n` is zero (because the null graph is not a tree).
+    """
+    t = random_labeled_tree(n, seed=seed)
+    t.graph["root"] = seed.randint(0, n - 1)
+    return t
+
+
+@py_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_labeled_rooted_forest(n, *, seed=None):
+    """Returns a labeled rooted forest with `n` nodes.
+
+    The returned forest is chosen uniformly at random using a
+    generalization of Prüfer sequences [1]_ in the form described in [2]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    seed : random_state
+       See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    :class:`networkx.Graph`
+        A `networkx.Graph` with integer nodes 0 <= node <= `n` - 1.
+        The "roots" graph attribute is a set of integers containing the roots.
+
+    References
+    ----------
+    .. [1] Knuth, Donald E. "Another Enumeration of Trees."
+        Canadian Journal of Mathematics, 20 (1968): 1077-1086.
+        https://doi.org/10.4153/CJM-1968-104-8
+    .. [2] Rubey, Martin. "Counting Spanning Trees". Diplomarbeit
+        zur Erlangung des akademischen Grades Magister der
+        Naturwissenschaften an der Formal- und Naturwissenschaftlichen
+        Fakultät der Universität Wien. Wien, May 2000.
+    """
+
+    # Select the number of roots by iterating over the cumulative count of trees
+    # with at most k roots
+    def _select_k(n, seed):
+        r = seed.randint(0, (n + 1) ** (n - 1) - 1)
+        cum_sum = 0
+        for k in range(1, n):
+            cum_sum += (factorial(n - 1) * n ** (n - k)) // (
+                factorial(k - 1) * factorial(n - k)
+            )
+            if r < cum_sum:
+                return k
+
+        return n
+
+    F = nx.empty_graph(n)
+    if n == 0:
+        F.graph["roots"] = {}
+        return F
+    # Select the number of roots k
+    k = _select_k(n, seed)
+    if k == n:
+        F.graph["roots"] = set(range(n))
+        return F  # Nothing to do
+    # Select the roots
+    roots = seed.sample(range(n), k)
+    # Nonroots
+    p = set(range(n)).difference(roots)
+    # Coding sequence
+    N = [seed.randint(0, n - 1) for i in range(n - k - 1)]
+    # Multiset of elements in N also in p
+    degree = Counter([x for x in N if x in p])
+    # Iterator over the elements of p with degree zero
+    iterator = iter(x for x in p if degree[x] == 0)
+    u = last = next(iterator)
+    # This loop is identical to that for Prüfer sequences,
+    # except that we can draw nodes only from p
+    for v in N:
+        F.add_edge(u, v)
+        degree[v] -= 1
+        if v < last and degree[v] == 0:
+            u = v
+        else:
+            last = u = next(iterator)
+
+    F.add_edge(u, roots[0])
+    F.graph["roots"] = set(roots)
+    return F
+
+
+# The following functions support generation of unlabeled trees and forests.
+
+
+def _to_nx(edges, n_nodes, root=None, roots=None):
+    """
+    Converts the (edges, n_nodes) input to a :class:`networkx.Graph`.
+    The (edges, n_nodes) input is a list of even length, where each pair
+    of consecutive integers represents an edge, and an integer `n_nodes`.
+    Integers in the list are elements of `range(n_nodes)`.
+
+    Parameters
+    ----------
+    edges : list of ints
+        The flattened list of edges of the graph.
+    n_nodes : int
+        The number of nodes of the graph.
+    root: int (default=None)
+        If not None, the "root" attribute of the graph will be set to this value.
+    roots: collection of ints (default=None)
+        If not None, he "roots" attribute of the graph will be set to this value.
+
+    Returns
+    -------
+    :class:`networkx.Graph`
+        The graph with `n_nodes` nodes and edges given by `edges`.
+    """
+    G = nx.empty_graph(n_nodes)
+    G.add_edges_from(edges)
+    if root is not None:
+        G.graph["root"] = root
+    if roots is not None:
+        G.graph["roots"] = roots
+    return G
+
+
+def _num_rooted_trees(n, cache_trees):
+    """Returns the number of unlabeled rooted trees with `n` nodes.
+
+    See also https://oeis.org/A000081.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    cache_trees : list of ints
+        The $i$-th element is the number of unlabeled rooted trees with $i$ nodes,
+        which is used as a cache (and is extended to length $n+1$ if needed)
+
+    Returns
+    -------
+    int
+        The number of unlabeled rooted trees with `n` nodes.
+    """
+    for n_i in range(len(cache_trees), n + 1):
+        cache_trees.append(
+            sum(
+                [
+                    d * cache_trees[n_i - j * d] * cache_trees[d]
+                    for d in range(1, n_i)
+                    for j in range(1, (n_i - 1) // d + 1)
+                ]
+            )
+            // (n_i - 1)
+        )
+    return cache_trees[n]
+
+
+def _select_jd_trees(n, cache_trees, seed):
+    """Returns a pair $(j,d)$ with a specific probability
+
+    Given $n$, returns a pair of positive integers $(j,d)$ with the probability
+    specified in formula (5) of Chapter 29 of [1]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    cache_trees : list of ints
+        Cache for :func:`_num_rooted_trees`.
+    seed : random_state
+       See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    (int, int)
+        A pair of positive integers $(j,d)$ satisfying formula (5) of
+        Chapter 29 of [1]_.
+
+    References
+    ----------
+    .. [1] Nijenhuis, Albert, and Wilf, Herbert S.
+        "Combinatorial algorithms: for computers and calculators."
+        Academic Press, 1978.
+        https://doi.org/10.1016/C2013-0-11243-3
+    """
+    p = seed.randint(0, _num_rooted_trees(n, cache_trees) * (n - 1) - 1)
+    cumsum = 0
+    for d in range(n - 1, 0, -1):
+        for j in range(1, (n - 1) // d + 1):
+            cumsum += (
+                d
+                * _num_rooted_trees(n - j * d, cache_trees)
+                * _num_rooted_trees(d, cache_trees)
+            )
+            if p < cumsum:
+                return (j, d)
+
+
+def _random_unlabeled_rooted_tree(n, cache_trees, seed):
+    """Returns an unlabeled rooted tree with `n` nodes.
+
+    Returns an unlabeled rooted tree with `n` nodes chosen uniformly
+    at random using the "RANRUT" algorithm from [1]_.
+    The tree is returned in the form: (list_of_edges, number_of_nodes)
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes, greater than zero.
+    cache_trees : list ints
+        Cache for :func:`_num_rooted_trees`.
+    seed : random_state
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    (list_of_edges, number_of_nodes) : list, int
+        A random unlabeled rooted tree with `n` nodes as a 2-tuple
+        ``(list_of_edges, number_of_nodes)``.
+        The root is node 0.
+
+    References
+    ----------
+    .. [1] Nijenhuis, Albert, and Wilf, Herbert S.
+        "Combinatorial algorithms: for computers and calculators."
+        Academic Press, 1978.
+        https://doi.org/10.1016/C2013-0-11243-3
+    """
+    if n == 1:
+        edges, n_nodes = [], 1
+        return edges, n_nodes
+    if n == 2:
+        edges, n_nodes = [(0, 1)], 2
+        return edges, n_nodes
+
+    j, d = _select_jd_trees(n, cache_trees, seed)
+    t1, t1_nodes = _random_unlabeled_rooted_tree(n - j * d, cache_trees, seed)
+    t2, t2_nodes = _random_unlabeled_rooted_tree(d, cache_trees, seed)
+    t12 = [(0, t2_nodes * i + t1_nodes) for i in range(j)]
+    t1.extend(t12)
+    for _ in range(j):
+        t1.extend((n1 + t1_nodes, n2 + t1_nodes) for n1, n2 in t2)
+        t1_nodes += t2_nodes
+
+    return t1, t1_nodes
+
+
+@py_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_unlabeled_rooted_tree(n, *, number_of_trees=None, seed=None):
+    """Returns a number of unlabeled rooted trees uniformly at random
+
+    Returns one or more (depending on `number_of_trees`)
+    unlabeled rooted trees with `n` nodes drawn uniformly
+    at random.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    number_of_trees : int or None (default)
+        If not None, this number of trees is generated and returned.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    :class:`networkx.Graph` or list of :class:`networkx.Graph`
+        A single `networkx.Graph` (or a list thereof, if `number_of_trees`
+        is specified) with nodes in the set {0, …, *n* - 1}.
+        The "root" graph attribute identifies the root of the tree.
+
+    Notes
+    -----
+    The trees are generated using the "RANRUT" algorithm from [1]_.
+    The algorithm needs to compute some counting functions
+    that are relatively expensive: in case several trees are needed,
+    it is advisable to use the `number_of_trees` optional argument
+    to reuse the counting functions.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `n` is zero (because the null graph is not a tree).
+
+    References
+    ----------
+    .. [1] Nijenhuis, Albert, and Wilf, Herbert S.
+        "Combinatorial algorithms: for computers and calculators."
+        Academic Press, 1978.
+        https://doi.org/10.1016/C2013-0-11243-3
+    """
+    if n == 0:
+        raise nx.NetworkXPointlessConcept("the null graph is not a tree")
+    cache_trees = [0, 1]  # initial cache of number of rooted trees
+    if number_of_trees is None:
+        return _to_nx(*_random_unlabeled_rooted_tree(n, cache_trees, seed), root=0)
+    return [
+        _to_nx(*_random_unlabeled_rooted_tree(n, cache_trees, seed), root=0)
+        for i in range(number_of_trees)
+    ]
+
+
+def _num_rooted_forests(n, q, cache_forests):
+    """Returns the number of unlabeled rooted forests with `n` nodes, and with
+    no more than `q` nodes per tree. A recursive formula for this is (2) in
+    [1]_. This function is implemented using dynamic programming instead of
+    recursion.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    q : int
+        The maximum number of nodes for each tree of the forest.
+    cache_forests : list of ints
+        The $i$-th element is the number of unlabeled rooted forests with
+        $i$ nodes, and with no more than `q` nodes per tree; this is used
+        as a cache (and is extended to length `n` + 1 if needed).
+
+    Returns
+    -------
+    int
+        The number of unlabeled rooted forests with `n` nodes with no more than
+        `q` nodes per tree.
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    for n_i in range(len(cache_forests), n + 1):
+        q_i = min(n_i, q)
+        cache_forests.append(
+            sum(
+                [
+                    d * cache_forests[n_i - j * d] * cache_forests[d - 1]
+                    for d in range(1, q_i + 1)
+                    for j in range(1, n_i // d + 1)
+                ]
+            )
+            // n_i
+        )
+
+    return cache_forests[n]
+
+
+def _select_jd_forests(n, q, cache_forests, seed):
+    """Given `n` and `q`, returns a pair of positive integers $(j,d)$
+    such that $j\\leq d$, with probability satisfying (F1) of [1]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    q : int
+        The maximum number of nodes for each tree of the forest.
+    cache_forests : list of ints
+        Cache for :func:`_num_rooted_forests`.
+    seed : random_state
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    (int, int)
+        A pair of positive integers $(j,d)$
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    p = seed.randint(0, _num_rooted_forests(n, q, cache_forests) * n - 1)
+    cumsum = 0
+    for d in range(q, 0, -1):
+        for j in range(1, n // d + 1):
+            cumsum += (
+                d
+                * _num_rooted_forests(n - j * d, q, cache_forests)
+                * _num_rooted_forests(d - 1, q, cache_forests)
+            )
+            if p < cumsum:
+                return (j, d)
+
+
+def _random_unlabeled_rooted_forest(n, q, cache_trees, cache_forests, seed):
+    """Returns an unlabeled rooted forest with `n` nodes, and with no more
+    than `q` nodes per tree, drawn uniformly at random. It is an implementation
+    of the algorithm "Forest" of [1]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    q : int
+        The maximum number of nodes per tree.
+    cache_trees :
+        Cache for :func:`_num_rooted_trees`.
+    cache_forests :
+        Cache for :func:`_num_rooted_forests`.
+    seed : random_state
+       See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    (edges, n, r) : (list, int, list)
+        The forest (edges, n) and a list r of root nodes.
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    if n == 0:
+        return ([], 0, [])
+
+    j, d = _select_jd_forests(n, q, cache_forests, seed)
+    t1, t1_nodes, r1 = _random_unlabeled_rooted_forest(
+        n - j * d, q, cache_trees, cache_forests, seed
+    )
+    t2, t2_nodes = _random_unlabeled_rooted_tree(d, cache_trees, seed)
+    for _ in range(j):
+        r1.append(t1_nodes)
+        t1.extend((n1 + t1_nodes, n2 + t1_nodes) for n1, n2 in t2)
+        t1_nodes += t2_nodes
+    return t1, t1_nodes, r1
+
+
+@py_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_unlabeled_rooted_forest(n, *, q=None, number_of_forests=None, seed=None):
+    """Returns a forest or list of forests selected at random.
+
+    Returns one or more (depending on `number_of_forests`)
+    unlabeled rooted forests with `n` nodes, and with no more than
+    `q` nodes per tree, drawn uniformly at random.
+    The "roots" graph attribute identifies the roots of the forest.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    q : int or None (default)
+        The maximum number of nodes per tree.
+    number_of_forests : int or None (default)
+        If not None, this number of forests is generated and returned.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    :class:`networkx.Graph` or list of :class:`networkx.Graph`
+        A single `networkx.Graph` (or a list thereof, if `number_of_forests`
+        is specified) with nodes in the set {0, …, *n* - 1}.
+        The "roots" graph attribute is a set containing the roots
+        of the trees in the forest.
+
+    Notes
+    -----
+    This function implements the algorithm "Forest" of [1]_.
+    The algorithm needs to compute some counting functions
+    that are relatively expensive: in case several trees are needed,
+    it is advisable to use the `number_of_forests` optional argument
+    to reuse the counting functions.
+
+    Raises
+    ------
+    ValueError
+        If `n` is non-zero but `q` is zero.
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    if q is None:
+        q = n
+    if q == 0 and n != 0:
+        raise ValueError("q must be a positive integer if n is positive.")
+
+    cache_trees = [0, 1]  # initial cache of number of rooted trees
+    cache_forests = [1]  # initial cache of number of rooted forests
+
+    if number_of_forests is None:
+        g, nodes, rs = _random_unlabeled_rooted_forest(
+            n, q, cache_trees, cache_forests, seed
+        )
+        return _to_nx(g, nodes, roots=set(rs))
+
+    res = []
+    for i in range(number_of_forests):
+        g, nodes, rs = _random_unlabeled_rooted_forest(
+            n, q, cache_trees, cache_forests, seed
+        )
+        res.append(_to_nx(g, nodes, roots=set(rs)))
+    return res
+
+
+def _num_trees(n, cache_trees):
+    """Returns the number of unlabeled trees with `n` nodes.
+
+    See also https://oeis.org/A000055.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    cache_trees : list of ints
+        Cache for :func:`_num_rooted_trees`.
+
+    Returns
+    -------
+    int
+        The number of unlabeled trees with `n` nodes.
+    """
+    r = _num_rooted_trees(n, cache_trees) - sum(
+        [
+            _num_rooted_trees(j, cache_trees) * _num_rooted_trees(n - j, cache_trees)
+            for j in range(1, n // 2 + 1)
+        ]
+    )
+    if n % 2 == 0:
+        r += comb(_num_rooted_trees(n // 2, cache_trees) + 1, 2)
+    return r
+
+
+def _bicenter(n, cache, seed):
+    """Returns a bi-centroidal tree on `n` nodes drawn uniformly at random.
+
+    This function implements the algorithm Bicenter of [1]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes (must be even).
+    cache : list of ints.
+        Cache for :func:`_num_rooted_trees`.
+    seed : random_state
+        See :ref:`Randomness<randomness>`
+
+    Returns
+    -------
+    (edges, n)
+        The tree as a list of edges and number of nodes.
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    t, t_nodes = _random_unlabeled_rooted_tree(n // 2, cache, seed)
+    if seed.randint(0, _num_rooted_trees(n // 2, cache)) == 0:
+        t2, t2_nodes = t, t_nodes
+    else:
+        t2, t2_nodes = _random_unlabeled_rooted_tree(n // 2, cache, seed)
+    t.extend([(n1 + (n // 2), n2 + (n // 2)) for n1, n2 in t2])
+    t.append((0, n // 2))
+    return t, t_nodes + t2_nodes
+
+
+def _random_unlabeled_tree(n, cache_trees, cache_forests, seed):
+    """Returns a tree on `n` nodes drawn uniformly at random.
+    It implements the Wilf's algorithm "Free" of [1]_.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes, greater than zero.
+    cache_trees : list of ints
+        Cache for :func:`_num_rooted_trees`.
+    cache_forests : list of ints
+        Cache for :func:`_num_rooted_forests`.
+    seed : random_state
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`
+
+    Returns
+    -------
+    (edges, n)
+        The tree as a list of edges and number of nodes.
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    if n % 2 == 1:
+        p = 0
+    else:
+        p = comb(_num_rooted_trees(n // 2, cache_trees) + 1, 2)
+    if seed.randint(0, _num_trees(n, cache_trees) - 1) < p:
+        return _bicenter(n, cache_trees, seed)
+    else:
+        f, n_f, r = _random_unlabeled_rooted_forest(
+            n - 1, (n - 1) // 2, cache_trees, cache_forests, seed
+        )
+        for i in r:
+            f.append((i, n_f))
+        return f, n_f + 1
+
+
+@py_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_unlabeled_tree(n, *, number_of_trees=None, seed=None):
+    """Returns a tree or list of trees chosen randomly.
+
+    Returns one or more (depending on `number_of_trees`)
+    unlabeled trees with `n` nodes drawn uniformly at random.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes
+    number_of_trees : int or None (default)
+        If not None, this number of trees is generated and returned.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    :class:`networkx.Graph` or list of :class:`networkx.Graph`
+        A single `networkx.Graph` (or a list thereof, if
+        `number_of_trees` is specified) with nodes in the set {0, …, *n* - 1}.
+
+    Raises
+    ------
+    NetworkXPointlessConcept
+        If `n` is zero (because the null graph is not a tree).
+
+    Notes
+    -----
+    This function generates an unlabeled tree uniformly at random using
+    Wilf's algorithm "Free" of [1]_. The algorithm needs to
+    compute some counting functions that are relatively expensive:
+    in case several trees are needed, it is advisable to use the
+    `number_of_trees` optional argument to reuse the counting
+    functions.
+
+    References
+    ----------
+    .. [1] Wilf, Herbert S. "The uniform selection of free trees."
+        Journal of Algorithms 2.2 (1981): 204-207.
+        https://doi.org/10.1016/0196-6774(81)90021-3
+    """
+    if n == 0:
+        raise nx.NetworkXPointlessConcept("the null graph is not a tree")
+
+    cache_trees = [0, 1]  # initial cache of number of rooted trees
+    cache_forests = [1]  # initial cache of number of rooted forests
+    if number_of_trees is None:
+        return _to_nx(*_random_unlabeled_tree(n, cache_trees, cache_forests, seed))
+    else:
+        return [
+            _to_nx(*_random_unlabeled_tree(n, cache_trees, cache_forests, seed))
+            for i in range(number_of_trees)
+        ]
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/triads.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/triads.py
new file mode 100644
index 0000000000000000000000000000000000000000..09b722dd1bd49dddae16086115d170ec989f8b06
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/generators/triads.py
@@ -0,0 +1,94 @@
+# See https://github.com/networkx/networkx/pull/1474
+# Copyright 2011 Reya Group <http://www.reyagroup.com>
+# Copyright 2011 Alex Levenson <alex@isnotinvain.com>
+# Copyright 2011 Diederik van Liere <diederik.vanliere@rotman.utoronto.ca>
+"""Functions that generate the triad graphs, that is, the possible
+digraphs on three nodes.
+
+"""
+
+import networkx as nx
+from networkx.classes import DiGraph
+
+__all__ = ["triad_graph"]
+
+#: Dictionary mapping triad name to list of directed edges in the
+#: digraph representation of that triad (with nodes 'a', 'b', and 'c').
+TRIAD_EDGES = {
+    "003": [],
+    "012": ["ab"],
+    "102": ["ab", "ba"],
+    "021D": ["ba", "bc"],
+    "021U": ["ab", "cb"],
+    "021C": ["ab", "bc"],
+    "111D": ["ac", "ca", "bc"],
+    "111U": ["ac", "ca", "cb"],
+    "030T": ["ab", "cb", "ac"],
+    "030C": ["ba", "cb", "ac"],
+    "201": ["ab", "ba", "ac", "ca"],
+    "120D": ["bc", "ba", "ac", "ca"],
+    "120U": ["ab", "cb", "ac", "ca"],
+    "120C": ["ab", "bc", "ac", "ca"],
+    "210": ["ab", "bc", "cb", "ac", "ca"],
+    "300": ["ab", "ba", "bc", "cb", "ac", "ca"],
+}
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def triad_graph(triad_name):
+    """Returns the triad graph with the given name.
+
+    Each string in the following tuple is a valid triad name::
+
+        (
+            "003",
+            "012",
+            "102",
+            "021D",
+            "021U",
+            "021C",
+            "111D",
+            "111U",
+            "030T",
+            "030C",
+            "201",
+            "120D",
+            "120U",
+            "120C",
+            "210",
+            "300",
+        )
+
+    Each triad name corresponds to one of the possible valid digraph on
+    three nodes.
+
+    Parameters
+    ----------
+    triad_name : string
+        The name of a triad, as described above.
+
+    Returns
+    -------
+    :class:`~networkx.DiGraph`
+        The digraph on three nodes with the given name. The nodes of the
+        graph are the single-character strings 'a', 'b', and 'c'.
+
+    Raises
+    ------
+    ValueError
+        If `triad_name` is not the name of a triad.
+
+    See also
+    --------
+    triadic_census
+
+    """
+    if triad_name not in TRIAD_EDGES:
+        raise ValueError(
+            f'unknown triad name "{triad_name}"; use one of the triad names'
+            " in the TRIAD_NAMES constant"
+        )
+    G = DiGraph()
+    G.add_nodes_from("abc")
+    G.add_edges_from(TRIAD_EDGES[triad_name])
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/__init__.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..119db185a1ae440fd2cdb6c7f531331642313c34
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/__init__.py
@@ -0,0 +1,13 @@
+from networkx.linalg.attrmatrix import *
+from networkx.linalg import attrmatrix
+from networkx.linalg.spectrum import *
+from networkx.linalg import spectrum
+from networkx.linalg.graphmatrix import *
+from networkx.linalg import graphmatrix
+from networkx.linalg.laplacianmatrix import *
+from networkx.linalg import laplacianmatrix
+from networkx.linalg.algebraicconnectivity import *
+from networkx.linalg.modularitymatrix import *
+from networkx.linalg import modularitymatrix
+from networkx.linalg.bethehessianmatrix import *
+from networkx.linalg import bethehessianmatrix
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/algebraicconnectivity.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/algebraicconnectivity.py
new file mode 100644
index 0000000000000000000000000000000000000000..55073c21c00e01dcd6a59a87556c71d73270b7d9
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/algebraicconnectivity.py
@@ -0,0 +1,655 @@
+"""
+Algebraic connectivity and Fiedler vectors of undirected graphs.
+"""
+
+import networkx as nx
+from networkx.utils import (
+    not_implemented_for,
+    np_random_state,
+    reverse_cuthill_mckee_ordering,
+)
+
+__all__ = [
+    "algebraic_connectivity",
+    "fiedler_vector",
+    "spectral_ordering",
+    "spectral_bisection",
+]
+
+
+class _PCGSolver:
+    """Preconditioned conjugate gradient method.
+
+    To solve Ax = b:
+        M = A.diagonal() # or some other preconditioner
+        solver = _PCGSolver(lambda x: A * x, lambda x: M * x)
+        x = solver.solve(b)
+
+    The inputs A and M are functions which compute
+    matrix multiplication on the argument.
+    A - multiply by the matrix A in Ax=b
+    M - multiply by M, the preconditioner surrogate for A
+
+    Warning: There is no limit on number of iterations.
+    """
+
+    def __init__(self, A, M):
+        self._A = A
+        self._M = M
+
+    def solve(self, B, tol):
+        import numpy as np
+
+        # Densifying step - can this be kept sparse?
+        B = np.asarray(B)
+        X = np.ndarray(B.shape, order="F")
+        for j in range(B.shape[1]):
+            X[:, j] = self._solve(B[:, j], tol)
+        return X
+
+    def _solve(self, b, tol):
+        import numpy as np
+        import scipy as sp
+
+        A = self._A
+        M = self._M
+        tol *= sp.linalg.blas.dasum(b)
+        # Initialize.
+        x = np.zeros(b.shape)
+        r = b.copy()
+        z = M(r)
+        rz = sp.linalg.blas.ddot(r, z)
+        p = z.copy()
+        # Iterate.
+        while True:
+            Ap = A(p)
+            alpha = rz / sp.linalg.blas.ddot(p, Ap)
+            x = sp.linalg.blas.daxpy(p, x, a=alpha)
+            r = sp.linalg.blas.daxpy(Ap, r, a=-alpha)
+            if sp.linalg.blas.dasum(r) < tol:
+                return x
+            z = M(r)
+            beta = sp.linalg.blas.ddot(r, z)
+            beta, rz = beta / rz, beta
+            p = sp.linalg.blas.daxpy(p, z, a=beta)
+
+
+class _LUSolver:
+    """LU factorization.
+
+    To solve Ax = b:
+        solver = _LUSolver(A)
+        x = solver.solve(b)
+
+    optional argument `tol` on solve method is ignored but included
+    to match _PCGsolver API.
+    """
+
+    def __init__(self, A):
+        import scipy as sp
+
+        self._LU = sp.sparse.linalg.splu(
+            A,
+            permc_spec="MMD_AT_PLUS_A",
+            diag_pivot_thresh=0.0,
+            options={"Equil": True, "SymmetricMode": True},
+        )
+
+    def solve(self, B, tol=None):
+        import numpy as np
+
+        B = np.asarray(B)
+        X = np.ndarray(B.shape, order="F")
+        for j in range(B.shape[1]):
+            X[:, j] = self._LU.solve(B[:, j])
+        return X
+
+
+def _preprocess_graph(G, weight):
+    """Compute edge weights and eliminate zero-weight edges."""
+    if G.is_directed():
+        H = nx.MultiGraph()
+        H.add_nodes_from(G)
+        H.add_weighted_edges_from(
+            ((u, v, e.get(weight, 1.0)) for u, v, e in G.edges(data=True) if u != v),
+            weight=weight,
+        )
+        G = H
+    if not G.is_multigraph():
+        edges = (
+            (u, v, abs(e.get(weight, 1.0))) for u, v, e in G.edges(data=True) if u != v
+        )
+    else:
+        edges = (
+            (u, v, sum(abs(e.get(weight, 1.0)) for e in G[u][v].values()))
+            for u, v in G.edges()
+            if u != v
+        )
+    H = nx.Graph()
+    H.add_nodes_from(G)
+    H.add_weighted_edges_from((u, v, e) for u, v, e in edges if e != 0)
+    return H
+
+
+def _rcm_estimate(G, nodelist):
+    """Estimate the Fiedler vector using the reverse Cuthill-McKee ordering."""
+    import numpy as np
+
+    G = G.subgraph(nodelist)
+    order = reverse_cuthill_mckee_ordering(G)
+    n = len(nodelist)
+    index = dict(zip(nodelist, range(n)))
+    x = np.ndarray(n, dtype=float)
+    for i, u in enumerate(order):
+        x[index[u]] = i
+    x -= (n - 1) / 2.0
+    return x
+
+
+def _tracemin_fiedler(L, X, normalized, tol, method):
+    """Compute the Fiedler vector of L using the TraceMIN-Fiedler algorithm.
+
+    The Fiedler vector of a connected undirected graph is the eigenvector
+    corresponding to the second smallest eigenvalue of the Laplacian matrix
+    of the graph. This function starts with the Laplacian L, not the Graph.
+
+    Parameters
+    ----------
+    L : Laplacian of a possibly weighted or normalized, but undirected graph
+
+    X : Initial guess for a solution. Usually a matrix of random numbers.
+        This function allows more than one column in X to identify more than
+        one eigenvector if desired.
+
+    normalized : bool
+        Whether the normalized Laplacian matrix is used.
+
+    tol : float
+        Tolerance of relative residual in eigenvalue computation.
+        Warning: There is no limit on number of iterations.
+
+    method : string
+        Should be 'tracemin_pcg' or 'tracemin_lu'.
+        Otherwise exception is raised.
+
+    Returns
+    -------
+    sigma, X : Two NumPy arrays of floats.
+        The lowest eigenvalues and corresponding eigenvectors of L.
+        The size of input X determines the size of these outputs.
+        As this is for Fiedler vectors, the zero eigenvalue (and
+        constant eigenvector) are avoided.
+    """
+    import numpy as np
+    import scipy as sp
+
+    n = X.shape[0]
+
+    if normalized:
+        # Form the normalized Laplacian matrix and determine the eigenvector of
+        # its nullspace.
+        e = np.sqrt(L.diagonal())
+        # TODO: rm csr_array wrapper when spdiags array creation becomes available
+        D = sp.sparse.csr_array(sp.sparse.spdiags(1 / e, 0, n, n, format="csr"))
+        L = D @ L @ D
+        e *= 1.0 / np.linalg.norm(e, 2)
+
+    if normalized:
+
+        def project(X):
+            """Make X orthogonal to the nullspace of L."""
+            X = np.asarray(X)
+            for j in range(X.shape[1]):
+                X[:, j] -= (X[:, j] @ e) * e
+
+    else:
+
+        def project(X):
+            """Make X orthogonal to the nullspace of L."""
+            X = np.asarray(X)
+            for j in range(X.shape[1]):
+                X[:, j] -= X[:, j].sum() / n
+
+    if method == "tracemin_pcg":
+        D = L.diagonal().astype(float)
+        solver = _PCGSolver(lambda x: L @ x, lambda x: D * x)
+    elif method == "tracemin_lu":
+        # Convert A to CSC to suppress SparseEfficiencyWarning.
+        A = sp.sparse.csc_array(L, dtype=float, copy=True)
+        # Force A to be nonsingular. Since A is the Laplacian matrix of a
+        # connected graph, its rank deficiency is one, and thus one diagonal
+        # element needs to modified. Changing to infinity forces a zero in the
+        # corresponding element in the solution.
+        i = (A.indptr[1:] - A.indptr[:-1]).argmax()
+        A[i, i] = np.inf
+        solver = _LUSolver(A)
+    else:
+        raise nx.NetworkXError(f"Unknown linear system solver: {method}")
+
+    # Initialize.
+    Lnorm = abs(L).sum(axis=1).flatten().max()
+    project(X)
+    W = np.ndarray(X.shape, order="F")
+
+    while True:
+        # Orthonormalize X.
+        X = np.linalg.qr(X)[0]
+        # Compute iteration matrix H.
+        W[:, :] = L @ X
+        H = X.T @ W
+        sigma, Y = sp.linalg.eigh(H, overwrite_a=True)
+        # Compute the Ritz vectors.
+        X = X @ Y
+        # Test for convergence exploiting the fact that L * X == W * Y.
+        res = sp.linalg.blas.dasum(W @ Y[:, 0] - sigma[0] * X[:, 0]) / Lnorm
+        if res < tol:
+            break
+        # Compute X = L \ X / (X' * (L \ X)).
+        # L \ X can have an arbitrary projection on the nullspace of L,
+        # which will be eliminated.
+        W[:, :] = solver.solve(X, tol)
+        X = (sp.linalg.inv(W.T @ X) @ W.T).T  # Preserves Fortran storage order.
+        project(X)
+
+    return sigma, np.asarray(X)
+
+
+def _get_fiedler_func(method):
+    """Returns a function that solves the Fiedler eigenvalue problem."""
+    import numpy as np
+
+    if method == "tracemin":  # old style keyword <v2.1
+        method = "tracemin_pcg"
+    if method in ("tracemin_pcg", "tracemin_lu"):
+
+        def find_fiedler(L, x, normalized, tol, seed):
+            q = 1 if method == "tracemin_pcg" else min(4, L.shape[0] - 1)
+            X = np.asarray(seed.normal(size=(q, L.shape[0]))).T
+            sigma, X = _tracemin_fiedler(L, X, normalized, tol, method)
+            return sigma[0], X[:, 0]
+
+    elif method == "lanczos" or method == "lobpcg":
+
+        def find_fiedler(L, x, normalized, tol, seed):
+            import scipy as sp
+
+            L = sp.sparse.csc_array(L, dtype=float)
+            n = L.shape[0]
+            if normalized:
+                # TODO: rm csc_array wrapping when spdiags array becomes available
+                D = sp.sparse.csc_array(
+                    sp.sparse.spdiags(
+                        1.0 / np.sqrt(L.diagonal()), [0], n, n, format="csc"
+                    )
+                )
+                L = D @ L @ D
+            if method == "lanczos" or n < 10:
+                # Avoid LOBPCG when n < 10 due to
+                # https://github.com/scipy/scipy/issues/3592
+                # https://github.com/scipy/scipy/pull/3594
+                sigma, X = sp.sparse.linalg.eigsh(
+                    L, 2, which="SM", tol=tol, return_eigenvectors=True
+                )
+                return sigma[1], X[:, 1]
+            else:
+                X = np.asarray(np.atleast_2d(x).T)
+                # TODO: rm csr_array wrapping when spdiags array becomes available
+                M = sp.sparse.csr_array(sp.sparse.spdiags(1.0 / L.diagonal(), 0, n, n))
+                Y = np.ones(n)
+                if normalized:
+                    Y /= D.diagonal()
+                sigma, X = sp.sparse.linalg.lobpcg(
+                    L, X, M=M, Y=np.atleast_2d(Y).T, tol=tol, maxiter=n, largest=False
+                )
+                return sigma[0], X[:, 0]
+
+    else:
+        raise nx.NetworkXError(f"unknown method {method!r}.")
+
+    return find_fiedler
+
+
+@not_implemented_for("directed")
+@np_random_state(5)
+@nx._dispatchable(edge_attrs="weight")
+def algebraic_connectivity(
+    G, weight="weight", normalized=False, tol=1e-8, method="tracemin_pcg", seed=None
+):
+    r"""Returns the algebraic connectivity of an undirected graph.
+
+    The algebraic connectivity of a connected undirected graph is the second
+    smallest eigenvalue of its Laplacian matrix.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    weight : object, optional (default: None)
+        The data key used to determine the weight of each edge. If None, then
+        each edge has unit weight.
+
+    normalized : bool, optional (default: False)
+        Whether the normalized Laplacian matrix is used.
+
+    tol : float, optional (default: 1e-8)
+        Tolerance of relative residual in eigenvalue computation.
+
+    method : string, optional (default: 'tracemin_pcg')
+        Method of eigenvalue computation. It must be one of the tracemin
+        options shown below (TraceMIN), 'lanczos' (Lanczos iteration)
+        or 'lobpcg' (LOBPCG).
+
+        The TraceMIN algorithm uses a linear system solver. The following
+        values allow specifying the solver to be used.
+
+        =============== ========================================
+        Value           Solver
+        =============== ========================================
+        'tracemin_pcg'  Preconditioned conjugate gradient method
+        'tracemin_lu'   LU factorization
+        =============== ========================================
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    algebraic_connectivity : float
+        Algebraic connectivity.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is directed.
+
+    NetworkXError
+        If G has less than two nodes.
+
+    Notes
+    -----
+    Edge weights are interpreted by their absolute values. For MultiGraph's,
+    weights of parallel edges are summed. Zero-weighted edges are ignored.
+
+    See Also
+    --------
+    laplacian_matrix
+
+    Examples
+    --------
+    For undirected graphs algebraic connectivity can tell us if a graph is connected or not
+    `G` is connected iff  ``algebraic_connectivity(G) > 0``:
+
+    >>> G = nx.complete_graph(5)
+    >>> nx.algebraic_connectivity(G) > 0
+    True
+    >>> G.add_node(10)  # G is no longer connected
+    >>> nx.algebraic_connectivity(G) > 0
+    False
+
+    """
+    if len(G) < 2:
+        raise nx.NetworkXError("graph has less than two nodes.")
+    G = _preprocess_graph(G, weight)
+    if not nx.is_connected(G):
+        return 0.0
+
+    L = nx.laplacian_matrix(G)
+    if L.shape[0] == 2:
+        return 2.0 * float(L[0, 0]) if not normalized else 2.0
+
+    find_fiedler = _get_fiedler_func(method)
+    x = None if method != "lobpcg" else _rcm_estimate(G, G)
+    sigma, fiedler = find_fiedler(L, x, normalized, tol, seed)
+    return float(sigma)
+
+
+@not_implemented_for("directed")
+@np_random_state(5)
+@nx._dispatchable(edge_attrs="weight")
+def fiedler_vector(
+    G, weight="weight", normalized=False, tol=1e-8, method="tracemin_pcg", seed=None
+):
+    """Returns the Fiedler vector of a connected undirected graph.
+
+    The Fiedler vector of a connected undirected graph is the eigenvector
+    corresponding to the second smallest eigenvalue of the Laplacian matrix
+    of the graph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    weight : object, optional (default: None)
+        The data key used to determine the weight of each edge. If None, then
+        each edge has unit weight.
+
+    normalized : bool, optional (default: False)
+        Whether the normalized Laplacian matrix is used.
+
+    tol : float, optional (default: 1e-8)
+        Tolerance of relative residual in eigenvalue computation.
+
+    method : string, optional (default: 'tracemin_pcg')
+        Method of eigenvalue computation. It must be one of the tracemin
+        options shown below (TraceMIN), 'lanczos' (Lanczos iteration)
+        or 'lobpcg' (LOBPCG).
+
+        The TraceMIN algorithm uses a linear system solver. The following
+        values allow specifying the solver to be used.
+
+        =============== ========================================
+        Value           Solver
+        =============== ========================================
+        'tracemin_pcg'  Preconditioned conjugate gradient method
+        'tracemin_lu'   LU factorization
+        =============== ========================================
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    fiedler_vector : NumPy array of floats.
+        Fiedler vector.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is directed.
+
+    NetworkXError
+        If G has less than two nodes or is not connected.
+
+    Notes
+    -----
+    Edge weights are interpreted by their absolute values. For MultiGraph's,
+    weights of parallel edges are summed. Zero-weighted edges are ignored.
+
+    See Also
+    --------
+    laplacian_matrix
+
+    Examples
+    --------
+    Given a connected graph the signs of the values in the Fiedler vector can be
+    used to partition the graph into two components.
+
+    >>> G = nx.barbell_graph(5, 0)
+    >>> nx.fiedler_vector(G, normalized=True, seed=1)
+    array([-0.32864129, -0.32864129, -0.32864129, -0.32864129, -0.26072899,
+            0.26072899,  0.32864129,  0.32864129,  0.32864129,  0.32864129])
+
+    The connected components are the two 5-node cliques of the barbell graph.
+    """
+    import numpy as np
+
+    if len(G) < 2:
+        raise nx.NetworkXError("graph has less than two nodes.")
+    G = _preprocess_graph(G, weight)
+    if not nx.is_connected(G):
+        raise nx.NetworkXError("graph is not connected.")
+
+    if len(G) == 2:
+        return np.array([1.0, -1.0])
+
+    find_fiedler = _get_fiedler_func(method)
+    L = nx.laplacian_matrix(G)
+    x = None if method != "lobpcg" else _rcm_estimate(G, G)
+    sigma, fiedler = find_fiedler(L, x, normalized, tol, seed)
+    return fiedler
+
+
+@np_random_state(5)
+@nx._dispatchable(edge_attrs="weight")
+def spectral_ordering(
+    G, weight="weight", normalized=False, tol=1e-8, method="tracemin_pcg", seed=None
+):
+    """Compute the spectral_ordering of a graph.
+
+    The spectral ordering of a graph is an ordering of its nodes where nodes
+    in the same weakly connected components appear contiguous and ordered by
+    their corresponding elements in the Fiedler vector of the component.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A graph.
+
+    weight : object, optional (default: None)
+        The data key used to determine the weight of each edge. If None, then
+        each edge has unit weight.
+
+    normalized : bool, optional (default: False)
+        Whether the normalized Laplacian matrix is used.
+
+    tol : float, optional (default: 1e-8)
+        Tolerance of relative residual in eigenvalue computation.
+
+    method : string, optional (default: 'tracemin_pcg')
+        Method of eigenvalue computation. It must be one of the tracemin
+        options shown below (TraceMIN), 'lanczos' (Lanczos iteration)
+        or 'lobpcg' (LOBPCG).
+
+        The TraceMIN algorithm uses a linear system solver. The following
+        values allow specifying the solver to be used.
+
+        =============== ========================================
+        Value           Solver
+        =============== ========================================
+        'tracemin_pcg'  Preconditioned conjugate gradient method
+        'tracemin_lu'   LU factorization
+        =============== ========================================
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    spectral_ordering : NumPy array of floats.
+        Spectral ordering of nodes.
+
+    Raises
+    ------
+    NetworkXError
+        If G is empty.
+
+    Notes
+    -----
+    Edge weights are interpreted by their absolute values. For MultiGraph's,
+    weights of parallel edges are summed. Zero-weighted edges are ignored.
+
+    See Also
+    --------
+    laplacian_matrix
+    """
+    if len(G) == 0:
+        raise nx.NetworkXError("graph is empty.")
+    G = _preprocess_graph(G, weight)
+
+    find_fiedler = _get_fiedler_func(method)
+    order = []
+    for component in nx.connected_components(G):
+        size = len(component)
+        if size > 2:
+            L = nx.laplacian_matrix(G, component)
+            x = None if method != "lobpcg" else _rcm_estimate(G, component)
+            sigma, fiedler = find_fiedler(L, x, normalized, tol, seed)
+            sort_info = zip(fiedler, range(size), component)
+            order.extend(u for x, c, u in sorted(sort_info))
+        else:
+            order.extend(component)
+
+    return order
+
+
+@nx._dispatchable(edge_attrs="weight")
+def spectral_bisection(
+    G, weight="weight", normalized=False, tol=1e-8, method="tracemin_pcg", seed=None
+):
+    """Bisect the graph using the Fiedler vector.
+
+    This method uses the Fiedler vector to bisect a graph.
+    The partition is defined by the nodes which are associated with
+    either positive or negative values in the vector.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+
+    weight : str, optional (default: weight)
+        The data key used to determine the weight of each edge. If None, then
+        each edge has unit weight.
+
+    normalized : bool, optional (default: False)
+        Whether the normalized Laplacian matrix is used.
+
+    tol : float, optional (default: 1e-8)
+        Tolerance of relative residual in eigenvalue computation.
+
+    method : string, optional (default: 'tracemin_pcg')
+        Method of eigenvalue computation. It must be one of the tracemin
+        options shown below (TraceMIN), 'lanczos' (Lanczos iteration)
+        or 'lobpcg' (LOBPCG).
+
+        The TraceMIN algorithm uses a linear system solver. The following
+        values allow specifying the solver to be used.
+
+        =============== ========================================
+        Value           Solver
+        =============== ========================================
+        'tracemin_pcg'  Preconditioned conjugate gradient method
+        'tracemin_lu'   LU factorization
+        =============== ========================================
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    bisection : tuple of sets
+        Sets with the bisection of nodes
+
+    Examples
+    --------
+    >>> G = nx.barbell_graph(3, 0)
+    >>> nx.spectral_bisection(G)
+    ({0, 1, 2}, {3, 4, 5})
+
+    References
+    ----------
+    .. [1] M. E. J Newman 'Networks: An Introduction', pages 364-370
+       Oxford University Press 2011.
+    """
+    import numpy as np
+
+    v = nx.fiedler_vector(G, weight, normalized, tol, method, seed)
+    nodes = np.array(list(G))
+    pos_vals = v >= 0
+
+    return set(nodes[~pos_vals].tolist()), set(nodes[pos_vals].tolist())
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/attrmatrix.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/attrmatrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..989e8ff5798e3d778dca6c13d762b09a3dd58bd7
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/attrmatrix.py
@@ -0,0 +1,466 @@
+"""
+Functions for constructing matrix-like objects from graph attributes.
+"""
+
+import networkx as nx
+
+__all__ = ["attr_matrix", "attr_sparse_matrix"]
+
+
+def _node_value(G, node_attr):
+    """Returns a function that returns a value from G.nodes[u].
+
+    We return a function expecting a node as its sole argument. Then, in the
+    simplest scenario, the returned function will return G.nodes[u][node_attr].
+    However, we also handle the case when `node_attr` is None (returns the node)
+    or when `node_attr` is a function itself.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph
+
+    node_attr : {None, str, callable}
+        Specification of how the value of the node attribute should be obtained
+        from the node attribute dictionary.
+
+    Returns
+    -------
+    value : function
+        A function expecting a node as its sole argument. The function will
+        returns a value from G.nodes[u] that depends on `edge_attr`.
+
+    """
+    if node_attr is None:
+
+        def value(u):
+            return u
+
+    elif not callable(node_attr):
+        # assume it is a key for the node attribute dictionary
+        def value(u):
+            return G.nodes[u][node_attr]
+
+    else:
+        # Advanced:  Allow users to specify something else.
+        #
+        # For example,
+        #     node_attr = lambda u: G.nodes[u].get('size', .5) * 3
+        #
+        value = node_attr
+
+    return value
+
+
+def _edge_value(G, edge_attr):
+    """Returns a function that returns a value from G[u][v].
+
+    Suppose there exists an edge between u and v.  Then we return a function
+    expecting u and v as arguments.  For Graph and DiGraph, G[u][v] is
+    the edge attribute dictionary, and the function (essentially) returns
+    G[u][v][edge_attr].  However, we also handle cases when `edge_attr` is None
+    and when it is a function itself. For MultiGraph and MultiDiGraph, G[u][v]
+    is a dictionary of all edges between u and v.  In this case, the returned
+    function sums the value of `edge_attr` for every edge between u and v.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    edge_attr : {None, str, callable}
+        Specification of how the value of the edge attribute should be obtained
+        from the edge attribute dictionary, G[u][v].  For multigraphs, G[u][v]
+        is a dictionary of all the edges between u and v.  This allows for
+        special treatment of multiedges.
+
+    Returns
+    -------
+    value : function
+        A function expecting two nodes as parameters. The nodes should
+        represent the from- and to- node of an edge. The function will
+        return a value from G[u][v] that depends on `edge_attr`.
+
+    """
+
+    if edge_attr is None:
+        # topological count of edges
+
+        if G.is_multigraph():
+
+            def value(u, v):
+                return len(G[u][v])
+
+        else:
+
+            def value(u, v):
+                return 1
+
+    elif not callable(edge_attr):
+        # assume it is a key for the edge attribute dictionary
+
+        if edge_attr == "weight":
+            # provide a default value
+            if G.is_multigraph():
+
+                def value(u, v):
+                    return sum(d.get(edge_attr, 1) for d in G[u][v].values())
+
+            else:
+
+                def value(u, v):
+                    return G[u][v].get(edge_attr, 1)
+
+        else:
+            # otherwise, the edge attribute MUST exist for each edge
+            if G.is_multigraph():
+
+                def value(u, v):
+                    return sum(d[edge_attr] for d in G[u][v].values())
+
+            else:
+
+                def value(u, v):
+                    return G[u][v][edge_attr]
+
+    else:
+        # Advanced:  Allow users to specify something else.
+        #
+        # Alternative default value:
+        #     edge_attr = lambda u,v: G[u][v].get('thickness', .5)
+        #
+        # Function on an attribute:
+        #     edge_attr = lambda u,v: abs(G[u][v]['weight'])
+        #
+        # Handle Multi(Di)Graphs differently:
+        #     edge_attr = lambda u,v: numpy.prod([d['size'] for d in G[u][v].values()])
+        #
+        # Ignore multiple edges
+        #     edge_attr = lambda u,v: 1 if len(G[u][v]) else 0
+        #
+        value = edge_attr
+
+    return value
+
+
+@nx._dispatchable(edge_attrs={"edge_attr": None}, node_attrs="node_attr")
+def attr_matrix(
+    G,
+    edge_attr=None,
+    node_attr=None,
+    normalized=False,
+    rc_order=None,
+    dtype=None,
+    order=None,
+):
+    """Returns the attribute matrix using attributes from `G` as a numpy array.
+
+    If only `G` is passed in, then the adjacency matrix is constructed.
+
+    Let A be a discrete set of values for the node attribute `node_attr`. Then
+    the elements of A represent the rows and columns of the constructed matrix.
+    Now, iterate through every edge e=(u,v) in `G` and consider the value
+    of the edge attribute `edge_attr`.  If ua and va are the values of the
+    node attribute `node_attr` for u and v, respectively, then the value of
+    the edge attribute is added to the matrix element at (ua, va).
+
+    Parameters
+    ----------
+    G : graph
+        The NetworkX graph used to construct the attribute matrix.
+
+    edge_attr : str, optional (default: number of edges for each matrix element)
+        Each element of the matrix represents a running total of the
+        specified edge attribute for edges whose node attributes correspond
+        to the rows/cols of the matrix. The attribute must be present for
+        all edges in the graph. If no attribute is specified, then we
+        just count the number of edges whose node attributes correspond
+        to the matrix element.
+
+    node_attr : str, optional (default: use nodes of the graph)
+        Each row and column in the matrix represents a particular value
+        of the node attribute.  The attribute must be present for all nodes
+        in the graph. Note, the values of this attribute should be reliably
+        hashable. So, float values are not recommended. If no attribute is
+        specified, then the rows and columns will be the nodes of the graph.
+
+    normalized : bool, optional (default: False)
+        If True, then each row is normalized by the summation of its values.
+
+    rc_order : list, optional (default: order of nodes in G)
+        A list of the node attribute values. This list specifies the ordering
+        of rows and columns of the array. If no ordering is provided, then
+        the ordering will be the same as the node order in `G`.
+        When `rc_order` is `None`, the function returns a 2-tuple ``(matrix, ordering)``
+
+    Other Parameters
+    ----------------
+    dtype : NumPy data-type, optional
+        A valid NumPy dtype used to initialize the array. Keep in mind certain
+        dtypes can yield unexpected results if the array is to be normalized.
+        The parameter is passed to numpy.zeros(). If unspecified, the NumPy
+        default is used.
+
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. This parameter is passed to
+        numpy.zeros(). If unspecified, the NumPy default is used.
+
+    Returns
+    -------
+    M : 2D NumPy ndarray
+        The attribute matrix.
+
+    ordering : list
+        If `rc_order` was specified, then only the attribute matrix is returned.
+        However, if `rc_order` was None, then the ordering used to construct
+        the matrix is returned as well.
+
+    Examples
+    --------
+    Construct an adjacency matrix:
+
+    >>> G = nx.Graph()
+    >>> G.add_edge(0, 1, thickness=1, weight=3)
+    >>> G.add_edge(0, 2, thickness=2)
+    >>> G.add_edge(1, 2, thickness=3)
+    >>> nx.attr_matrix(G, rc_order=[0, 1, 2])
+    array([[0., 1., 1.],
+           [1., 0., 1.],
+           [1., 1., 0.]])
+
+    Alternatively, we can obtain the matrix describing edge thickness.
+
+    >>> nx.attr_matrix(G, edge_attr="thickness", rc_order=[0, 1, 2])
+    array([[0., 1., 2.],
+           [1., 0., 3.],
+           [2., 3., 0.]])
+
+    We can also color the nodes and ask for the probability distribution over
+    all edges (u,v) describing:
+
+        Pr(v has color Y | u has color X)
+
+    >>> G.nodes[0]["color"] = "red"
+    >>> G.nodes[1]["color"] = "red"
+    >>> G.nodes[2]["color"] = "blue"
+    >>> rc = ["red", "blue"]
+    >>> nx.attr_matrix(G, node_attr="color", normalized=True, rc_order=rc)
+    array([[0.33333333, 0.66666667],
+           [1.        , 0.        ]])
+
+    For example, the above tells us that for all edges (u,v):
+
+        Pr( v is red  | u is red)  = 1/3
+        Pr( v is blue | u is red)  = 2/3
+
+        Pr( v is red  | u is blue) = 1
+        Pr( v is blue | u is blue) = 0
+
+    Finally, we can obtain the total weights listed by the node colors.
+
+    >>> nx.attr_matrix(G, edge_attr="weight", node_attr="color", rc_order=rc)
+    array([[3., 2.],
+           [2., 0.]])
+
+    Thus, the total weight over all edges (u,v) with u and v having colors:
+
+        (red, red)   is 3   # the sole contribution is from edge (0,1)
+        (red, blue)  is 2   # contributions from edges (0,2) and (1,2)
+        (blue, red)  is 2   # same as (red, blue) since graph is undirected
+        (blue, blue) is 0   # there are no edges with blue endpoints
+
+    """
+    import numpy as np
+
+    edge_value = _edge_value(G, edge_attr)
+    node_value = _node_value(G, node_attr)
+
+    if rc_order is None:
+        ordering = list({node_value(n) for n in G})
+    else:
+        ordering = rc_order
+
+    N = len(ordering)
+    undirected = not G.is_directed()
+    index = dict(zip(ordering, range(N)))
+    M = np.zeros((N, N), dtype=dtype, order=order)
+
+    seen = set()
+    for u, nbrdict in G.adjacency():
+        for v in nbrdict:
+            # Obtain the node attribute values.
+            i, j = index[node_value(u)], index[node_value(v)]
+            if v not in seen:
+                M[i, j] += edge_value(u, v)
+                if undirected:
+                    M[j, i] = M[i, j]
+
+        if undirected:
+            seen.add(u)
+
+    if normalized:
+        M /= M.sum(axis=1).reshape((N, 1))
+
+    if rc_order is None:
+        return M, ordering
+    else:
+        return M
+
+
+@nx._dispatchable(edge_attrs={"edge_attr": None}, node_attrs="node_attr")
+def attr_sparse_matrix(
+    G, edge_attr=None, node_attr=None, normalized=False, rc_order=None, dtype=None
+):
+    """Returns a SciPy sparse array using attributes from G.
+
+    If only `G` is passed in, then the adjacency matrix is constructed.
+
+    Let A be a discrete set of values for the node attribute `node_attr`. Then
+    the elements of A represent the rows and columns of the constructed matrix.
+    Now, iterate through every edge e=(u,v) in `G` and consider the value
+    of the edge attribute `edge_attr`.  If ua and va are the values of the
+    node attribute `node_attr` for u and v, respectively, then the value of
+    the edge attribute is added to the matrix element at (ua, va).
+
+    Parameters
+    ----------
+    G : graph
+        The NetworkX graph used to construct the NumPy matrix.
+
+    edge_attr : str, optional (default: number of edges for each matrix element)
+        Each element of the matrix represents a running total of the
+        specified edge attribute for edges whose node attributes correspond
+        to the rows/cols of the matrix. The attribute must be present for
+        all edges in the graph. If no attribute is specified, then we
+        just count the number of edges whose node attributes correspond
+        to the matrix element.
+
+    node_attr : str, optional (default: use nodes of the graph)
+        Each row and column in the matrix represents a particular value
+        of the node attribute.  The attribute must be present for all nodes
+        in the graph. Note, the values of this attribute should be reliably
+        hashable. So, float values are not recommended. If no attribute is
+        specified, then the rows and columns will be the nodes of the graph.
+
+    normalized : bool, optional (default: False)
+        If True, then each row is normalized by the summation of its values.
+
+    rc_order : list, optional (default: order of nodes in G)
+        A list of the node attribute values. This list specifies the ordering
+        of rows and columns of the array and the return value. If no ordering
+        is provided, then the ordering will be that of nodes in `G`.
+
+    Other Parameters
+    ----------------
+    dtype : NumPy data-type, optional
+        A valid NumPy dtype used to initialize the array. Keep in mind certain
+        dtypes can yield unexpected results if the array is to be normalized.
+        The parameter is passed to numpy.zeros(). If unspecified, the NumPy
+        default is used.
+
+    Returns
+    -------
+    M : SciPy sparse array
+        The attribute matrix.
+
+    ordering : list
+        If `rc_order` was specified, then only the matrix is returned.
+        However, if `rc_order` was None, then the ordering used to construct
+        the matrix is returned as well.
+
+    Examples
+    --------
+    Construct an adjacency matrix:
+
+    >>> G = nx.Graph()
+    >>> G.add_edge(0, 1, thickness=1, weight=3)
+    >>> G.add_edge(0, 2, thickness=2)
+    >>> G.add_edge(1, 2, thickness=3)
+    >>> M = nx.attr_sparse_matrix(G, rc_order=[0, 1, 2])
+    >>> M.toarray()
+    array([[0., 1., 1.],
+           [1., 0., 1.],
+           [1., 1., 0.]])
+
+    Alternatively, we can obtain the matrix describing edge thickness.
+
+    >>> M = nx.attr_sparse_matrix(G, edge_attr="thickness", rc_order=[0, 1, 2])
+    >>> M.toarray()
+    array([[0., 1., 2.],
+           [1., 0., 3.],
+           [2., 3., 0.]])
+
+    We can also color the nodes and ask for the probability distribution over
+    all edges (u,v) describing:
+
+        Pr(v has color Y | u has color X)
+
+    >>> G.nodes[0]["color"] = "red"
+    >>> G.nodes[1]["color"] = "red"
+    >>> G.nodes[2]["color"] = "blue"
+    >>> rc = ["red", "blue"]
+    >>> M = nx.attr_sparse_matrix(G, node_attr="color", normalized=True, rc_order=rc)
+    >>> M.toarray()
+    array([[0.33333333, 0.66666667],
+           [1.        , 0.        ]])
+
+    For example, the above tells us that for all edges (u,v):
+
+        Pr( v is red  | u is red)  = 1/3
+        Pr( v is blue | u is red)  = 2/3
+
+        Pr( v is red  | u is blue) = 1
+        Pr( v is blue | u is blue) = 0
+
+    Finally, we can obtain the total weights listed by the node colors.
+
+    >>> M = nx.attr_sparse_matrix(G, edge_attr="weight", node_attr="color", rc_order=rc)
+    >>> M.toarray()
+    array([[3., 2.],
+           [2., 0.]])
+
+    Thus, the total weight over all edges (u,v) with u and v having colors:
+
+        (red, red)   is 3   # the sole contribution is from edge (0,1)
+        (red, blue)  is 2   # contributions from edges (0,2) and (1,2)
+        (blue, red)  is 2   # same as (red, blue) since graph is undirected
+        (blue, blue) is 0   # there are no edges with blue endpoints
+
+    """
+    import numpy as np
+    import scipy as sp
+
+    edge_value = _edge_value(G, edge_attr)
+    node_value = _node_value(G, node_attr)
+
+    if rc_order is None:
+        ordering = list({node_value(n) for n in G})
+    else:
+        ordering = rc_order
+
+    N = len(ordering)
+    undirected = not G.is_directed()
+    index = dict(zip(ordering, range(N)))
+    M = sp.sparse.lil_array((N, N), dtype=dtype)
+
+    seen = set()
+    for u, nbrdict in G.adjacency():
+        for v in nbrdict:
+            # Obtain the node attribute values.
+            i, j = index[node_value(u)], index[node_value(v)]
+            if v not in seen:
+                M[i, j] += edge_value(u, v)
+                if undirected:
+                    M[j, i] = M[i, j]
+
+        if undirected:
+            seen.add(u)
+
+    if normalized:
+        M *= 1 / M.sum(axis=1)[:, np.newaxis]  # in-place mult preserves sparse
+
+    if rc_order is None:
+        return M, ordering
+    else:
+        return M
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/bethehessianmatrix.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/bethehessianmatrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d42fc6d37f033f058338d9a7a8eb718bd6dc14e
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/bethehessianmatrix.py
@@ -0,0 +1,79 @@
+"""Bethe Hessian or deformed Laplacian matrix of graphs."""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["bethe_hessian_matrix"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable
+def bethe_hessian_matrix(G, r=None, nodelist=None):
+    r"""Returns the Bethe Hessian matrix of G.
+
+    The Bethe Hessian is a family of matrices parametrized by r, defined as
+    H(r) = (r^2 - 1) I - r A + D where A is the adjacency matrix, D is the
+    diagonal matrix of node degrees, and I is the identify matrix. It is equal
+    to the graph laplacian when the regularizer r = 1.
+
+    The default choice of regularizer should be the ratio [2]_
+
+    .. math::
+      r_m = \left(\sum k_i \right)^{-1}\left(\sum k_i^2 \right) - 1
+
+    Parameters
+    ----------
+    G : Graph
+       A NetworkX graph
+    r : float
+       Regularizer parameter
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by ``G.nodes()``.
+
+    Returns
+    -------
+    H : scipy.sparse.csr_array
+      The Bethe Hessian matrix of `G`, with parameter `r`.
+
+    Examples
+    --------
+    >>> k = [3, 2, 2, 1, 0]
+    >>> G = nx.havel_hakimi_graph(k)
+    >>> H = nx.bethe_hessian_matrix(G)
+    >>> H.toarray()
+    array([[ 3.5625, -1.25  , -1.25  , -1.25  ,  0.    ],
+           [-1.25  ,  2.5625, -1.25  ,  0.    ,  0.    ],
+           [-1.25  , -1.25  ,  2.5625,  0.    ,  0.    ],
+           [-1.25  ,  0.    ,  0.    ,  1.5625,  0.    ],
+           [ 0.    ,  0.    ,  0.    ,  0.    ,  0.5625]])
+
+    See Also
+    --------
+    bethe_hessian_spectrum
+    adjacency_matrix
+    laplacian_matrix
+
+    References
+    ----------
+    .. [1] A. Saade, F. Krzakala and L. Zdeborová
+       "Spectral Clustering of Graphs with the Bethe Hessian",
+       Advances in Neural Information Processing Systems, 2014.
+    .. [2] C. M. Le, E. Levina
+       "Estimating the number of communities in networks by spectral methods"
+       arXiv:1507.00827, 2015.
+    """
+    import scipy as sp
+
+    if nodelist is None:
+        nodelist = list(G)
+    if r is None:
+        r = sum(d**2 for v, d in nx.degree(G)) / sum(d for v, d in nx.degree(G)) - 1
+    A = nx.to_scipy_sparse_array(G, nodelist=nodelist, format="csr")
+    n, m = A.shape
+    # TODO: Rm csr_array wrapper when spdiags array creation becomes available
+    D = sp.sparse.csr_array(sp.sparse.spdiags(A.sum(axis=1), 0, m, n, format="csr"))
+    # TODO: Rm csr_array wrapper when eye array creation becomes available
+    I = sp.sparse.csr_array(sp.sparse.eye(m, n, format="csr"))
+    return (r**2 - 1) * I - r * A + D
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/graphmatrix.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/graphmatrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f477bcccc5b6a077b8c7f1807299d71664ebede
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/graphmatrix.py
@@ -0,0 +1,168 @@
+"""
+Adjacency matrix and incidence matrix of graphs.
+"""
+
+import networkx as nx
+
+__all__ = ["incidence_matrix", "adjacency_matrix"]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def incidence_matrix(
+    G, nodelist=None, edgelist=None, oriented=False, weight=None, *, dtype=None
+):
+    """Returns incidence matrix of G.
+
+    The incidence matrix assigns each row to a node and each column to an edge.
+    For a standard incidence matrix a 1 appears wherever a row's node is
+    incident on the column's edge.  For an oriented incidence matrix each
+    edge is assigned an orientation (arbitrarily for undirected and aligning to
+    direction for directed).  A -1 appears for the source (tail) of an edge and
+    1 for the destination (head) of the edge.  The elements are zero otherwise.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nodelist : list, optional   (default= all nodes in G)
+       The rows are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by G.nodes().
+
+    edgelist : list, optional (default= all edges in G)
+       The columns are ordered according to the edges in edgelist.
+       If edgelist is None, then the ordering is produced by G.edges().
+
+    oriented: bool, optional (default=False)
+       If True, matrix elements are +1 or -1 for the head or tail node
+       respectively of each edge.  If False, +1 occurs at both nodes.
+
+    weight : string or None, optional (default=None)
+       The edge data key used to provide each value in the matrix.
+       If None, then each edge has weight 1.  Edge weights, if used,
+       should be positive so that the orientation can provide the sign.
+
+    dtype : a NumPy dtype or None (default=None)
+        The dtype of the output sparse array. This type should be a compatible
+        type of the weight argument, eg. if weight would return a float this
+        argument should also be a float.
+        If None, then the default for SciPy is used.
+
+    Returns
+    -------
+    A : SciPy sparse array
+      The incidence matrix of G.
+
+    Notes
+    -----
+    For MultiGraph/MultiDiGraph, the edges in edgelist should be
+    (u,v,key) 3-tuples.
+
+    "Networks are the best discrete model for so many problems in
+    applied mathematics" [1]_.
+
+    References
+    ----------
+    .. [1] Gil Strang, Network applications: A = incidence matrix,
+       http://videolectures.net/mit18085f07_strang_lec03/
+    """
+    import scipy as sp
+
+    if nodelist is None:
+        nodelist = list(G)
+    if edgelist is None:
+        if G.is_multigraph():
+            edgelist = list(G.edges(keys=True))
+        else:
+            edgelist = list(G.edges())
+    A = sp.sparse.lil_array((len(nodelist), len(edgelist)), dtype=dtype)
+    node_index = {node: i for i, node in enumerate(nodelist)}
+    for ei, e in enumerate(edgelist):
+        (u, v) = e[:2]
+        if u == v:
+            continue  # self loops give zero column
+        try:
+            ui = node_index[u]
+            vi = node_index[v]
+        except KeyError as err:
+            raise nx.NetworkXError(
+                f"node {u} or {v} in edgelist but not in nodelist"
+            ) from err
+        if weight is None:
+            wt = 1
+        else:
+            if G.is_multigraph():
+                ekey = e[2]
+                wt = G[u][v][ekey].get(weight, 1)
+            else:
+                wt = G[u][v].get(weight, 1)
+        if oriented:
+            A[ui, ei] = -wt
+            A[vi, ei] = wt
+        else:
+            A[ui, ei] = wt
+            A[vi, ei] = wt
+    return A.asformat("csc")
+
+
+@nx._dispatchable(edge_attrs="weight")
+def adjacency_matrix(G, nodelist=None, dtype=None, weight="weight"):
+    """Returns adjacency matrix of `G`.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in `nodelist`.
+       If ``nodelist=None`` (the default), then the ordering is produced by
+       ``G.nodes()``.
+
+    dtype : NumPy data-type, optional
+        The desired data-type for the array.
+        If `None`, then the NumPy default is used.
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to provide each value in the matrix.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    A : SciPy sparse array
+      Adjacency matrix representation of G.
+
+    Notes
+    -----
+    For directed graphs, entry ``i, j`` corresponds to an edge from ``i`` to ``j``.
+
+    If you want a pure Python adjacency matrix representation try
+    :func:`~networkx.convert.to_dict_of_dicts` which will return a
+    dictionary-of-dictionaries format that can be addressed as a
+    sparse matrix.
+
+    For multigraphs with parallel edges the weights are summed.
+    See :func:`networkx.convert_matrix.to_numpy_array` for other options.
+
+    The convention used for self-loop edges in graphs is to assign the
+    diagonal matrix entry value to the edge weight attribute
+    (or the number 1 if the edge has no weight attribute).  If the
+    alternate convention of doubling the edge weight is desired the
+    resulting SciPy sparse array can be modified as follows::
+
+        >>> G = nx.Graph([(1, 1)])
+        >>> A = nx.adjacency_matrix(G)
+        >>> A.toarray()
+        array([[1]])
+        >>> A.setdiag(A.diagonal() * 2)
+        >>> A.toarray()
+        array([[2]])
+
+    See Also
+    --------
+    to_numpy_array
+    to_scipy_sparse_array
+    to_dict_of_dicts
+    adjacency_spectrum
+    """
+    return nx.to_scipy_sparse_array(G, nodelist=nodelist, dtype=dtype, weight=weight)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/laplacianmatrix.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/laplacianmatrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..690924fdfd1fec423f3f120c80bde3af25657fbd
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/laplacianmatrix.py
@@ -0,0 +1,520 @@
+"""Laplacian matrix of graphs.
+
+All calculations here are done using the out-degree. For Laplacians using
+in-degree, use `G.reverse(copy=False)` instead of `G` and take the transpose.
+
+The `laplacian_matrix` function provides an unnormalized matrix,
+while `normalized_laplacian_matrix`, `directed_laplacian_matrix`,
+and `directed_combinatorial_laplacian_matrix` are all normalized.
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = [
+    "laplacian_matrix",
+    "normalized_laplacian_matrix",
+    "directed_laplacian_matrix",
+    "directed_combinatorial_laplacian_matrix",
+]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def laplacian_matrix(G, nodelist=None, weight="weight"):
+    """Returns the Laplacian matrix of G.
+
+    The graph Laplacian is the matrix L = D - A, where
+    A is the adjacency matrix and D is the diagonal matrix of node degrees.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by G.nodes().
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    L : SciPy sparse array
+      The Laplacian matrix of G.
+
+    Notes
+    -----
+    For MultiGraph, the edges weights are summed.
+
+    This returns an unnormalized matrix. For a normalized output,
+    use `normalized_laplacian_matrix`, `directed_laplacian_matrix`,
+    or `directed_combinatorial_laplacian_matrix`.
+
+    This calculation uses the out-degree of the graph `G`. To use the
+    in-degree for calculations instead, use `G.reverse(copy=False)` and
+    take the transpose.
+
+    See Also
+    --------
+    :func:`~networkx.convert_matrix.to_numpy_array`
+    normalized_laplacian_matrix
+    directed_laplacian_matrix
+    directed_combinatorial_laplacian_matrix
+    :func:`~networkx.linalg.spectrum.laplacian_spectrum`
+
+    Examples
+    --------
+    For graphs with multiple connected components, L is permutation-similar
+    to a block diagonal matrix where each block is the respective Laplacian
+    matrix for each component.
+
+    >>> G = nx.Graph([(1, 2), (2, 3), (4, 5)])
+    >>> print(nx.laplacian_matrix(G).toarray())
+    [[ 1 -1  0  0  0]
+     [-1  2 -1  0  0]
+     [ 0 -1  1  0  0]
+     [ 0  0  0  1 -1]
+     [ 0  0  0 -1  1]]
+
+    >>> edges = [
+    ...     (1, 2),
+    ...     (2, 1),
+    ...     (2, 4),
+    ...     (4, 3),
+    ...     (3, 4),
+    ... ]
+    >>> DiG = nx.DiGraph(edges)
+    >>> print(nx.laplacian_matrix(DiG).toarray())
+    [[ 1 -1  0  0]
+     [-1  2 -1  0]
+     [ 0  0  1 -1]
+     [ 0  0 -1  1]]
+
+    Notice that node 4 is represented by the third column and row. This is because
+    by default the row/column order is the order of `G.nodes` (i.e. the node added
+    order -- in the edgelist, 4 first appears in (2, 4), before node 3 in edge (4, 3).)
+    To control the node order of the matrix, use the `nodelist` argument.
+
+    >>> print(nx.laplacian_matrix(DiG, nodelist=[1, 2, 3, 4]).toarray())
+    [[ 1 -1  0  0]
+     [-1  2  0 -1]
+     [ 0  0  1 -1]
+     [ 0  0 -1  1]]
+
+    This calculation uses the out-degree of the graph `G`. To use the
+    in-degree for calculations instead, use `G.reverse(copy=False)` and
+    take the transpose.
+
+    >>> print(nx.laplacian_matrix(DiG.reverse(copy=False)).toarray().T)
+    [[ 1 -1  0  0]
+     [-1  1 -1  0]
+     [ 0  0  2 -1]
+     [ 0  0 -1  1]]
+
+    References
+    ----------
+    .. [1] Langville, Amy N., and Carl D. Meyer. Google’s PageRank and Beyond:
+       The Science of Search Engine Rankings. Princeton University Press, 2006.
+
+    """
+    import scipy as sp
+
+    if nodelist is None:
+        nodelist = list(G)
+    A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format="csr")
+    n, m = A.shape
+    # TODO: rm csr_array wrapper when spdiags can produce arrays
+    D = sp.sparse.csr_array(sp.sparse.spdiags(A.sum(axis=1), 0, m, n, format="csr"))
+    return D - A
+
+
+@nx._dispatchable(edge_attrs="weight")
+def normalized_laplacian_matrix(G, nodelist=None, weight="weight"):
+    r"""Returns the normalized Laplacian matrix of G.
+
+    The normalized graph Laplacian is the matrix
+
+    .. math::
+
+        N = D^{-1/2} L D^{-1/2}
+
+    where `L` is the graph Laplacian and `D` is the diagonal matrix of
+    node degrees [1]_.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by G.nodes().
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    N : SciPy sparse array
+      The normalized Laplacian matrix of G.
+
+    Notes
+    -----
+    For MultiGraph, the edges weights are summed.
+    See :func:`to_numpy_array` for other options.
+
+    If the Graph contains selfloops, D is defined as ``diag(sum(A, 1))``, where A is
+    the adjacency matrix [2]_.
+
+    This calculation uses the out-degree of the graph `G`. To use the
+    in-degree for calculations instead, use `G.reverse(copy=False)` and
+    take the transpose.
+
+    For an unnormalized output, use `laplacian_matrix`.
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> edges = [
+    ...     (1, 2),
+    ...     (2, 1),
+    ...     (2, 4),
+    ...     (4, 3),
+    ...     (3, 4),
+    ... ]
+    >>> DiG = nx.DiGraph(edges)
+    >>> print(nx.normalized_laplacian_matrix(DiG).toarray())
+    [[ 1.         -0.70710678  0.          0.        ]
+     [-0.70710678  1.         -0.70710678  0.        ]
+     [ 0.          0.          1.         -1.        ]
+     [ 0.          0.         -1.          1.        ]]
+
+    Notice that node 4 is represented by the third column and row. This is because
+    by default the row/column order is the order of `G.nodes` (i.e. the node added
+    order -- in the edgelist, 4 first appears in (2, 4), before node 3 in edge (4, 3).)
+    To control the node order of the matrix, use the `nodelist` argument.
+
+    >>> print(nx.normalized_laplacian_matrix(DiG, nodelist=[1, 2, 3, 4]).toarray())
+    [[ 1.         -0.70710678  0.          0.        ]
+     [-0.70710678  1.          0.         -0.70710678]
+     [ 0.          0.          1.         -1.        ]
+     [ 0.          0.         -1.          1.        ]]
+    >>> G = nx.Graph(edges)
+    >>> print(nx.normalized_laplacian_matrix(G).toarray())
+    [[ 1.         -0.70710678  0.          0.        ]
+     [-0.70710678  1.         -0.5         0.        ]
+     [ 0.         -0.5         1.         -0.70710678]
+     [ 0.          0.         -0.70710678  1.        ]]
+
+    See Also
+    --------
+    laplacian_matrix
+    normalized_laplacian_spectrum
+    directed_laplacian_matrix
+    directed_combinatorial_laplacian_matrix
+
+    References
+    ----------
+    .. [1] Fan Chung-Graham, Spectral Graph Theory,
+       CBMS Regional Conference Series in Mathematics, Number 92, 1997.
+    .. [2] Steve Butler, Interlacing For Weighted Graphs Using The Normalized
+       Laplacian, Electronic Journal of Linear Algebra, Volume 16, pp. 90-98,
+       March 2007.
+    .. [3] Langville, Amy N., and Carl D. Meyer. Google’s PageRank and Beyond:
+       The Science of Search Engine Rankings. Princeton University Press, 2006.
+    """
+    import numpy as np
+    import scipy as sp
+
+    if nodelist is None:
+        nodelist = list(G)
+    A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format="csr")
+    n, _ = A.shape
+    diags = A.sum(axis=1)
+    # TODO: rm csr_array wrapper when spdiags can produce arrays
+    D = sp.sparse.csr_array(sp.sparse.spdiags(diags, 0, n, n, format="csr"))
+    L = D - A
+    with np.errstate(divide="ignore"):
+        diags_sqrt = 1.0 / np.sqrt(diags)
+    diags_sqrt[np.isinf(diags_sqrt)] = 0
+    # TODO: rm csr_array wrapper when spdiags can produce arrays
+    DH = sp.sparse.csr_array(sp.sparse.spdiags(diags_sqrt, 0, n, n, format="csr"))
+    return DH @ (L @ DH)
+
+
+###############################################################################
+# Code based on work from https://github.com/bjedwards
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable(edge_attrs="weight")
+def directed_laplacian_matrix(
+    G, nodelist=None, weight="weight", walk_type=None, alpha=0.95
+):
+    r"""Returns the directed Laplacian matrix of G.
+
+    The graph directed Laplacian is the matrix
+
+    .. math::
+
+        L = I - \frac{1}{2} \left (\Phi^{1/2} P \Phi^{-1/2} + \Phi^{-1/2} P^T \Phi^{1/2} \right )
+
+    where `I` is the identity matrix, `P` is the transition matrix of the
+    graph, and `\Phi` a matrix with the Perron vector of `P` in the diagonal and
+    zeros elsewhere [1]_.
+
+    Depending on the value of walk_type, `P` can be the transition matrix
+    induced by a random walk, a lazy random walk, or a random walk with
+    teleportation (PageRank).
+
+    Parameters
+    ----------
+    G : DiGraph
+       A NetworkX graph
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by G.nodes().
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    walk_type : string or None, optional (default=None)
+       One of ``"random"``, ``"lazy"``, or ``"pagerank"``. If ``walk_type=None``
+       (the default), then a value is selected according to the properties of `G`:
+       - ``walk_type="random"`` if `G` is strongly connected and aperiodic
+       - ``walk_type="lazy"`` if `G` is strongly connected but not aperiodic
+       - ``walk_type="pagerank"`` for all other cases.
+
+    alpha : real
+       (1 - alpha) is the teleportation probability used with pagerank
+
+    Returns
+    -------
+    L : NumPy matrix
+      Normalized Laplacian of G.
+
+    Notes
+    -----
+    Only implemented for DiGraphs
+
+    The result is always a symmetric matrix.
+
+    This calculation uses the out-degree of the graph `G`. To use the
+    in-degree for calculations instead, use `G.reverse(copy=False)` and
+    take the transpose.
+
+    See Also
+    --------
+    laplacian_matrix
+    normalized_laplacian_matrix
+    directed_combinatorial_laplacian_matrix
+
+    References
+    ----------
+    .. [1] Fan Chung (2005).
+       Laplacians and the Cheeger inequality for directed graphs.
+       Annals of Combinatorics, 9(1), 2005
+    """
+    import numpy as np
+    import scipy as sp
+
+    # NOTE: P has type ndarray if walk_type=="pagerank", else csr_array
+    P = _transition_matrix(
+        G, nodelist=nodelist, weight=weight, walk_type=walk_type, alpha=alpha
+    )
+
+    n, m = P.shape
+
+    evals, evecs = sp.sparse.linalg.eigs(P.T, k=1)
+    v = evecs.flatten().real
+    p = v / v.sum()
+    # p>=0 by Perron-Frobenius Thm. Use abs() to fix roundoff across zero gh-6865
+    sqrtp = np.sqrt(np.abs(p))
+    Q = (
+        # TODO: rm csr_array wrapper when spdiags creates arrays
+        sp.sparse.csr_array(sp.sparse.spdiags(sqrtp, 0, n, n))
+        @ P
+        # TODO: rm csr_array wrapper when spdiags creates arrays
+        @ sp.sparse.csr_array(sp.sparse.spdiags(1.0 / sqrtp, 0, n, n))
+    )
+    # NOTE: This could be sparsified for the non-pagerank cases
+    I = np.identity(len(G))
+
+    return I - (Q + Q.T) / 2.0
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable(edge_attrs="weight")
+def directed_combinatorial_laplacian_matrix(
+    G, nodelist=None, weight="weight", walk_type=None, alpha=0.95
+):
+    r"""Return the directed combinatorial Laplacian matrix of G.
+
+    The graph directed combinatorial Laplacian is the matrix
+
+    .. math::
+
+        L = \Phi - \frac{1}{2} \left (\Phi P + P^T \Phi \right)
+
+    where `P` is the transition matrix of the graph and `\Phi` a matrix
+    with the Perron vector of `P` in the diagonal and zeros elsewhere [1]_.
+
+    Depending on the value of walk_type, `P` can be the transition matrix
+    induced by a random walk, a lazy random walk, or a random walk with
+    teleportation (PageRank).
+
+    Parameters
+    ----------
+    G : DiGraph
+       A NetworkX graph
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by G.nodes().
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    walk_type : string or None, optional (default=None)
+        One of ``"random"``, ``"lazy"``, or ``"pagerank"``. If ``walk_type=None``
+        (the default), then a value is selected according to the properties of `G`:
+        - ``walk_type="random"`` if `G` is strongly connected and aperiodic
+        - ``walk_type="lazy"`` if `G` is strongly connected but not aperiodic
+        - ``walk_type="pagerank"`` for all other cases.
+
+    alpha : real
+       (1 - alpha) is the teleportation probability used with pagerank
+
+    Returns
+    -------
+    L : NumPy matrix
+      Combinatorial Laplacian of G.
+
+    Notes
+    -----
+    Only implemented for DiGraphs
+
+    The result is always a symmetric matrix.
+
+    This calculation uses the out-degree of the graph `G`. To use the
+    in-degree for calculations instead, use `G.reverse(copy=False)` and
+    take the transpose.
+
+    See Also
+    --------
+    laplacian_matrix
+    normalized_laplacian_matrix
+    directed_laplacian_matrix
+
+    References
+    ----------
+    .. [1] Fan Chung (2005).
+       Laplacians and the Cheeger inequality for directed graphs.
+       Annals of Combinatorics, 9(1), 2005
+    """
+    import scipy as sp
+
+    P = _transition_matrix(
+        G, nodelist=nodelist, weight=weight, walk_type=walk_type, alpha=alpha
+    )
+
+    n, m = P.shape
+
+    evals, evecs = sp.sparse.linalg.eigs(P.T, k=1)
+    v = evecs.flatten().real
+    p = v / v.sum()
+    # NOTE: could be improved by not densifying
+    # TODO: Rm csr_array wrapper when spdiags array creation becomes available
+    Phi = sp.sparse.csr_array(sp.sparse.spdiags(p, 0, n, n)).toarray()
+
+    return Phi - (Phi @ P + P.T @ Phi) / 2.0
+
+
+def _transition_matrix(G, nodelist=None, weight="weight", walk_type=None, alpha=0.95):
+    """Returns the transition matrix of G.
+
+    This is a row stochastic giving the transition probabilities while
+    performing a random walk on the graph. Depending on the value of walk_type,
+    P can be the transition matrix induced by a random walk, a lazy random walk,
+    or a random walk with teleportation (PageRank).
+
+    Parameters
+    ----------
+    G : DiGraph
+       A NetworkX graph
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by G.nodes().
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    walk_type : string or None, optional (default=None)
+       One of ``"random"``, ``"lazy"``, or ``"pagerank"``. If ``walk_type=None``
+       (the default), then a value is selected according to the properties of `G`:
+        - ``walk_type="random"`` if `G` is strongly connected and aperiodic
+        - ``walk_type="lazy"`` if `G` is strongly connected but not aperiodic
+        - ``walk_type="pagerank"`` for all other cases.
+
+    alpha : real
+       (1 - alpha) is the teleportation probability used with pagerank
+
+    Returns
+    -------
+    P : numpy.ndarray
+      transition matrix of G.
+
+    Raises
+    ------
+    NetworkXError
+        If walk_type not specified or alpha not in valid range
+    """
+    import numpy as np
+    import scipy as sp
+
+    if walk_type is None:
+        if nx.is_strongly_connected(G):
+            if nx.is_aperiodic(G):
+                walk_type = "random"
+            else:
+                walk_type = "lazy"
+        else:
+            walk_type = "pagerank"
+
+    A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, dtype=float)
+    n, m = A.shape
+    if walk_type in ["random", "lazy"]:
+        # TODO: Rm csr_array wrapper when spdiags array creation becomes available
+        DI = sp.sparse.csr_array(sp.sparse.spdiags(1.0 / A.sum(axis=1), 0, n, n))
+        if walk_type == "random":
+            P = DI @ A
+        else:
+            # TODO: Rm csr_array wrapper when identity array creation becomes available
+            I = sp.sparse.csr_array(sp.sparse.identity(n))
+            P = (I + DI @ A) / 2.0
+
+    elif walk_type == "pagerank":
+        if not (0 < alpha < 1):
+            raise nx.NetworkXError("alpha must be between 0 and 1")
+        # this is using a dense representation. NOTE: This should be sparsified!
+        A = A.toarray()
+        # add constant to dangling nodes' row
+        A[A.sum(axis=1) == 0, :] = 1 / n
+        # normalize
+        A = A / A.sum(axis=1)[np.newaxis, :].T
+        P = alpha * A + (1 - alpha) / n
+    else:
+        raise nx.NetworkXError("walk_type must be random, lazy, or pagerank")
+
+    return P
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/modularitymatrix.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/modularitymatrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..0287910bdccc61263afa4d6adfa2b0d78afc4daa
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/modularitymatrix.py
@@ -0,0 +1,166 @@
+"""Modularity matrix of graphs."""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["modularity_matrix", "directed_modularity_matrix"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(edge_attrs="weight")
+def modularity_matrix(G, nodelist=None, weight=None):
+    r"""Returns the modularity matrix of G.
+
+    The modularity matrix is the matrix B = A - <A>, where A is the adjacency
+    matrix and <A> is the average adjacency matrix, assuming that the graph
+    is described by the configuration model.
+
+    More specifically, the element B_ij of B is defined as
+
+    .. math::
+        A_{ij} - {k_i k_j \over 2 m}
+
+    where k_i is the degree of node i, and where m is the number of edges
+    in the graph. When weight is set to a name of an attribute edge, Aij, k_i,
+    k_j and m are computed using its value.
+
+    Parameters
+    ----------
+    G : Graph
+       A NetworkX graph
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by G.nodes().
+
+    weight : string or None, optional (default=None)
+       The edge attribute that holds the numerical value used for
+       the edge weight.  If None then all edge weights are 1.
+
+    Returns
+    -------
+    B : Numpy array
+      The modularity matrix of G.
+
+    Examples
+    --------
+    >>> k = [3, 2, 2, 1, 0]
+    >>> G = nx.havel_hakimi_graph(k)
+    >>> B = nx.modularity_matrix(G)
+
+
+    See Also
+    --------
+    to_numpy_array
+    modularity_spectrum
+    adjacency_matrix
+    directed_modularity_matrix
+
+    References
+    ----------
+    .. [1] M. E. J. Newman, "Modularity and community structure in networks",
+           Proc. Natl. Acad. Sci. USA, vol. 103, pp. 8577-8582, 2006.
+    """
+    import numpy as np
+
+    if nodelist is None:
+        nodelist = list(G)
+    A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format="csr")
+    k = A.sum(axis=1)
+    m = k.sum() * 0.5
+    # Expected adjacency matrix
+    X = np.outer(k, k) / (2 * m)
+
+    return A - X
+
+
+@not_implemented_for("undirected")
+@not_implemented_for("multigraph")
+@nx._dispatchable(edge_attrs="weight")
+def directed_modularity_matrix(G, nodelist=None, weight=None):
+    """Returns the directed modularity matrix of G.
+
+    The modularity matrix is the matrix B = A - <A>, where A is the adjacency
+    matrix and <A> is the expected adjacency matrix, assuming that the graph
+    is described by the configuration model.
+
+    More specifically, the element B_ij of B is defined as
+
+    .. math::
+        B_{ij} = A_{ij} - k_i^{out} k_j^{in} / m
+
+    where :math:`k_i^{in}` is the in degree of node i, and :math:`k_j^{out}` is the out degree
+    of node j, with m the number of edges in the graph. When weight is set
+    to a name of an attribute edge, Aij, k_i, k_j and m are computed using
+    its value.
+
+    Parameters
+    ----------
+    G : DiGraph
+       A NetworkX DiGraph
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in nodelist.
+       If nodelist is None, then the ordering is produced by G.nodes().
+
+    weight : string or None, optional (default=None)
+       The edge attribute that holds the numerical value used for
+       the edge weight.  If None then all edge weights are 1.
+
+    Returns
+    -------
+    B : Numpy array
+      The modularity matrix of G.
+
+    Examples
+    --------
+    >>> G = nx.DiGraph()
+    >>> G.add_edges_from(
+    ...     (
+    ...         (1, 2),
+    ...         (1, 3),
+    ...         (3, 1),
+    ...         (3, 2),
+    ...         (3, 5),
+    ...         (4, 5),
+    ...         (4, 6),
+    ...         (5, 4),
+    ...         (5, 6),
+    ...         (6, 4),
+    ...     )
+    ... )
+    >>> B = nx.directed_modularity_matrix(G)
+
+
+    Notes
+    -----
+    NetworkX defines the element A_ij of the adjacency matrix as 1 if there
+    is a link going from node i to node j. Leicht and Newman use the opposite
+    definition. This explains the different expression for B_ij.
+
+    See Also
+    --------
+    to_numpy_array
+    modularity_spectrum
+    adjacency_matrix
+    modularity_matrix
+
+    References
+    ----------
+    .. [1] E. A. Leicht, M. E. J. Newman,
+        "Community structure in directed networks",
+        Phys. Rev Lett., vol. 100, no. 11, p. 118703, 2008.
+    """
+    import numpy as np
+
+    if nodelist is None:
+        nodelist = list(G)
+    A = nx.to_scipy_sparse_array(G, nodelist=nodelist, weight=weight, format="csr")
+    k_in = A.sum(axis=0)
+    k_out = A.sum(axis=1)
+    m = k_in.sum()
+    # Expected adjacency matrix
+    X = np.outer(k_out, k_in) / m
+
+    return A - X
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/spectrum.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/spectrum.py
new file mode 100644
index 0000000000000000000000000000000000000000..079b18550ca5b1478ec3f4b12ee40e5451627355
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/linalg/spectrum.py
@@ -0,0 +1,186 @@
+"""
+Eigenvalue spectrum of graphs.
+"""
+
+import networkx as nx
+
+__all__ = [
+    "laplacian_spectrum",
+    "adjacency_spectrum",
+    "modularity_spectrum",
+    "normalized_laplacian_spectrum",
+    "bethe_hessian_spectrum",
+]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def laplacian_spectrum(G, weight="weight"):
+    """Returns eigenvalues of the Laplacian of G
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    evals : NumPy array
+      Eigenvalues
+
+    Notes
+    -----
+    For MultiGraph/MultiDiGraph, the edges weights are summed.
+    See :func:`~networkx.convert_matrix.to_numpy_array` for other options.
+
+    See Also
+    --------
+    laplacian_matrix
+
+    Examples
+    --------
+    The multiplicity of 0 as an eigenvalue of the laplacian matrix is equal
+    to the number of connected components of G.
+
+    >>> G = nx.Graph()  # Create a graph with 5 nodes and 3 connected components
+    >>> G.add_nodes_from(range(5))
+    >>> G.add_edges_from([(0, 2), (3, 4)])
+    >>> nx.laplacian_spectrum(G)
+    array([0., 0., 0., 2., 2.])
+
+    """
+    import scipy as sp
+
+    return sp.linalg.eigvalsh(nx.laplacian_matrix(G, weight=weight).todense())
+
+
+@nx._dispatchable(edge_attrs="weight")
+def normalized_laplacian_spectrum(G, weight="weight"):
+    """Return eigenvalues of the normalized Laplacian of G
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    evals : NumPy array
+      Eigenvalues
+
+    Notes
+    -----
+    For MultiGraph/MultiDiGraph, the edges weights are summed.
+    See to_numpy_array for other options.
+
+    See Also
+    --------
+    normalized_laplacian_matrix
+    """
+    import scipy as sp
+
+    return sp.linalg.eigvalsh(
+        nx.normalized_laplacian_matrix(G, weight=weight).todense()
+    )
+
+
+@nx._dispatchable(edge_attrs="weight")
+def adjacency_spectrum(G, weight="weight"):
+    """Returns eigenvalues of the adjacency matrix of G.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    weight : string or None, optional (default='weight')
+       The edge data key used to compute each value in the matrix.
+       If None, then each edge has weight 1.
+
+    Returns
+    -------
+    evals : NumPy array
+      Eigenvalues
+
+    Notes
+    -----
+    For MultiGraph/MultiDiGraph, the edges weights are summed.
+    See to_numpy_array for other options.
+
+    See Also
+    --------
+    adjacency_matrix
+    """
+    import scipy as sp
+
+    return sp.linalg.eigvals(nx.adjacency_matrix(G, weight=weight).todense())
+
+
+@nx._dispatchable
+def modularity_spectrum(G):
+    """Returns eigenvalues of the modularity matrix of G.
+
+    Parameters
+    ----------
+    G : Graph
+       A NetworkX Graph or DiGraph
+
+    Returns
+    -------
+    evals : NumPy array
+      Eigenvalues
+
+    See Also
+    --------
+    modularity_matrix
+
+    References
+    ----------
+    .. [1] M. E. J. Newman, "Modularity and community structure in networks",
+       Proc. Natl. Acad. Sci. USA, vol. 103, pp. 8577-8582, 2006.
+    """
+    import scipy as sp
+
+    if G.is_directed():
+        return sp.linalg.eigvals(nx.directed_modularity_matrix(G))
+    else:
+        return sp.linalg.eigvals(nx.modularity_matrix(G))
+
+
+@nx._dispatchable
+def bethe_hessian_spectrum(G, r=None):
+    """Returns eigenvalues of the Bethe Hessian matrix of G.
+
+    Parameters
+    ----------
+    G : Graph
+       A NetworkX Graph or DiGraph
+
+    r : float
+       Regularizer parameter
+
+    Returns
+    -------
+    evals : NumPy array
+      Eigenvalues
+
+    See Also
+    --------
+    bethe_hessian_matrix
+
+    References
+    ----------
+    .. [1] A. Saade, F. Krzakala and L. Zdeborová
+       "Spectral clustering of graphs with the bethe hessian",
+       Advances in Neural Information Processing Systems. 2014.
+    """
+    import scipy as sp
+
+    return sp.linalg.eigvalsh(nx.bethe_hessian_matrix(G, r).todense())
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/__init__.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a805c50a7b18bc818f7bb0a8978ee1e7e90277b5
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/__init__.py
@@ -0,0 +1,17 @@
+"""
+A package for reading and writing graphs in various formats.
+
+"""
+
+from networkx.readwrite.adjlist import *
+from networkx.readwrite.multiline_adjlist import *
+from networkx.readwrite.edgelist import *
+from networkx.readwrite.pajek import *
+from networkx.readwrite.leda import *
+from networkx.readwrite.sparse6 import *
+from networkx.readwrite.graph6 import *
+from networkx.readwrite.gml import *
+from networkx.readwrite.graphml import *
+from networkx.readwrite.gexf import *
+from networkx.readwrite.json_graph import *
+from networkx.readwrite.text import *
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/adjlist.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/adjlist.py
new file mode 100644
index 0000000000000000000000000000000000000000..d75993905a1346abb119119c1b707e9bfe2823da
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/adjlist.py
@@ -0,0 +1,310 @@
+"""
+**************
+Adjacency List
+**************
+Read and write NetworkX graphs as adjacency lists.
+
+Adjacency list format is useful for graphs without data associated
+with nodes or edges and for nodes that can be meaningfully represented
+as strings.
+
+Format
+------
+The adjacency list format consists of lines with node labels.  The
+first label in a line is the source node.  Further labels in the line
+are considered target nodes and are added to the graph along with an edge
+between the source node and target node.
+
+The graph with edges a-b, a-c, d-e can be represented as the following
+adjacency list (anything following the # in a line is a comment)::
+
+     a b c # source target target
+     d e
+"""
+
+__all__ = ["generate_adjlist", "write_adjlist", "parse_adjlist", "read_adjlist"]
+
+import networkx as nx
+from networkx.utils import open_file
+
+
+def generate_adjlist(G, delimiter=" "):
+    """Generate a single line of the graph G in adjacency list format.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    delimiter : string, optional
+       Separator for node labels
+
+    Returns
+    -------
+    lines : string
+        Lines of data in adjlist format.
+
+    Examples
+    --------
+    >>> G = nx.lollipop_graph(4, 3)
+    >>> for line in nx.generate_adjlist(G):
+    ...     print(line)
+    0 1 2 3
+    1 2 3
+    2 3
+    3 4
+    4 5
+    5 6
+    6
+
+    See Also
+    --------
+    write_adjlist, read_adjlist
+
+    Notes
+    -----
+    The default `delimiter=" "` will result in unexpected results if node names contain
+    whitespace characters. To avoid this problem, specify an alternate delimiter when spaces are
+    valid in node names.
+
+    NB: This option is not available for data that isn't user-generated.
+
+    """
+    directed = G.is_directed()
+    seen = set()
+    for s, nbrs in G.adjacency():
+        line = str(s) + delimiter
+        for t, data in nbrs.items():
+            if not directed and t in seen:
+                continue
+            if G.is_multigraph():
+                for d in data.values():
+                    line += str(t) + delimiter
+            else:
+                line += str(t) + delimiter
+        if not directed:
+            seen.add(s)
+        yield line[: -len(delimiter)]
+
+
+@open_file(1, mode="wb")
+def write_adjlist(G, path, comments="#", delimiter=" ", encoding="utf-8"):
+    """Write graph G in single-line adjacency-list format to path.
+
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    path : string or file
+       Filename or file handle for data output.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels
+
+    encoding : string, optional
+       Text encoding.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_adjlist(G, "path4.adjlist")
+
+    The path can be a filehandle or a string with the name of the file. If a
+    filehandle is provided, it has to be opened in 'wb' mode.
+
+    >>> fh = open("path4.adjlist2", "wb")
+    >>> nx.write_adjlist(G, fh)
+
+    Notes
+    -----
+    The default `delimiter=" "` will result in unexpected results if node names contain
+    whitespace characters. To avoid this problem, specify an alternate delimiter when spaces are
+    valid in node names.
+    NB: This option is not available for data that isn't user-generated.
+
+    This format does not store graph, node, or edge data.
+
+    See Also
+    --------
+    read_adjlist, generate_adjlist
+    """
+    import sys
+    import time
+
+    pargs = comments + " ".join(sys.argv) + "\n"
+    header = (
+        pargs
+        + comments
+        + f" GMT {time.asctime(time.gmtime())}\n"
+        + comments
+        + f" {G.name}\n"
+    )
+    path.write(header.encode(encoding))
+
+    for line in generate_adjlist(G, delimiter):
+        line += "\n"
+        path.write(line.encode(encoding))
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_adjlist(
+    lines, comments="#", delimiter=None, create_using=None, nodetype=None
+):
+    """Parse lines of a graph adjacency list representation.
+
+    Parameters
+    ----------
+    lines : list or iterator of strings
+        Input data in adjlist format
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    nodetype : Python type, optional
+       Convert nodes to this type.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels.  The default is whitespace.
+
+    Returns
+    -------
+    G: NetworkX graph
+        The graph corresponding to the lines in adjacency list format.
+
+    Examples
+    --------
+    >>> lines = ["1 2 5", "2 3 4", "3 5", "4", "5"]
+    >>> G = nx.parse_adjlist(lines, nodetype=int)
+    >>> nodes = [1, 2, 3, 4, 5]
+    >>> all(node in G for node in nodes)
+    True
+    >>> edges = [(1, 2), (1, 5), (2, 3), (2, 4), (3, 5)]
+    >>> all((u, v) in G.edges() or (v, u) in G.edges() for (u, v) in edges)
+    True
+
+    See Also
+    --------
+    read_adjlist
+
+    """
+    G = nx.empty_graph(0, create_using)
+    for line in lines:
+        p = line.find(comments)
+        if p >= 0:
+            line = line[:p]
+        if not len(line):
+            continue
+        vlist = line.rstrip("\n").split(delimiter)
+        u = vlist.pop(0)
+        # convert types
+        if nodetype is not None:
+            try:
+                u = nodetype(u)
+            except BaseException as err:
+                raise TypeError(
+                    f"Failed to convert node ({u}) to type {nodetype}"
+                ) from err
+        G.add_node(u)
+        if nodetype is not None:
+            try:
+                vlist = list(map(nodetype, vlist))
+            except BaseException as err:
+                raise TypeError(
+                    f"Failed to convert nodes ({','.join(vlist)}) to type {nodetype}"
+                ) from err
+        G.add_edges_from([(u, v) for v in vlist])
+    return G
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_adjlist(
+    path,
+    comments="#",
+    delimiter=None,
+    create_using=None,
+    nodetype=None,
+    encoding="utf-8",
+):
+    """Read graph in adjacency list format from path.
+
+    Parameters
+    ----------
+    path : string or file
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    nodetype : Python type, optional
+       Convert nodes to this type.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels.  The default is whitespace.
+
+    Returns
+    -------
+    G: NetworkX graph
+        The graph corresponding to the lines in adjacency list format.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_adjlist(G, "test.adjlist")
+    >>> G = nx.read_adjlist("test.adjlist")
+
+    The path can be a filehandle or a string with the name of the file. If a
+    filehandle is provided, it has to be opened in 'rb' mode.
+
+    >>> fh = open("test.adjlist", "rb")
+    >>> G = nx.read_adjlist(fh)
+
+    Filenames ending in .gz or .bz2 will be compressed.
+
+    >>> nx.write_adjlist(G, "test.adjlist.gz")
+    >>> G = nx.read_adjlist("test.adjlist.gz")
+
+    The optional nodetype is a function to convert node strings to nodetype.
+
+    For example
+
+    >>> G = nx.read_adjlist("test.adjlist", nodetype=int)
+
+    will attempt to convert all nodes to integer type.
+
+    Since nodes must be hashable, the function nodetype must return hashable
+    types (e.g. int, float, str, frozenset - or tuples of those, etc.)
+
+    The optional create_using parameter indicates the type of NetworkX graph
+    created.  The default is `nx.Graph`, an undirected graph.
+    To read the data as a directed graph use
+
+    >>> G = nx.read_adjlist("test.adjlist", create_using=nx.DiGraph)
+
+    Notes
+    -----
+    This format does not store graph or node data.
+
+    See Also
+    --------
+    write_adjlist
+    """
+    lines = (line.decode(encoding) for line in path)
+    return parse_adjlist(
+        lines,
+        comments=comments,
+        delimiter=delimiter,
+        create_using=create_using,
+        nodetype=nodetype,
+    )
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/edgelist.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/edgelist.py
new file mode 100644
index 0000000000000000000000000000000000000000..afdb175e8cf4dfb5d8ddb81b136259d2471ad13e
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/edgelist.py
@@ -0,0 +1,489 @@
+"""
+**********
+Edge Lists
+**********
+Read and write NetworkX graphs as edge lists.
+
+The multi-line adjacency list format is useful for graphs with nodes
+that can be meaningfully represented as strings.  With the edgelist
+format simple edge data can be stored but node or graph data is not.
+There is no way of representing isolated nodes unless the node has a
+self-loop edge.
+
+Format
+------
+You can read or write three formats of edge lists with these functions.
+
+Node pairs with no data::
+
+ 1 2
+
+Python dictionary as data::
+
+ 1 2 {'weight':7, 'color':'green'}
+
+Arbitrary data::
+
+ 1 2 7 green
+"""
+
+__all__ = [
+    "generate_edgelist",
+    "write_edgelist",
+    "parse_edgelist",
+    "read_edgelist",
+    "read_weighted_edgelist",
+    "write_weighted_edgelist",
+]
+
+import networkx as nx
+from networkx.utils import open_file
+
+
+def generate_edgelist(G, delimiter=" ", data=True):
+    """Generate a single line of the graph G in edge list format.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    delimiter : string, optional
+       Separator for node labels
+
+    data : bool or list of keys
+       If False generate no edge data.  If True use a dictionary
+       representation of edge data.  If a list of keys use a list of data
+       values corresponding to the keys.
+
+    Returns
+    -------
+    lines : string
+        Lines of data in adjlist format.
+
+    Examples
+    --------
+    >>> G = nx.lollipop_graph(4, 3)
+    >>> G[1][2]["weight"] = 3
+    >>> G[3][4]["capacity"] = 12
+    >>> for line in nx.generate_edgelist(G, data=False):
+    ...     print(line)
+    0 1
+    0 2
+    0 3
+    1 2
+    1 3
+    2 3
+    3 4
+    4 5
+    5 6
+
+    >>> for line in nx.generate_edgelist(G):
+    ...     print(line)
+    0 1 {}
+    0 2 {}
+    0 3 {}
+    1 2 {'weight': 3}
+    1 3 {}
+    2 3 {}
+    3 4 {'capacity': 12}
+    4 5 {}
+    5 6 {}
+
+    >>> for line in nx.generate_edgelist(G, data=["weight"]):
+    ...     print(line)
+    0 1
+    0 2
+    0 3
+    1 2 3
+    1 3
+    2 3
+    3 4
+    4 5
+    5 6
+
+    See Also
+    --------
+    write_adjlist, read_adjlist
+    """
+    if data is True:
+        for u, v, d in G.edges(data=True):
+            e = u, v, dict(d)
+            yield delimiter.join(map(str, e))
+    elif data is False:
+        for u, v in G.edges(data=False):
+            e = u, v
+            yield delimiter.join(map(str, e))
+    else:
+        for u, v, d in G.edges(data=True):
+            e = [u, v]
+            try:
+                e.extend(d[k] for k in data)
+            except KeyError:
+                pass  # missing data for this edge, should warn?
+            yield delimiter.join(map(str, e))
+
+
+@open_file(1, mode="wb")
+def write_edgelist(G, path, comments="#", delimiter=" ", data=True, encoding="utf-8"):
+    """Write graph as a list of edges.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+    path : file or string
+       File or filename to write. If a file is provided, it must be
+       opened in 'wb' mode. Filenames ending in .gz or .bz2 will be compressed.
+    comments : string, optional
+       The character used to indicate the start of a comment
+    delimiter : string, optional
+       The string used to separate values.  The default is whitespace.
+    data : bool or list, optional
+       If False write no edge data.
+       If True write a string representation of the edge data dictionary..
+       If a list (or other iterable) is provided, write the  keys specified
+       in the list.
+    encoding: string, optional
+       Specify which encoding to use when writing file.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_edgelist(G, "test.edgelist")
+    >>> G = nx.path_graph(4)
+    >>> fh = open("test.edgelist", "wb")
+    >>> nx.write_edgelist(G, fh)
+    >>> nx.write_edgelist(G, "test.edgelist.gz")
+    >>> nx.write_edgelist(G, "test.edgelist_nodata.gz", data=False)
+
+    >>> G = nx.Graph()
+    >>> G.add_edge(1, 2, weight=7, color="red")
+    >>> nx.write_edgelist(G, "test.edgelist_bigger_nodata", data=False)
+    >>> nx.write_edgelist(G, "test.edgelist_color", data=["color"])
+    >>> nx.write_edgelist(G, "test.edgelist_color_weight", data=["color", "weight"])
+
+    See Also
+    --------
+    read_edgelist
+    write_weighted_edgelist
+    """
+
+    for line in generate_edgelist(G, delimiter, data):
+        line += "\n"
+        path.write(line.encode(encoding))
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_edgelist(
+    lines, comments="#", delimiter=None, create_using=None, nodetype=None, data=True
+):
+    """Parse lines of an edge list representation of a graph.
+
+    Parameters
+    ----------
+    lines : list or iterator of strings
+        Input data in edgelist format
+    comments : string, optional
+       Marker for comment lines. Default is `'#'`. To specify that no character
+       should be treated as a comment, use ``comments=None``.
+    delimiter : string, optional
+       Separator for node labels. Default is `None`, meaning any whitespace.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+    nodetype : Python type, optional
+       Convert nodes to this type. Default is `None`, meaning no conversion is
+       performed.
+    data : bool or list of (label,type) tuples
+       If `False` generate no edge data or if `True` use a dictionary
+       representation of edge data or a list tuples specifying dictionary
+       key names and types for edge data.
+
+    Returns
+    -------
+    G: NetworkX Graph
+        The graph corresponding to lines
+
+    Examples
+    --------
+    Edgelist with no data:
+
+    >>> lines = ["1 2", "2 3", "3 4"]
+    >>> G = nx.parse_edgelist(lines, nodetype=int)
+    >>> list(G)
+    [1, 2, 3, 4]
+    >>> list(G.edges())
+    [(1, 2), (2, 3), (3, 4)]
+
+    Edgelist with data in Python dictionary representation:
+
+    >>> lines = ["1 2 {'weight': 3}", "2 3 {'weight': 27}", "3 4 {'weight': 3.0}"]
+    >>> G = nx.parse_edgelist(lines, nodetype=int)
+    >>> list(G)
+    [1, 2, 3, 4]
+    >>> list(G.edges(data=True))
+    [(1, 2, {'weight': 3}), (2, 3, {'weight': 27}), (3, 4, {'weight': 3.0})]
+
+    Edgelist with data in a list:
+
+    >>> lines = ["1 2 3", "2 3 27", "3 4 3.0"]
+    >>> G = nx.parse_edgelist(lines, nodetype=int, data=(("weight", float),))
+    >>> list(G)
+    [1, 2, 3, 4]
+    >>> list(G.edges(data=True))
+    [(1, 2, {'weight': 3.0}), (2, 3, {'weight': 27.0}), (3, 4, {'weight': 3.0})]
+
+    See Also
+    --------
+    read_weighted_edgelist
+    """
+    from ast import literal_eval
+
+    G = nx.empty_graph(0, create_using)
+    for line in lines:
+        if comments is not None:
+            p = line.find(comments)
+            if p >= 0:
+                line = line[:p]
+            if not line:
+                continue
+        # split line, should have 2 or more
+        s = line.rstrip("\n").split(delimiter)
+        if len(s) < 2:
+            continue
+        u = s.pop(0)
+        v = s.pop(0)
+        d = s
+        if nodetype is not None:
+            try:
+                u = nodetype(u)
+                v = nodetype(v)
+            except Exception as err:
+                raise TypeError(
+                    f"Failed to convert nodes {u},{v} to type {nodetype}."
+                ) from err
+
+        if len(d) == 0 or data is False:
+            # no data or data type specified
+            edgedata = {}
+        elif data is True:
+            # no edge types specified
+            try:  # try to evaluate as dictionary
+                if delimiter == ",":
+                    edgedata_str = ",".join(d)
+                else:
+                    edgedata_str = " ".join(d)
+                edgedata = dict(literal_eval(edgedata_str.strip()))
+            except Exception as err:
+                raise TypeError(
+                    f"Failed to convert edge data ({d}) to dictionary."
+                ) from err
+        else:
+            # convert edge data to dictionary with specified keys and type
+            if len(d) != len(data):
+                raise IndexError(
+                    f"Edge data {d} and data_keys {data} are not the same length"
+                )
+            edgedata = {}
+            for (edge_key, edge_type), edge_value in zip(data, d):
+                try:
+                    edge_value = edge_type(edge_value)
+                except Exception as err:
+                    raise TypeError(
+                        f"Failed to convert {edge_key} data {edge_value} "
+                        f"to type {edge_type}."
+                    ) from err
+                edgedata.update({edge_key: edge_value})
+        G.add_edge(u, v, **edgedata)
+    return G
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_edgelist(
+    path,
+    comments="#",
+    delimiter=None,
+    create_using=None,
+    nodetype=None,
+    data=True,
+    edgetype=None,
+    encoding="utf-8",
+):
+    """Read a graph from a list of edges.
+
+    Parameters
+    ----------
+    path : file or string
+       File or filename to read. If a file is provided, it must be
+       opened in 'rb' mode.
+       Filenames ending in .gz or .bz2 will be decompressed.
+    comments : string, optional
+       The character used to indicate the start of a comment. To specify that
+       no character should be treated as a comment, use ``comments=None``.
+    delimiter : string, optional
+       The string used to separate values.  The default is whitespace.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+    nodetype : int, float, str, Python type, optional
+       Convert node data from strings to specified type
+    data : bool or list of (label,type) tuples
+       Tuples specifying dictionary key names and types for edge data
+    edgetype : int, float, str, Python type, optional OBSOLETE
+       Convert edge data from strings to specified type and use as 'weight'
+    encoding: string, optional
+       Specify which encoding to use when reading file.
+
+    Returns
+    -------
+    G : graph
+       A networkx Graph or other type specified with create_using
+
+    Examples
+    --------
+    >>> nx.write_edgelist(nx.path_graph(4), "test.edgelist_P4")
+    >>> G = nx.read_edgelist("test.edgelist_P4")
+
+    >>> fh = open("test.edgelist_P4", "rb")
+    >>> G = nx.read_edgelist(fh)
+    >>> fh.close()
+
+    >>> G = nx.read_edgelist("test.edgelist_P4", nodetype=int)
+    >>> G = nx.read_edgelist("test.edgelist_P4", create_using=nx.DiGraph)
+
+    Edgelist with data in a list:
+
+    >>> textline = "1 2 3"
+    >>> fh = open("test.textline", "w")
+    >>> d = fh.write(textline)
+    >>> fh.close()
+    >>> G = nx.read_edgelist("test.textline", nodetype=int, data=(("weight", float),))
+    >>> list(G)
+    [1, 2]
+    >>> list(G.edges(data=True))
+    [(1, 2, {'weight': 3.0})]
+
+    See parse_edgelist() for more examples of formatting.
+
+    See Also
+    --------
+    parse_edgelist
+    write_edgelist
+
+    Notes
+    -----
+    Since nodes must be hashable, the function nodetype must return hashable
+    types (e.g. int, float, str, frozenset - or tuples of those, etc.)
+    """
+    lines = (line if isinstance(line, str) else line.decode(encoding) for line in path)
+    return parse_edgelist(
+        lines,
+        comments=comments,
+        delimiter=delimiter,
+        create_using=create_using,
+        nodetype=nodetype,
+        data=data,
+    )
+
+
+def write_weighted_edgelist(G, path, comments="#", delimiter=" ", encoding="utf-8"):
+    """Write graph G as a list of edges with numeric weights.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+    path : file or string
+       File or filename to write. If a file is provided, it must be
+       opened in 'wb' mode.
+       Filenames ending in .gz or .bz2 will be compressed.
+    comments : string, optional
+       The character used to indicate the start of a comment
+    delimiter : string, optional
+       The string used to separate values.  The default is whitespace.
+    encoding: string, optional
+       Specify which encoding to use when writing file.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_edge(1, 2, weight=7)
+    >>> nx.write_weighted_edgelist(G, "test.weighted.edgelist")
+
+    See Also
+    --------
+    read_edgelist
+    write_edgelist
+    read_weighted_edgelist
+    """
+    write_edgelist(
+        G,
+        path,
+        comments=comments,
+        delimiter=delimiter,
+        data=("weight",),
+        encoding=encoding,
+    )
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_weighted_edgelist(
+    path,
+    comments="#",
+    delimiter=None,
+    create_using=None,
+    nodetype=None,
+    encoding="utf-8",
+):
+    """Read a graph as list of edges with numeric weights.
+
+    Parameters
+    ----------
+    path : file or string
+       File or filename to read. If a file is provided, it must be
+       opened in 'rb' mode.
+       Filenames ending in .gz or .bz2 will be decompressed.
+    comments : string, optional
+       The character used to indicate the start of a comment.
+    delimiter : string, optional
+       The string used to separate values.  The default is whitespace.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+    nodetype : int, float, str, Python type, optional
+       Convert node data from strings to specified type
+    encoding: string, optional
+       Specify which encoding to use when reading file.
+
+    Returns
+    -------
+    G : graph
+       A networkx Graph or other type specified with create_using
+
+    Notes
+    -----
+    Since nodes must be hashable, the function nodetype must return hashable
+    types (e.g. int, float, str, frozenset - or tuples of those, etc.)
+
+    Example edgelist file format.
+
+    With numeric edge data::
+
+     # read with
+     # >>> G=nx.read_weighted_edgelist(fh)
+     # source target data
+     a b 1
+     a c 3.14159
+     d e 42
+
+    See Also
+    --------
+    write_weighted_edgelist
+    """
+    return read_edgelist(
+        path,
+        comments=comments,
+        delimiter=delimiter,
+        create_using=create_using,
+        nodetype=nodetype,
+        data=(("weight", float),),
+        encoding=encoding,
+    )
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/gexf.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/gexf.py
new file mode 100644
index 0000000000000000000000000000000000000000..da726e319896fc40c8b86b3a636a134483f31ebe
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/gexf.py
@@ -0,0 +1,1064 @@
+"""Read and write graphs in GEXF format.
+
+.. warning::
+    This parser uses the standard xml library present in Python, which is
+    insecure - see :external+python:mod:`xml` for additional information.
+    Only parse GEFX files you trust.
+
+GEXF (Graph Exchange XML Format) is a language for describing complex
+network structures, their associated data and dynamics.
+
+This implementation does not support mixed graphs (directed and
+undirected edges together).
+
+Format
+------
+GEXF is an XML format.  See http://gexf.net/schema.html for the
+specification and http://gexf.net/basic.html for examples.
+"""
+
+import itertools
+import time
+from xml.etree.ElementTree import (
+    Element,
+    ElementTree,
+    SubElement,
+    register_namespace,
+    tostring,
+)
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = ["write_gexf", "read_gexf", "relabel_gexf_graph", "generate_gexf"]
+
+
+@open_file(1, mode="wb")
+def write_gexf(G, path, encoding="utf-8", prettyprint=True, version="1.2draft"):
+    """Write G in GEXF format to path.
+
+    "GEXF (Graph Exchange XML Format) is a language for describing
+    complex networks structures, their associated data and dynamics" [1]_.
+
+    Node attributes are checked according to the version of the GEXF
+    schemas used for parameters which are not user defined,
+    e.g. visualization 'viz' [2]_. See example for usage.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+    path : file or string
+       File or file name to write.
+       File names ending in .gz or .bz2 will be compressed.
+    encoding : string (optional, default: 'utf-8')
+       Encoding for text data.
+    prettyprint : bool (optional, default: True)
+       If True use line breaks and indenting in output XML.
+    version: string (optional, default: '1.2draft')
+       The version of GEXF to be used for nodes attributes checking
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_gexf(G, "test.gexf")
+
+    # visualization data
+    >>> G.nodes[0]["viz"] = {"size": 54}
+    >>> G.nodes[0]["viz"]["position"] = {"x": 0, "y": 1}
+    >>> G.nodes[0]["viz"]["color"] = {"r": 0, "g": 0, "b": 256}
+
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed and undirected
+    edges together).
+
+    The node id attribute is set to be the string of the node label.
+    If you want to specify an id use set it as node data, e.g.
+    node['a']['id']=1 to set the id of node 'a' to 1.
+
+    References
+    ----------
+    .. [1] GEXF File Format, http://gexf.net/
+    .. [2] GEXF schema, http://gexf.net/schema.html
+    """
+    writer = GEXFWriter(encoding=encoding, prettyprint=prettyprint, version=version)
+    writer.add_graph(G)
+    writer.write(path)
+
+
+def generate_gexf(G, encoding="utf-8", prettyprint=True, version="1.2draft"):
+    """Generate lines of GEXF format representation of G.
+
+    "GEXF (Graph Exchange XML Format) is a language for describing
+    complex networks structures, their associated data and dynamics" [1]_.
+
+    Parameters
+    ----------
+    G : graph
+    A NetworkX graph
+    encoding : string (optional, default: 'utf-8')
+    Encoding for text data.
+    prettyprint : bool (optional, default: True)
+    If True use line breaks and indenting in output XML.
+    version : string (default: 1.2draft)
+    Version of GEFX File Format (see http://gexf.net/schema.html)
+    Supported values: "1.1draft", "1.2draft"
+
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> linefeed = chr(10)  # linefeed=\n
+    >>> s = linefeed.join(nx.generate_gexf(G))
+    >>> for line in nx.generate_gexf(G):  # doctest: +SKIP
+    ...     print(line)
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed and undirected
+    edges together).
+
+    The node id attribute is set to be the string of the node label.
+    If you want to specify an id use set it as node data, e.g.
+    node['a']['id']=1 to set the id of node 'a' to 1.
+
+    References
+    ----------
+    .. [1] GEXF File Format, https://gephi.org/gexf/format/
+    """
+    writer = GEXFWriter(encoding=encoding, prettyprint=prettyprint, version=version)
+    writer.add_graph(G)
+    yield from str(writer).splitlines()
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_gexf(path, node_type=None, relabel=False, version="1.2draft"):
+    """Read graph in GEXF format from path.
+
+    "GEXF (Graph Exchange XML Format) is a language for describing
+    complex networks structures, their associated data and dynamics" [1]_.
+
+    Parameters
+    ----------
+    path : file or string
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+    node_type: Python type (default: None)
+       Convert node ids to this type if not None.
+    relabel : bool (default: False)
+       If True relabel the nodes to use the GEXF node "label" attribute
+       instead of the node "id" attribute as the NetworkX node label.
+    version : string (default: 1.2draft)
+    Version of GEFX File Format (see http://gexf.net/schema.html)
+       Supported values: "1.1draft", "1.2draft"
+
+    Returns
+    -------
+    graph: NetworkX graph
+        If no parallel edges are found a Graph or DiGraph is returned.
+        Otherwise a MultiGraph or MultiDiGraph is returned.
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed and undirected
+    edges together).
+
+    References
+    ----------
+    .. [1] GEXF File Format, http://gexf.net/
+    """
+    reader = GEXFReader(node_type=node_type, version=version)
+    if relabel:
+        G = relabel_gexf_graph(reader(path))
+    else:
+        G = reader(path)
+    return G
+
+
+class GEXF:
+    versions = {
+        "1.1draft": {
+            "NS_GEXF": "http://www.gexf.net/1.1draft",
+            "NS_VIZ": "http://www.gexf.net/1.1draft/viz",
+            "NS_XSI": "http://www.w3.org/2001/XMLSchema-instance",
+            "SCHEMALOCATION": " ".join(
+                [
+                    "http://www.gexf.net/1.1draft",
+                    "http://www.gexf.net/1.1draft/gexf.xsd",
+                ]
+            ),
+            "VERSION": "1.1",
+        },
+        "1.2draft": {
+            "NS_GEXF": "http://www.gexf.net/1.2draft",
+            "NS_VIZ": "http://www.gexf.net/1.2draft/viz",
+            "NS_XSI": "http://www.w3.org/2001/XMLSchema-instance",
+            "SCHEMALOCATION": " ".join(
+                [
+                    "http://www.gexf.net/1.2draft",
+                    "http://www.gexf.net/1.2draft/gexf.xsd",
+                ]
+            ),
+            "VERSION": "1.2",
+        },
+    }
+
+    def construct_types(self):
+        types = [
+            (int, "integer"),
+            (float, "float"),
+            (float, "double"),
+            (bool, "boolean"),
+            (list, "string"),
+            (dict, "string"),
+            (int, "long"),
+            (str, "liststring"),
+            (str, "anyURI"),
+            (str, "string"),
+        ]
+
+        # These additions to types allow writing numpy types
+        try:
+            import numpy as np
+        except ImportError:
+            pass
+        else:
+            # prepend so that python types are created upon read (last entry wins)
+            types = [
+                (np.float64, "float"),
+                (np.float32, "float"),
+                (np.float16, "float"),
+                (np.int_, "int"),
+                (np.int8, "int"),
+                (np.int16, "int"),
+                (np.int32, "int"),
+                (np.int64, "int"),
+                (np.uint8, "int"),
+                (np.uint16, "int"),
+                (np.uint32, "int"),
+                (np.uint64, "int"),
+                (np.int_, "int"),
+                (np.intc, "int"),
+                (np.intp, "int"),
+            ] + types
+
+        self.xml_type = dict(types)
+        self.python_type = dict(reversed(a) for a in types)
+
+    # http://www.w3.org/TR/xmlschema-2/#boolean
+    convert_bool = {
+        "true": True,
+        "false": False,
+        "True": True,
+        "False": False,
+        "0": False,
+        0: False,
+        "1": True,
+        1: True,
+    }
+
+    def set_version(self, version):
+        d = self.versions.get(version)
+        if d is None:
+            raise nx.NetworkXError(f"Unknown GEXF version {version}.")
+        self.NS_GEXF = d["NS_GEXF"]
+        self.NS_VIZ = d["NS_VIZ"]
+        self.NS_XSI = d["NS_XSI"]
+        self.SCHEMALOCATION = d["SCHEMALOCATION"]
+        self.VERSION = d["VERSION"]
+        self.version = version
+
+
+class GEXFWriter(GEXF):
+    # class for writing GEXF format files
+    # use write_gexf() function
+    def __init__(
+        self, graph=None, encoding="utf-8", prettyprint=True, version="1.2draft"
+    ):
+        self.construct_types()
+        self.prettyprint = prettyprint
+        self.encoding = encoding
+        self.set_version(version)
+        self.xml = Element(
+            "gexf",
+            {
+                "xmlns": self.NS_GEXF,
+                "xmlns:xsi": self.NS_XSI,
+                "xsi:schemaLocation": self.SCHEMALOCATION,
+                "version": self.VERSION,
+            },
+        )
+
+        # Make meta element a non-graph element
+        # Also add lastmodifieddate as attribute, not tag
+        meta_element = Element("meta")
+        subelement_text = f"NetworkX {nx.__version__}"
+        SubElement(meta_element, "creator").text = subelement_text
+        meta_element.set("lastmodifieddate", time.strftime("%Y-%m-%d"))
+        self.xml.append(meta_element)
+
+        register_namespace("viz", self.NS_VIZ)
+
+        # counters for edge and attribute identifiers
+        self.edge_id = itertools.count()
+        self.attr_id = itertools.count()
+        self.all_edge_ids = set()
+        # default attributes are stored in dictionaries
+        self.attr = {}
+        self.attr["node"] = {}
+        self.attr["edge"] = {}
+        self.attr["node"]["dynamic"] = {}
+        self.attr["node"]["static"] = {}
+        self.attr["edge"]["dynamic"] = {}
+        self.attr["edge"]["static"] = {}
+
+        if graph is not None:
+            self.add_graph(graph)
+
+    def __str__(self):
+        if self.prettyprint:
+            self.indent(self.xml)
+        s = tostring(self.xml).decode(self.encoding)
+        return s
+
+    def add_graph(self, G):
+        # first pass through G collecting edge ids
+        for u, v, dd in G.edges(data=True):
+            eid = dd.get("id")
+            if eid is not None:
+                self.all_edge_ids.add(str(eid))
+        # set graph attributes
+        if G.graph.get("mode") == "dynamic":
+            mode = "dynamic"
+        else:
+            mode = "static"
+        # Add a graph element to the XML
+        if G.is_directed():
+            default = "directed"
+        else:
+            default = "undirected"
+        name = G.graph.get("name", "")
+        graph_element = Element("graph", defaultedgetype=default, mode=mode, name=name)
+        self.graph_element = graph_element
+        self.add_nodes(G, graph_element)
+        self.add_edges(G, graph_element)
+        self.xml.append(graph_element)
+
+    def add_nodes(self, G, graph_element):
+        nodes_element = Element("nodes")
+        for node, data in G.nodes(data=True):
+            node_data = data.copy()
+            node_id = str(node_data.pop("id", node))
+            kw = {"id": node_id}
+            label = str(node_data.pop("label", node))
+            kw["label"] = label
+            try:
+                pid = node_data.pop("pid")
+                kw["pid"] = str(pid)
+            except KeyError:
+                pass
+            try:
+                start = node_data.pop("start")
+                kw["start"] = str(start)
+                self.alter_graph_mode_timeformat(start)
+            except KeyError:
+                pass
+            try:
+                end = node_data.pop("end")
+                kw["end"] = str(end)
+                self.alter_graph_mode_timeformat(end)
+            except KeyError:
+                pass
+            # add node element with attributes
+            node_element = Element("node", **kw)
+            # add node element and attr subelements
+            default = G.graph.get("node_default", {})
+            node_data = self.add_parents(node_element, node_data)
+            if self.VERSION == "1.1":
+                node_data = self.add_slices(node_element, node_data)
+            else:
+                node_data = self.add_spells(node_element, node_data)
+            node_data = self.add_viz(node_element, node_data)
+            node_data = self.add_attributes("node", node_element, node_data, default)
+            nodes_element.append(node_element)
+        graph_element.append(nodes_element)
+
+    def add_edges(self, G, graph_element):
+        def edge_key_data(G):
+            # helper function to unify multigraph and graph edge iterator
+            if G.is_multigraph():
+                for u, v, key, data in G.edges(data=True, keys=True):
+                    edge_data = data.copy()
+                    edge_data.update(key=key)
+                    edge_id = edge_data.pop("id", None)
+                    if edge_id is None:
+                        edge_id = next(self.edge_id)
+                        while str(edge_id) in self.all_edge_ids:
+                            edge_id = next(self.edge_id)
+                        self.all_edge_ids.add(str(edge_id))
+                    yield u, v, edge_id, edge_data
+            else:
+                for u, v, data in G.edges(data=True):
+                    edge_data = data.copy()
+                    edge_id = edge_data.pop("id", None)
+                    if edge_id is None:
+                        edge_id = next(self.edge_id)
+                        while str(edge_id) in self.all_edge_ids:
+                            edge_id = next(self.edge_id)
+                        self.all_edge_ids.add(str(edge_id))
+                    yield u, v, edge_id, edge_data
+
+        edges_element = Element("edges")
+        for u, v, key, edge_data in edge_key_data(G):
+            kw = {"id": str(key)}
+            try:
+                edge_label = edge_data.pop("label")
+                kw["label"] = str(edge_label)
+            except KeyError:
+                pass
+            try:
+                edge_weight = edge_data.pop("weight")
+                kw["weight"] = str(edge_weight)
+            except KeyError:
+                pass
+            try:
+                edge_type = edge_data.pop("type")
+                kw["type"] = str(edge_type)
+            except KeyError:
+                pass
+            try:
+                start = edge_data.pop("start")
+                kw["start"] = str(start)
+                self.alter_graph_mode_timeformat(start)
+            except KeyError:
+                pass
+            try:
+                end = edge_data.pop("end")
+                kw["end"] = str(end)
+                self.alter_graph_mode_timeformat(end)
+            except KeyError:
+                pass
+            source_id = str(G.nodes[u].get("id", u))
+            target_id = str(G.nodes[v].get("id", v))
+            edge_element = Element("edge", source=source_id, target=target_id, **kw)
+            default = G.graph.get("edge_default", {})
+            if self.VERSION == "1.1":
+                edge_data = self.add_slices(edge_element, edge_data)
+            else:
+                edge_data = self.add_spells(edge_element, edge_data)
+            edge_data = self.add_viz(edge_element, edge_data)
+            edge_data = self.add_attributes("edge", edge_element, edge_data, default)
+            edges_element.append(edge_element)
+        graph_element.append(edges_element)
+
+    def add_attributes(self, node_or_edge, xml_obj, data, default):
+        # Add attrvalues to node or edge
+        attvalues = Element("attvalues")
+        if len(data) == 0:
+            return data
+        mode = "static"
+        for k, v in data.items():
+            # rename generic multigraph key to avoid any name conflict
+            if k == "key":
+                k = "networkx_key"
+            val_type = type(v)
+            if val_type not in self.xml_type:
+                raise TypeError(f"attribute value type is not allowed: {val_type}")
+            if isinstance(v, list):
+                # dynamic data
+                for val, start, end in v:
+                    val_type = type(val)
+                    if start is not None or end is not None:
+                        mode = "dynamic"
+                        self.alter_graph_mode_timeformat(start)
+                        self.alter_graph_mode_timeformat(end)
+                        break
+                attr_id = self.get_attr_id(
+                    str(k), self.xml_type[val_type], node_or_edge, default, mode
+                )
+                for val, start, end in v:
+                    e = Element("attvalue")
+                    e.attrib["for"] = attr_id
+                    e.attrib["value"] = str(val)
+                    # Handle nan, inf, -inf differently
+                    if val_type is float:
+                        if e.attrib["value"] == "inf":
+                            e.attrib["value"] = "INF"
+                        elif e.attrib["value"] == "nan":
+                            e.attrib["value"] = "NaN"
+                        elif e.attrib["value"] == "-inf":
+                            e.attrib["value"] = "-INF"
+                    if start is not None:
+                        e.attrib["start"] = str(start)
+                    if end is not None:
+                        e.attrib["end"] = str(end)
+                    attvalues.append(e)
+            else:
+                # static data
+                mode = "static"
+                attr_id = self.get_attr_id(
+                    str(k), self.xml_type[val_type], node_or_edge, default, mode
+                )
+                e = Element("attvalue")
+                e.attrib["for"] = attr_id
+                if isinstance(v, bool):
+                    e.attrib["value"] = str(v).lower()
+                else:
+                    e.attrib["value"] = str(v)
+                    # Handle float nan, inf, -inf differently
+                    if val_type is float:
+                        if e.attrib["value"] == "inf":
+                            e.attrib["value"] = "INF"
+                        elif e.attrib["value"] == "nan":
+                            e.attrib["value"] = "NaN"
+                        elif e.attrib["value"] == "-inf":
+                            e.attrib["value"] = "-INF"
+                attvalues.append(e)
+        xml_obj.append(attvalues)
+        return data
+
+    def get_attr_id(self, title, attr_type, edge_or_node, default, mode):
+        # find the id of the attribute or generate a new id
+        try:
+            return self.attr[edge_or_node][mode][title]
+        except KeyError:
+            # generate new id
+            new_id = str(next(self.attr_id))
+            self.attr[edge_or_node][mode][title] = new_id
+            attr_kwargs = {"id": new_id, "title": title, "type": attr_type}
+            attribute = Element("attribute", **attr_kwargs)
+            # add subelement for data default value if present
+            default_title = default.get(title)
+            if default_title is not None:
+                default_element = Element("default")
+                default_element.text = str(default_title)
+                attribute.append(default_element)
+            # new insert it into the XML
+            attributes_element = None
+            for a in self.graph_element.findall("attributes"):
+                # find existing attributes element by class and mode
+                a_class = a.get("class")
+                a_mode = a.get("mode", "static")
+                if a_class == edge_or_node and a_mode == mode:
+                    attributes_element = a
+            if attributes_element is None:
+                # create new attributes element
+                attr_kwargs = {"mode": mode, "class": edge_or_node}
+                attributes_element = Element("attributes", **attr_kwargs)
+                self.graph_element.insert(0, attributes_element)
+            attributes_element.append(attribute)
+        return new_id
+
+    def add_viz(self, element, node_data):
+        viz = node_data.pop("viz", False)
+        if viz:
+            color = viz.get("color")
+            if color is not None:
+                if self.VERSION == "1.1":
+                    e = Element(
+                        f"{{{self.NS_VIZ}}}color",
+                        r=str(color.get("r")),
+                        g=str(color.get("g")),
+                        b=str(color.get("b")),
+                    )
+                else:
+                    e = Element(
+                        f"{{{self.NS_VIZ}}}color",
+                        r=str(color.get("r")),
+                        g=str(color.get("g")),
+                        b=str(color.get("b")),
+                        a=str(color.get("a", 1.0)),
+                    )
+                element.append(e)
+
+            size = viz.get("size")
+            if size is not None:
+                e = Element(f"{{{self.NS_VIZ}}}size", value=str(size))
+                element.append(e)
+
+            thickness = viz.get("thickness")
+            if thickness is not None:
+                e = Element(f"{{{self.NS_VIZ}}}thickness", value=str(thickness))
+                element.append(e)
+
+            shape = viz.get("shape")
+            if shape is not None:
+                if shape.startswith("http"):
+                    e = Element(
+                        f"{{{self.NS_VIZ}}}shape", value="image", uri=str(shape)
+                    )
+                else:
+                    e = Element(f"{{{self.NS_VIZ}}}shape", value=str(shape))
+                element.append(e)
+
+            position = viz.get("position")
+            if position is not None:
+                e = Element(
+                    f"{{{self.NS_VIZ}}}position",
+                    x=str(position.get("x")),
+                    y=str(position.get("y")),
+                    z=str(position.get("z")),
+                )
+                element.append(e)
+        return node_data
+
+    def add_parents(self, node_element, node_data):
+        parents = node_data.pop("parents", False)
+        if parents:
+            parents_element = Element("parents")
+            for p in parents:
+                e = Element("parent")
+                e.attrib["for"] = str(p)
+                parents_element.append(e)
+            node_element.append(parents_element)
+        return node_data
+
+    def add_slices(self, node_or_edge_element, node_or_edge_data):
+        slices = node_or_edge_data.pop("slices", False)
+        if slices:
+            slices_element = Element("slices")
+            for start, end in slices:
+                e = Element("slice", start=str(start), end=str(end))
+                slices_element.append(e)
+            node_or_edge_element.append(slices_element)
+        return node_or_edge_data
+
+    def add_spells(self, node_or_edge_element, node_or_edge_data):
+        spells = node_or_edge_data.pop("spells", False)
+        if spells:
+            spells_element = Element("spells")
+            for start, end in spells:
+                e = Element("spell")
+                if start is not None:
+                    e.attrib["start"] = str(start)
+                    self.alter_graph_mode_timeformat(start)
+                if end is not None:
+                    e.attrib["end"] = str(end)
+                    self.alter_graph_mode_timeformat(end)
+                spells_element.append(e)
+            node_or_edge_element.append(spells_element)
+        return node_or_edge_data
+
+    def alter_graph_mode_timeformat(self, start_or_end):
+        # If 'start' or 'end' appears, set timeformat
+        if start_or_end is not None:
+            if isinstance(start_or_end, str):
+                timeformat = "date"
+            elif isinstance(start_or_end, float):
+                timeformat = "double"
+            elif isinstance(start_or_end, int):
+                timeformat = "long"
+            else:
+                raise nx.NetworkXError(
+                    "timeformat should be of the type int, float or str"
+                )
+            self.graph_element.set("timeformat", timeformat)
+            # If Graph mode is static, alter to dynamic
+            if self.graph_element.get("mode") == "static":
+                self.graph_element.set("mode", "dynamic")
+
+    def write(self, fh):
+        # Serialize graph G in GEXF to the open fh
+        if self.prettyprint:
+            self.indent(self.xml)
+        document = ElementTree(self.xml)
+        document.write(fh, encoding=self.encoding, xml_declaration=True)
+
+    def indent(self, elem, level=0):
+        # in-place prettyprint formatter
+        i = "\n" + "  " * level
+        if len(elem):
+            if not elem.text or not elem.text.strip():
+                elem.text = i + "  "
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+            for elem in elem:
+                self.indent(elem, level + 1)
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+        else:
+            if level and (not elem.tail or not elem.tail.strip()):
+                elem.tail = i
+
+
+class GEXFReader(GEXF):
+    # Class to read GEXF format files
+    # use read_gexf() function
+    def __init__(self, node_type=None, version="1.2draft"):
+        self.construct_types()
+        self.node_type = node_type
+        # assume simple graph and test for multigraph on read
+        self.simple_graph = True
+        self.set_version(version)
+
+    def __call__(self, stream):
+        self.xml = ElementTree(file=stream)
+        g = self.xml.find(f"{{{self.NS_GEXF}}}graph")
+        if g is not None:
+            return self.make_graph(g)
+        # try all the versions
+        for version in self.versions:
+            self.set_version(version)
+            g = self.xml.find(f"{{{self.NS_GEXF}}}graph")
+            if g is not None:
+                return self.make_graph(g)
+        raise nx.NetworkXError("No <graph> element in GEXF file.")
+
+    def make_graph(self, graph_xml):
+        # start with empty DiGraph or MultiDiGraph
+        edgedefault = graph_xml.get("defaultedgetype", None)
+        if edgedefault == "directed":
+            G = nx.MultiDiGraph()
+        else:
+            G = nx.MultiGraph()
+
+        # graph attributes
+        graph_name = graph_xml.get("name", "")
+        if graph_name != "":
+            G.graph["name"] = graph_name
+        graph_start = graph_xml.get("start")
+        if graph_start is not None:
+            G.graph["start"] = graph_start
+        graph_end = graph_xml.get("end")
+        if graph_end is not None:
+            G.graph["end"] = graph_end
+        graph_mode = graph_xml.get("mode", "")
+        if graph_mode == "dynamic":
+            G.graph["mode"] = "dynamic"
+        else:
+            G.graph["mode"] = "static"
+
+        # timeformat
+        self.timeformat = graph_xml.get("timeformat")
+        if self.timeformat == "date":
+            self.timeformat = "string"
+
+        # node and edge attributes
+        attributes_elements = graph_xml.findall(f"{{{self.NS_GEXF}}}attributes")
+        # dictionaries to hold attributes and attribute defaults
+        node_attr = {}
+        node_default = {}
+        edge_attr = {}
+        edge_default = {}
+        for a in attributes_elements:
+            attr_class = a.get("class")
+            if attr_class == "node":
+                na, nd = self.find_gexf_attributes(a)
+                node_attr.update(na)
+                node_default.update(nd)
+                G.graph["node_default"] = node_default
+            elif attr_class == "edge":
+                ea, ed = self.find_gexf_attributes(a)
+                edge_attr.update(ea)
+                edge_default.update(ed)
+                G.graph["edge_default"] = edge_default
+            else:
+                raise  # unknown attribute class
+
+        # Hack to handle Gephi0.7beta bug
+        # add weight attribute
+        ea = {"weight": {"type": "double", "mode": "static", "title": "weight"}}
+        ed = {}
+        edge_attr.update(ea)
+        edge_default.update(ed)
+        G.graph["edge_default"] = edge_default
+
+        # add nodes
+        nodes_element = graph_xml.find(f"{{{self.NS_GEXF}}}nodes")
+        if nodes_element is not None:
+            for node_xml in nodes_element.findall(f"{{{self.NS_GEXF}}}node"):
+                self.add_node(G, node_xml, node_attr)
+
+        # add edges
+        edges_element = graph_xml.find(f"{{{self.NS_GEXF}}}edges")
+        if edges_element is not None:
+            for edge_xml in edges_element.findall(f"{{{self.NS_GEXF}}}edge"):
+                self.add_edge(G, edge_xml, edge_attr)
+
+        # switch to Graph or DiGraph if no parallel edges were found.
+        if self.simple_graph:
+            if G.is_directed():
+                G = nx.DiGraph(G)
+            else:
+                G = nx.Graph(G)
+        return G
+
+    def add_node(self, G, node_xml, node_attr, node_pid=None):
+        # add a single node with attributes to the graph
+
+        # get attributes and subattributues for node
+        data = self.decode_attr_elements(node_attr, node_xml)
+        data = self.add_parents(data, node_xml)  # add any parents
+        if self.VERSION == "1.1":
+            data = self.add_slices(data, node_xml)  # add slices
+        else:
+            data = self.add_spells(data, node_xml)  # add spells
+        data = self.add_viz(data, node_xml)  # add viz
+        data = self.add_start_end(data, node_xml)  # add start/end
+
+        # find the node id and cast it to the appropriate type
+        node_id = node_xml.get("id")
+        if self.node_type is not None:
+            node_id = self.node_type(node_id)
+
+        # every node should have a label
+        node_label = node_xml.get("label")
+        data["label"] = node_label
+
+        # parent node id
+        node_pid = node_xml.get("pid", node_pid)
+        if node_pid is not None:
+            data["pid"] = node_pid
+
+        # check for subnodes, recursive
+        subnodes = node_xml.find(f"{{{self.NS_GEXF}}}nodes")
+        if subnodes is not None:
+            for node_xml in subnodes.findall(f"{{{self.NS_GEXF}}}node"):
+                self.add_node(G, node_xml, node_attr, node_pid=node_id)
+
+        G.add_node(node_id, **data)
+
+    def add_start_end(self, data, xml):
+        # start and end times
+        ttype = self.timeformat
+        node_start = xml.get("start")
+        if node_start is not None:
+            data["start"] = self.python_type[ttype](node_start)
+        node_end = xml.get("end")
+        if node_end is not None:
+            data["end"] = self.python_type[ttype](node_end)
+        return data
+
+    def add_viz(self, data, node_xml):
+        # add viz element for node
+        viz = {}
+        color = node_xml.find(f"{{{self.NS_VIZ}}}color")
+        if color is not None:
+            if self.VERSION == "1.1":
+                viz["color"] = {
+                    "r": int(color.get("r")),
+                    "g": int(color.get("g")),
+                    "b": int(color.get("b")),
+                }
+            else:
+                viz["color"] = {
+                    "r": int(color.get("r")),
+                    "g": int(color.get("g")),
+                    "b": int(color.get("b")),
+                    "a": float(color.get("a", 1)),
+                }
+
+        size = node_xml.find(f"{{{self.NS_VIZ}}}size")
+        if size is not None:
+            viz["size"] = float(size.get("value"))
+
+        thickness = node_xml.find(f"{{{self.NS_VIZ}}}thickness")
+        if thickness is not None:
+            viz["thickness"] = float(thickness.get("value"))
+
+        shape = node_xml.find(f"{{{self.NS_VIZ}}}shape")
+        if shape is not None:
+            viz["shape"] = shape.get("shape")
+            if viz["shape"] == "image":
+                viz["shape"] = shape.get("uri")
+
+        position = node_xml.find(f"{{{self.NS_VIZ}}}position")
+        if position is not None:
+            viz["position"] = {
+                "x": float(position.get("x", 0)),
+                "y": float(position.get("y", 0)),
+                "z": float(position.get("z", 0)),
+            }
+
+        if len(viz) > 0:
+            data["viz"] = viz
+        return data
+
+    def add_parents(self, data, node_xml):
+        parents_element = node_xml.find(f"{{{self.NS_GEXF}}}parents")
+        if parents_element is not None:
+            data["parents"] = []
+            for p in parents_element.findall(f"{{{self.NS_GEXF}}}parent"):
+                parent = p.get("for")
+                data["parents"].append(parent)
+        return data
+
+    def add_slices(self, data, node_or_edge_xml):
+        slices_element = node_or_edge_xml.find(f"{{{self.NS_GEXF}}}slices")
+        if slices_element is not None:
+            data["slices"] = []
+            for s in slices_element.findall(f"{{{self.NS_GEXF}}}slice"):
+                start = s.get("start")
+                end = s.get("end")
+                data["slices"].append((start, end))
+        return data
+
+    def add_spells(self, data, node_or_edge_xml):
+        spells_element = node_or_edge_xml.find(f"{{{self.NS_GEXF}}}spells")
+        if spells_element is not None:
+            data["spells"] = []
+            ttype = self.timeformat
+            for s in spells_element.findall(f"{{{self.NS_GEXF}}}spell"):
+                start = self.python_type[ttype](s.get("start"))
+                end = self.python_type[ttype](s.get("end"))
+                data["spells"].append((start, end))
+        return data
+
+    def add_edge(self, G, edge_element, edge_attr):
+        # add an edge to the graph
+
+        # raise error if we find mixed directed and undirected edges
+        edge_direction = edge_element.get("type")
+        if G.is_directed() and edge_direction == "undirected":
+            raise nx.NetworkXError("Undirected edge found in directed graph.")
+        if (not G.is_directed()) and edge_direction == "directed":
+            raise nx.NetworkXError("Directed edge found in undirected graph.")
+
+        # Get source and target and recast type if required
+        source = edge_element.get("source")
+        target = edge_element.get("target")
+        if self.node_type is not None:
+            source = self.node_type(source)
+            target = self.node_type(target)
+
+        data = self.decode_attr_elements(edge_attr, edge_element)
+        data = self.add_start_end(data, edge_element)
+
+        if self.VERSION == "1.1":
+            data = self.add_slices(data, edge_element)  # add slices
+        else:
+            data = self.add_spells(data, edge_element)  # add spells
+
+        # GEXF stores edge ids as an attribute
+        # NetworkX uses them as keys in multigraphs
+        # if networkx_key is not specified as an attribute
+        edge_id = edge_element.get("id")
+        if edge_id is not None:
+            data["id"] = edge_id
+
+        # check if there is a 'multigraph_key' and use that as edge_id
+        multigraph_key = data.pop("networkx_key", None)
+        if multigraph_key is not None:
+            edge_id = multigraph_key
+
+        weight = edge_element.get("weight")
+        if weight is not None:
+            data["weight"] = float(weight)
+
+        edge_label = edge_element.get("label")
+        if edge_label is not None:
+            data["label"] = edge_label
+
+        if G.has_edge(source, target):
+            # seen this edge before - this is a multigraph
+            self.simple_graph = False
+        G.add_edge(source, target, key=edge_id, **data)
+        if edge_direction == "mutual":
+            G.add_edge(target, source, key=edge_id, **data)
+
+    def decode_attr_elements(self, gexf_keys, obj_xml):
+        # Use the key information to decode the attr XML
+        attr = {}
+        # look for outer '<attvalues>' element
+        attr_element = obj_xml.find(f"{{{self.NS_GEXF}}}attvalues")
+        if attr_element is not None:
+            # loop over <attvalue> elements
+            for a in attr_element.findall(f"{{{self.NS_GEXF}}}attvalue"):
+                key = a.get("for")  # for is required
+                try:  # should be in our gexf_keys dictionary
+                    title = gexf_keys[key]["title"]
+                except KeyError as err:
+                    raise nx.NetworkXError(f"No attribute defined for={key}.") from err
+                atype = gexf_keys[key]["type"]
+                value = a.get("value")
+                if atype == "boolean":
+                    value = self.convert_bool[value]
+                else:
+                    value = self.python_type[atype](value)
+                if gexf_keys[key]["mode"] == "dynamic":
+                    # for dynamic graphs use list of three-tuples
+                    # [(value1,start1,end1), (value2,start2,end2), etc]
+                    ttype = self.timeformat
+                    start = self.python_type[ttype](a.get("start"))
+                    end = self.python_type[ttype](a.get("end"))
+                    if title in attr:
+                        attr[title].append((value, start, end))
+                    else:
+                        attr[title] = [(value, start, end)]
+                else:
+                    # for static graphs just assign the value
+                    attr[title] = value
+        return attr
+
+    def find_gexf_attributes(self, attributes_element):
+        # Extract all the attributes and defaults
+        attrs = {}
+        defaults = {}
+        mode = attributes_element.get("mode")
+        for k in attributes_element.findall(f"{{{self.NS_GEXF}}}attribute"):
+            attr_id = k.get("id")
+            title = k.get("title")
+            atype = k.get("type")
+            attrs[attr_id] = {"title": title, "type": atype, "mode": mode}
+            # check for the 'default' subelement of key element and add
+            default = k.find(f"{{{self.NS_GEXF}}}default")
+            if default is not None:
+                if atype == "boolean":
+                    value = self.convert_bool[default.text]
+                else:
+                    value = self.python_type[atype](default.text)
+                defaults[title] = value
+        return attrs, defaults
+
+
+def relabel_gexf_graph(G):
+    """Relabel graph using "label" node keyword for node label.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph read from GEXF data
+
+    Returns
+    -------
+    H : graph
+      A NetworkX graph with relabeled nodes
+
+    Raises
+    ------
+    NetworkXError
+        If node labels are missing or not unique while relabel=True.
+
+    Notes
+    -----
+    This function relabels the nodes in a NetworkX graph with the
+    "label" attribute.  It also handles relabeling the specific GEXF
+    node attributes "parents", and "pid".
+    """
+    # build mapping of node labels, do some error checking
+    try:
+        mapping = [(u, G.nodes[u]["label"]) for u in G]
+    except KeyError as err:
+        raise nx.NetworkXError(
+            "Failed to relabel nodes: missing node labels found. Use relabel=False."
+        ) from err
+    x, y = zip(*mapping)
+    if len(set(y)) != len(G):
+        raise nx.NetworkXError(
+            "Failed to relabel nodes: duplicate node labels found. Use relabel=False."
+        )
+    mapping = dict(mapping)
+    H = nx.relabel_nodes(G, mapping)
+    # relabel attributes
+    for n in G:
+        m = mapping[n]
+        H.nodes[m]["id"] = n
+        H.nodes[m].pop("label")
+        if "pid" in H.nodes[m]:
+            H.nodes[m]["pid"] = mapping[G.nodes[n]["pid"]]
+        if "parents" in H.nodes[m]:
+            H.nodes[m]["parents"] = [mapping[p] for p in G.nodes[n]["parents"]]
+    return H
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/gml.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/gml.py
new file mode 100644
index 0000000000000000000000000000000000000000..c53496c3e7fd2797ce2f786e665d0ad069d8184c
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/gml.py
@@ -0,0 +1,879 @@
+"""
+Read graphs in GML format.
+
+"GML, the Graph Modelling Language, is our proposal for a portable
+file format for graphs. GML's key features are portability, simple
+syntax, extensibility and flexibility. A GML file consists of a
+hierarchical key-value lists. Graphs can be annotated with arbitrary
+data structures. The idea for a common file format was born at the
+GD'95; this proposal is the outcome of many discussions. GML is the
+standard file format in the Graphlet graph editor system. It has been
+overtaken and adapted by several other systems for drawing graphs."
+
+GML files are stored using a 7-bit ASCII encoding with any extended
+ASCII characters (iso8859-1) appearing as HTML character entities.
+You will need to give some thought into how the exported data should
+interact with different languages and even different Python versions.
+Re-importing from gml is also a concern.
+
+Without specifying a `stringizer`/`destringizer`, the code is capable of
+writing `int`/`float`/`str`/`dict`/`list` data as required by the GML
+specification.  For writing other data types, and for reading data other
+than `str` you need to explicitly supply a `stringizer`/`destringizer`.
+
+For additional documentation on the GML file format, please see the
+`GML website <https://web.archive.org/web/20190207140002/http://www.fim.uni-passau.de/index.php?id=17297&L=1>`_.
+
+Several example graphs in GML format may be found on Mark Newman's
+`Network data page <http://www-personal.umich.edu/~mejn/netdata/>`_.
+"""
+
+import html.entities as htmlentitydefs
+import re
+from ast import literal_eval
+from collections import defaultdict
+from enum import Enum
+from io import StringIO
+from typing import Any, NamedTuple
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.utils import open_file
+
+__all__ = ["read_gml", "parse_gml", "generate_gml", "write_gml"]
+
+
+def escape(text):
+    """Use XML character references to escape characters.
+
+    Use XML character references for unprintable or non-ASCII
+    characters, double quotes and ampersands in a string
+    """
+
+    def fixup(m):
+        ch = m.group(0)
+        return "&#" + str(ord(ch)) + ";"
+
+    text = re.sub('[^ -~]|[&"]', fixup, text)
+    return text if isinstance(text, str) else str(text)
+
+
+def unescape(text):
+    """Replace XML character references with the referenced characters"""
+
+    def fixup(m):
+        text = m.group(0)
+        if text[1] == "#":
+            # Character reference
+            if text[2] == "x":
+                code = int(text[3:-1], 16)
+            else:
+                code = int(text[2:-1])
+        else:
+            # Named entity
+            try:
+                code = htmlentitydefs.name2codepoint[text[1:-1]]
+            except KeyError:
+                return text  # leave unchanged
+        try:
+            return chr(code)
+        except (ValueError, OverflowError):
+            return text  # leave unchanged
+
+    return re.sub("&(?:[0-9A-Za-z]+|#(?:[0-9]+|x[0-9A-Fa-f]+));", fixup, text)
+
+
+def literal_destringizer(rep):
+    """Convert a Python literal to the value it represents.
+
+    Parameters
+    ----------
+    rep : string
+        A Python literal.
+
+    Returns
+    -------
+    value : object
+        The value of the Python literal.
+
+    Raises
+    ------
+    ValueError
+        If `rep` is not a Python literal.
+    """
+    if isinstance(rep, str):
+        orig_rep = rep
+        try:
+            return literal_eval(rep)
+        except SyntaxError as err:
+            raise ValueError(f"{orig_rep!r} is not a valid Python literal") from err
+    else:
+        raise ValueError(f"{rep!r} is not a string")
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_gml(path, label="label", destringizer=None):
+    """Read graph in GML format from `path`.
+
+    Parameters
+    ----------
+    path : file or string
+        Filename or file handle to read.
+        Filenames ending in .gz or .bz2 will be decompressed.
+
+    label : string, optional
+        If not None, the parsed nodes will be renamed according to node
+        attributes indicated by `label`. Default value: 'label'.
+
+    destringizer : callable, optional
+        A `destringizer` that recovers values stored as strings in GML. If it
+        cannot convert a string to a value, a `ValueError` is raised. Default
+        value : None.
+
+    Returns
+    -------
+    G : NetworkX graph
+        The parsed graph.
+
+    Raises
+    ------
+    NetworkXError
+        If the input cannot be parsed.
+
+    See Also
+    --------
+    write_gml, parse_gml
+    literal_destringizer
+
+    Notes
+    -----
+    GML files are stored using a 7-bit ASCII encoding with any extended
+    ASCII characters (iso8859-1) appearing as HTML character entities.
+    Without specifying a `stringizer`/`destringizer`, the code is capable of
+    writing `int`/`float`/`str`/`dict`/`list` data as required by the GML
+    specification.  For writing other data types, and for reading data other
+    than `str` you need to explicitly supply a `stringizer`/`destringizer`.
+
+    For additional documentation on the GML file format, please see the
+    `GML url <https://web.archive.org/web/20190207140002/http://www.fim.uni-passau.de/index.php?id=17297&L=1>`_.
+
+    See the module docstring :mod:`networkx.readwrite.gml` for more details.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_gml(G, "test_path4.gml")
+
+    GML values are interpreted as strings by default:
+
+    >>> H = nx.read_gml("test_path4.gml")
+    >>> H.nodes
+    NodeView(('0', '1', '2', '3'))
+
+    When a `destringizer` is provided, GML values are converted to the provided type.
+    For example, integer nodes can be recovered as shown below:
+
+    >>> J = nx.read_gml("test_path4.gml", destringizer=int)
+    >>> J.nodes
+    NodeView((0, 1, 2, 3))
+
+    """
+
+    def filter_lines(lines):
+        for line in lines:
+            try:
+                line = line.decode("ascii")
+            except UnicodeDecodeError as err:
+                raise NetworkXError("input is not ASCII-encoded") from err
+            if not isinstance(line, str):
+                lines = str(lines)
+            if line and line[-1] == "\n":
+                line = line[:-1]
+            yield line
+
+    G = parse_gml_lines(filter_lines(path), label, destringizer)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_gml(lines, label="label", destringizer=None):
+    """Parse GML graph from a string or iterable.
+
+    Parameters
+    ----------
+    lines : string or iterable of strings
+       Data in GML format.
+
+    label : string, optional
+        If not None, the parsed nodes will be renamed according to node
+        attributes indicated by `label`. Default value: 'label'.
+
+    destringizer : callable, optional
+        A `destringizer` that recovers values stored as strings in GML. If it
+        cannot convert a string to a value, a `ValueError` is raised. Default
+        value : None.
+
+    Returns
+    -------
+    G : NetworkX graph
+        The parsed graph.
+
+    Raises
+    ------
+    NetworkXError
+        If the input cannot be parsed.
+
+    See Also
+    --------
+    write_gml, read_gml
+
+    Notes
+    -----
+    This stores nested GML attributes as dictionaries in the NetworkX graph,
+    node, and edge attribute structures.
+
+    GML files are stored using a 7-bit ASCII encoding with any extended
+    ASCII characters (iso8859-1) appearing as HTML character entities.
+    Without specifying a `stringizer`/`destringizer`, the code is capable of
+    writing `int`/`float`/`str`/`dict`/`list` data as required by the GML
+    specification.  For writing other data types, and for reading data other
+    than `str` you need to explicitly supply a `stringizer`/`destringizer`.
+
+    For additional documentation on the GML file format, please see the
+    `GML url <https://web.archive.org/web/20190207140002/http://www.fim.uni-passau.de/index.php?id=17297&L=1>`_.
+
+    See the module docstring :mod:`networkx.readwrite.gml` for more details.
+    """
+
+    def decode_line(line):
+        if isinstance(line, bytes):
+            try:
+                line.decode("ascii")
+            except UnicodeDecodeError as err:
+                raise NetworkXError("input is not ASCII-encoded") from err
+        if not isinstance(line, str):
+            line = str(line)
+        return line
+
+    def filter_lines(lines):
+        if isinstance(lines, str):
+            lines = decode_line(lines)
+            lines = lines.splitlines()
+            yield from lines
+        else:
+            for line in lines:
+                line = decode_line(line)
+                if line and line[-1] == "\n":
+                    line = line[:-1]
+                if line.find("\n") != -1:
+                    raise NetworkXError("input line contains newline")
+                yield line
+
+    G = parse_gml_lines(filter_lines(lines), label, destringizer)
+    return G
+
+
+class Pattern(Enum):
+    """encodes the index of each token-matching pattern in `tokenize`."""
+
+    KEYS = 0
+    REALS = 1
+    INTS = 2
+    STRINGS = 3
+    DICT_START = 4
+    DICT_END = 5
+    COMMENT_WHITESPACE = 6
+
+
+class Token(NamedTuple):
+    category: Pattern
+    value: Any
+    line: int
+    position: int
+
+
+LIST_START_VALUE = "_networkx_list_start"
+
+
+def parse_gml_lines(lines, label, destringizer):
+    """Parse GML `lines` into a graph."""
+
+    def tokenize():
+        patterns = [
+            r"[A-Za-z][0-9A-Za-z_]*\b",  # keys
+            # reals
+            r"[+-]?(?:[0-9]*\.[0-9]+|[0-9]+\.[0-9]*|INF)(?:[Ee][+-]?[0-9]+)?",
+            r"[+-]?[0-9]+",  # ints
+            r'".*?"',  # strings
+            r"\[",  # dict start
+            r"\]",  # dict end
+            r"#.*$|\s+",  # comments and whitespaces
+        ]
+        tokens = re.compile("|".join(f"({pattern})" for pattern in patterns))
+        lineno = 0
+        multilines = []  # entries spread across multiple lines
+        for line in lines:
+            pos = 0
+
+            # deal with entries spread across multiple lines
+            #
+            # should we actually have to deal with escaped "s then do it here
+            if multilines:
+                multilines.append(line.strip())
+                if line[-1] == '"':  # closing multiline entry
+                    # multiline entries will be joined by space. cannot
+                    # reintroduce newlines as this will break the tokenizer
+                    line = " ".join(multilines)
+                    multilines = []
+                else:  # continued multiline entry
+                    lineno += 1
+                    continue
+            else:
+                if line.count('"') == 1:  # opening multiline entry
+                    if line.strip()[0] != '"' and line.strip()[-1] != '"':
+                        # since we expect something like key "value", the " should not be found at ends
+                        # otherwise tokenizer will pick up the formatting mistake.
+                        multilines = [line.rstrip()]
+                        lineno += 1
+                        continue
+
+            length = len(line)
+
+            while pos < length:
+                match = tokens.match(line, pos)
+                if match is None:
+                    m = f"cannot tokenize {line[pos:]} at ({lineno + 1}, {pos + 1})"
+                    raise NetworkXError(m)
+                for i in range(len(patterns)):
+                    group = match.group(i + 1)
+                    if group is not None:
+                        if i == 0:  # keys
+                            value = group.rstrip()
+                        elif i == 1:  # reals
+                            value = float(group)
+                        elif i == 2:  # ints
+                            value = int(group)
+                        else:
+                            value = group
+                        if i != 6:  # comments and whitespaces
+                            yield Token(Pattern(i), value, lineno + 1, pos + 1)
+                        pos += len(group)
+                        break
+            lineno += 1
+        yield Token(None, None, lineno + 1, 1)  # EOF
+
+    def unexpected(curr_token, expected):
+        category, value, lineno, pos = curr_token
+        value = repr(value) if value is not None else "EOF"
+        raise NetworkXError(f"expected {expected}, found {value} at ({lineno}, {pos})")
+
+    def consume(curr_token, category, expected):
+        if curr_token.category == category:
+            return next(tokens)
+        unexpected(curr_token, expected)
+
+    def parse_kv(curr_token):
+        dct = defaultdict(list)
+        while curr_token.category == Pattern.KEYS:
+            key = curr_token.value
+            curr_token = next(tokens)
+            category = curr_token.category
+            if category == Pattern.REALS or category == Pattern.INTS:
+                value = curr_token.value
+                curr_token = next(tokens)
+            elif category == Pattern.STRINGS:
+                value = unescape(curr_token.value[1:-1])
+                if destringizer:
+                    try:
+                        value = destringizer(value)
+                    except ValueError:
+                        pass
+                # Special handling for empty lists and tuples
+                if value == "()":
+                    value = ()
+                if value == "[]":
+                    value = []
+                curr_token = next(tokens)
+            elif category == Pattern.DICT_START:
+                curr_token, value = parse_dict(curr_token)
+            else:
+                # Allow for string convertible id and label values
+                if key in ("id", "label", "source", "target"):
+                    try:
+                        # String convert the token value
+                        value = unescape(str(curr_token.value))
+                        if destringizer:
+                            try:
+                                value = destringizer(value)
+                            except ValueError:
+                                pass
+                        curr_token = next(tokens)
+                    except Exception:
+                        msg = (
+                            "an int, float, string, '[' or string"
+                            + " convertible ASCII value for node id or label"
+                        )
+                        unexpected(curr_token, msg)
+                # Special handling for nan and infinity.  Since the gml language
+                # defines unquoted strings as keys, the numeric and string branches
+                # are skipped and we end up in this special branch, so we need to
+                # convert the current token value to a float for NAN and plain INF.
+                # +/-INF are handled in the pattern for 'reals' in tokenize().  This
+                # allows labels and values to be nan or infinity, but not keys.
+                elif curr_token.value in {"NAN", "INF"}:
+                    value = float(curr_token.value)
+                    curr_token = next(tokens)
+                else:  # Otherwise error out
+                    unexpected(curr_token, "an int, float, string or '['")
+            dct[key].append(value)
+
+        def clean_dict_value(value):
+            if not isinstance(value, list):
+                return value
+            if len(value) == 1:
+                return value[0]
+            if value[0] == LIST_START_VALUE:
+                return value[1:]
+            return value
+
+        dct = {key: clean_dict_value(value) for key, value in dct.items()}
+        return curr_token, dct
+
+    def parse_dict(curr_token):
+        # dict start
+        curr_token = consume(curr_token, Pattern.DICT_START, "'['")
+        # dict contents
+        curr_token, dct = parse_kv(curr_token)
+        # dict end
+        curr_token = consume(curr_token, Pattern.DICT_END, "']'")
+        return curr_token, dct
+
+    def parse_graph():
+        curr_token, dct = parse_kv(next(tokens))
+        if curr_token.category is not None:  # EOF
+            unexpected(curr_token, "EOF")
+        if "graph" not in dct:
+            raise NetworkXError("input contains no graph")
+        graph = dct["graph"]
+        if isinstance(graph, list):
+            raise NetworkXError("input contains more than one graph")
+        return graph
+
+    tokens = tokenize()
+    graph = parse_graph()
+
+    directed = graph.pop("directed", False)
+    multigraph = graph.pop("multigraph", False)
+    if not multigraph:
+        G = nx.DiGraph() if directed else nx.Graph()
+    else:
+        G = nx.MultiDiGraph() if directed else nx.MultiGraph()
+    graph_attr = {k: v for k, v in graph.items() if k not in ("node", "edge")}
+    G.graph.update(graph_attr)
+
+    def pop_attr(dct, category, attr, i):
+        try:
+            return dct.pop(attr)
+        except KeyError as err:
+            raise NetworkXError(f"{category} #{i} has no {attr!r} attribute") from err
+
+    nodes = graph.get("node", [])
+    mapping = {}
+    node_labels = set()
+    for i, node in enumerate(nodes if isinstance(nodes, list) else [nodes]):
+        id = pop_attr(node, "node", "id", i)
+        if id in G:
+            raise NetworkXError(f"node id {id!r} is duplicated")
+        if label is not None and label != "id":
+            node_label = pop_attr(node, "node", label, i)
+            if node_label in node_labels:
+                raise NetworkXError(f"node label {node_label!r} is duplicated")
+            node_labels.add(node_label)
+            mapping[id] = node_label
+        G.add_node(id, **node)
+
+    edges = graph.get("edge", [])
+    for i, edge in enumerate(edges if isinstance(edges, list) else [edges]):
+        source = pop_attr(edge, "edge", "source", i)
+        target = pop_attr(edge, "edge", "target", i)
+        if source not in G:
+            raise NetworkXError(f"edge #{i} has undefined source {source!r}")
+        if target not in G:
+            raise NetworkXError(f"edge #{i} has undefined target {target!r}")
+        if not multigraph:
+            if not G.has_edge(source, target):
+                G.add_edge(source, target, **edge)
+            else:
+                arrow = "->" if directed else "--"
+                msg = f"edge #{i} ({source!r}{arrow}{target!r}) is duplicated"
+                raise nx.NetworkXError(msg)
+        else:
+            key = edge.pop("key", None)
+            if key is not None and G.has_edge(source, target, key):
+                arrow = "->" if directed else "--"
+                msg = f"edge #{i} ({source!r}{arrow}{target!r}, {key!r})"
+                msg2 = 'Hint: If multigraph add "multigraph 1" to file header.'
+                raise nx.NetworkXError(msg + " is duplicated\n" + msg2)
+            G.add_edge(source, target, key, **edge)
+
+    if label is not None and label != "id":
+        G = nx.relabel_nodes(G, mapping)
+    return G
+
+
+def literal_stringizer(value):
+    """Convert a `value` to a Python literal in GML representation.
+
+    Parameters
+    ----------
+    value : object
+        The `value` to be converted to GML representation.
+
+    Returns
+    -------
+    rep : string
+        A double-quoted Python literal representing value. Unprintable
+        characters are replaced by XML character references.
+
+    Raises
+    ------
+    ValueError
+        If `value` cannot be converted to GML.
+
+    Notes
+    -----
+    The original value can be recovered using the
+    :func:`networkx.readwrite.gml.literal_destringizer` function.
+    """
+
+    def stringize(value):
+        if isinstance(value, int | bool) or value is None:
+            if value is True:  # GML uses 1/0 for boolean values.
+                buf.write(str(1))
+            elif value is False:
+                buf.write(str(0))
+            else:
+                buf.write(str(value))
+        elif isinstance(value, str):
+            text = repr(value)
+            if text[0] != "u":
+                try:
+                    value.encode("latin1")
+                except UnicodeEncodeError:
+                    text = "u" + text
+            buf.write(text)
+        elif isinstance(value, float | complex | str | bytes):
+            buf.write(repr(value))
+        elif isinstance(value, list):
+            buf.write("[")
+            first = True
+            for item in value:
+                if not first:
+                    buf.write(",")
+                else:
+                    first = False
+                stringize(item)
+            buf.write("]")
+        elif isinstance(value, tuple):
+            if len(value) > 1:
+                buf.write("(")
+                first = True
+                for item in value:
+                    if not first:
+                        buf.write(",")
+                    else:
+                        first = False
+                    stringize(item)
+                buf.write(")")
+            elif value:
+                buf.write("(")
+                stringize(value[0])
+                buf.write(",)")
+            else:
+                buf.write("()")
+        elif isinstance(value, dict):
+            buf.write("{")
+            first = True
+            for key, value in value.items():
+                if not first:
+                    buf.write(",")
+                else:
+                    first = False
+                stringize(key)
+                buf.write(":")
+                stringize(value)
+            buf.write("}")
+        elif isinstance(value, set):
+            buf.write("{")
+            first = True
+            for item in value:
+                if not first:
+                    buf.write(",")
+                else:
+                    first = False
+                stringize(item)
+            buf.write("}")
+        else:
+            msg = f"{value!r} cannot be converted into a Python literal"
+            raise ValueError(msg)
+
+    buf = StringIO()
+    stringize(value)
+    return buf.getvalue()
+
+
+def generate_gml(G, stringizer=None):
+    r"""Generate a single entry of the graph `G` in GML format.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The graph to be converted to GML.
+
+    stringizer : callable, optional
+        A `stringizer` which converts non-int/non-float/non-dict values into
+        strings. If it cannot convert a value into a string, it should raise a
+        `ValueError` to indicate that. Default value: None.
+
+    Returns
+    -------
+    lines: generator of strings
+        Lines of GML data. Newlines are not appended.
+
+    Raises
+    ------
+    NetworkXError
+        If `stringizer` cannot convert a value into a string, or the value to
+        convert is not a string while `stringizer` is None.
+
+    See Also
+    --------
+    literal_stringizer
+
+    Notes
+    -----
+    Graph attributes named 'directed', 'multigraph', 'node' or
+    'edge', node attributes named 'id' or 'label', edge attributes
+    named 'source' or 'target' (or 'key' if `G` is a multigraph)
+    are ignored because these attribute names are used to encode the graph
+    structure.
+
+    GML files are stored using a 7-bit ASCII encoding with any extended
+    ASCII characters (iso8859-1) appearing as HTML character entities.
+    Without specifying a `stringizer`/`destringizer`, the code is capable of
+    writing `int`/`float`/`str`/`dict`/`list` data as required by the GML
+    specification.  For writing other data types, and for reading data other
+    than `str` you need to explicitly supply a `stringizer`/`destringizer`.
+
+    For additional documentation on the GML file format, please see the
+    `GML url <https://web.archive.org/web/20190207140002/http://www.fim.uni-passau.de/index.php?id=17297&L=1>`_.
+
+    See the module docstring :mod:`networkx.readwrite.gml` for more details.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> G.add_node("1")
+    >>> print("\n".join(nx.generate_gml(G)))
+    graph [
+      node [
+        id 0
+        label "1"
+      ]
+    ]
+    >>> G = nx.MultiGraph([("a", "b"), ("a", "b")])
+    >>> print("\n".join(nx.generate_gml(G)))
+    graph [
+      multigraph 1
+      node [
+        id 0
+        label "a"
+      ]
+      node [
+        id 1
+        label "b"
+      ]
+      edge [
+        source 0
+        target 1
+        key 0
+      ]
+      edge [
+        source 0
+        target 1
+        key 1
+      ]
+    ]
+    """
+    valid_keys = re.compile("^[A-Za-z][0-9A-Za-z_]*$")
+
+    def stringize(key, value, ignored_keys, indent, in_list=False):
+        if not isinstance(key, str):
+            raise NetworkXError(f"{key!r} is not a string")
+        if not valid_keys.match(key):
+            raise NetworkXError(f"{key!r} is not a valid key")
+        if not isinstance(key, str):
+            key = str(key)
+        if key not in ignored_keys:
+            if isinstance(value, int | bool):
+                if key == "label":
+                    yield indent + key + ' "' + str(value) + '"'
+                elif value is True:
+                    # python bool is an instance of int
+                    yield indent + key + " 1"
+                elif value is False:
+                    yield indent + key + " 0"
+                # GML only supports signed 32-bit integers
+                elif value < -(2**31) or value >= 2**31:
+                    yield indent + key + ' "' + str(value) + '"'
+                else:
+                    yield indent + key + " " + str(value)
+            elif isinstance(value, float):
+                text = repr(value).upper()
+                # GML matches INF to keys, so prepend + to INF. Use repr(float(*))
+                # instead of string literal to future proof against changes to repr.
+                if text == repr(float("inf")).upper():
+                    text = "+" + text
+                else:
+                    # GML requires that a real literal contain a decimal point, but
+                    # repr may not output a decimal point when the mantissa is
+                    # integral and hence needs fixing.
+                    epos = text.rfind("E")
+                    if epos != -1 and text.find(".", 0, epos) == -1:
+                        text = text[:epos] + "." + text[epos:]
+                if key == "label":
+                    yield indent + key + ' "' + text + '"'
+                else:
+                    yield indent + key + " " + text
+            elif isinstance(value, dict):
+                yield indent + key + " ["
+                next_indent = indent + "  "
+                for key, value in value.items():
+                    yield from stringize(key, value, (), next_indent)
+                yield indent + "]"
+            elif isinstance(value, tuple) and key == "label":
+                yield indent + key + f' "({",".join(repr(v) for v in value)})"'
+            elif isinstance(value, list | tuple) and key != "label" and not in_list:
+                if len(value) == 0:
+                    yield indent + key + " " + f'"{value!r}"'
+                if len(value) == 1:
+                    yield indent + key + " " + f'"{LIST_START_VALUE}"'
+                for val in value:
+                    yield from stringize(key, val, (), indent, True)
+            else:
+                if stringizer:
+                    try:
+                        value = stringizer(value)
+                    except ValueError as err:
+                        raise NetworkXError(
+                            f"{value!r} cannot be converted into a string"
+                        ) from err
+                if not isinstance(value, str):
+                    raise NetworkXError(f"{value!r} is not a string")
+                yield indent + key + ' "' + escape(value) + '"'
+
+    multigraph = G.is_multigraph()
+    yield "graph ["
+
+    # Output graph attributes
+    if G.is_directed():
+        yield "  directed 1"
+    if multigraph:
+        yield "  multigraph 1"
+    ignored_keys = {"directed", "multigraph", "node", "edge"}
+    for attr, value in G.graph.items():
+        yield from stringize(attr, value, ignored_keys, "  ")
+
+    # Output node data
+    node_id = dict(zip(G, range(len(G))))
+    ignored_keys = {"id", "label"}
+    for node, attrs in G.nodes.items():
+        yield "  node ["
+        yield "    id " + str(node_id[node])
+        yield from stringize("label", node, (), "    ")
+        for attr, value in attrs.items():
+            yield from stringize(attr, value, ignored_keys, "    ")
+        yield "  ]"
+
+    # Output edge data
+    ignored_keys = {"source", "target"}
+    kwargs = {"data": True}
+    if multigraph:
+        ignored_keys.add("key")
+        kwargs["keys"] = True
+    for e in G.edges(**kwargs):
+        yield "  edge ["
+        yield "    source " + str(node_id[e[0]])
+        yield "    target " + str(node_id[e[1]])
+        if multigraph:
+            yield from stringize("key", e[2], (), "    ")
+        for attr, value in e[-1].items():
+            yield from stringize(attr, value, ignored_keys, "    ")
+        yield "  ]"
+    yield "]"
+
+
+@open_file(1, mode="wb")
+def write_gml(G, path, stringizer=None):
+    """Write a graph `G` in GML format to the file or file handle `path`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        The graph to be converted to GML.
+
+    path : string or file
+        Filename or file handle to write to.
+        Filenames ending in .gz or .bz2 will be compressed.
+
+    stringizer : callable, optional
+        A `stringizer` which converts non-int/non-float/non-dict values into
+        strings. If it cannot convert a value into a string, it should raise a
+        `ValueError` to indicate that. Default value: None.
+
+    Raises
+    ------
+    NetworkXError
+        If `stringizer` cannot convert a value into a string, or the value to
+        convert is not a string while `stringizer` is None.
+
+    See Also
+    --------
+    read_gml, generate_gml
+    literal_stringizer
+
+    Notes
+    -----
+    Graph attributes named 'directed', 'multigraph', 'node' or
+    'edge', node attributes named 'id' or 'label', edge attributes
+    named 'source' or 'target' (or 'key' if `G` is a multigraph)
+    are ignored because these attribute names are used to encode the graph
+    structure.
+
+    GML files are stored using a 7-bit ASCII encoding with any extended
+    ASCII characters (iso8859-1) appearing as HTML character entities.
+    Without specifying a `stringizer`/`destringizer`, the code is capable of
+    writing `int`/`float`/`str`/`dict`/`list` data as required by the GML
+    specification.  For writing other data types, and for reading data other
+    than `str` you need to explicitly supply a `stringizer`/`destringizer`.
+
+    Note that while we allow non-standard GML to be read from a file, we make
+    sure to write GML format. In particular, underscores are not allowed in
+    attribute names.
+    For additional documentation on the GML file format, please see the
+    `GML url <https://web.archive.org/web/20190207140002/http://www.fim.uni-passau.de/index.php?id=17297&L=1>`_.
+
+    See the module docstring :mod:`networkx.readwrite.gml` for more details.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> nx.write_gml(G, "test_path5.gml")
+
+    Filenames ending in .gz or .bz2 will be compressed.
+
+    >>> nx.write_gml(G, "test_path5.gml.gz")
+    """
+    for line in generate_gml(G, stringizer):
+        path.write((line + "\n").encode("ascii"))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/graph6.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/graph6.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdc2925e5fb264a3d3e3235571682e38530d7f69
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/graph6.py
@@ -0,0 +1,427 @@
+# Original author: D. Eppstein, UC Irvine, August 12, 2003.
+# The original code at http://www.ics.uci.edu/~eppstein/PADS/ is public domain.
+"""Functions for reading and writing graphs in the *graph6* format.
+
+The *graph6* file format is suitable for small graphs or large dense
+graphs. For large sparse graphs, use the *sparse6* format.
+
+For more information, see the `graph6`_ homepage.
+
+.. _graph6: http://users.cecs.anu.edu.au/~bdm/data/formats.html
+
+"""
+
+from itertools import islice
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.utils import not_implemented_for, open_file
+
+__all__ = ["from_graph6_bytes", "read_graph6", "to_graph6_bytes", "write_graph6"]
+
+
+def _generate_graph6_bytes(G, nodes, header):
+    """Yield bytes in the graph6 encoding of a graph.
+
+    `G` is an undirected simple graph. `nodes` is the list of nodes for
+    which the node-induced subgraph will be encoded; if `nodes` is the
+    list of all nodes in the graph, the entire graph will be
+    encoded. `header` is a Boolean that specifies whether to generate
+    the header ``b'>>graph6<<'`` before the remaining data.
+
+    This function generates `bytes` objects in the following order:
+
+    1. the header (if requested),
+    2. the encoding of the number of nodes,
+    3. each character, one-at-a-time, in the encoding of the requested
+       node-induced subgraph,
+    4. a newline character.
+
+    This function raises :exc:`ValueError` if the graph is too large for
+    the graph6 format (that is, greater than ``2 ** 36`` nodes).
+
+    """
+    n = len(G)
+    if n >= 2**36:
+        raise ValueError(
+            "graph6 is only defined if number of nodes is less than 2 ** 36"
+        )
+    if header:
+        yield b">>graph6<<"
+    for d in n_to_data(n):
+        yield str.encode(chr(d + 63))
+    # This generates the same as `(v in G[u] for u, v in combinations(G, 2))`,
+    # but in "column-major" order instead of "row-major" order.
+    bits = (nodes[j] in G[nodes[i]] for j in range(1, n) for i in range(j))
+    chunk = list(islice(bits, 6))
+    while chunk:
+        d = sum(b << 5 - i for i, b in enumerate(chunk))
+        yield str.encode(chr(d + 63))
+        chunk = list(islice(bits, 6))
+    yield b"\n"
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_graph6_bytes(bytes_in):
+    """Read a simple undirected graph in graph6 format from bytes.
+
+    Parameters
+    ----------
+    bytes_in : bytes
+       Data in graph6 format
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `bytes_in` is unable to be parsed in graph6 format
+
+    ValueError
+        If any character ``c`` in bytes_in does not satisfy
+        ``63 <= ord(c) < 127``.
+
+    Examples
+    --------
+    >>> G = nx.from_graph6_bytes(b"A_")
+    >>> sorted(G.edges())
+    [(0, 1)]
+
+    Notes
+    -----
+    Per the graph6 spec, the header (e.g. ``b'>>graph6<<'``) must not be
+    followed by a newline character.
+
+    See Also
+    --------
+    read_graph6, write_graph6
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <http://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+
+    def bits():
+        """Returns sequence of individual bits from 6-bit-per-value
+        list of data values."""
+        for d in data:
+            for i in [5, 4, 3, 2, 1, 0]:
+                yield (d >> i) & 1
+
+    # Ignore trailing newline
+    bytes_in = bytes_in.rstrip(b"\n")
+
+    if bytes_in.startswith(b">>graph6<<"):
+        bytes_in = bytes_in[10:]
+
+    data = [c - 63 for c in bytes_in]
+    if any(c > 63 for c in data):
+        raise ValueError("each input character must be in range(63, 127)")
+
+    n, data = data_to_n(data)
+    nd = (n * (n - 1) // 2 + 5) // 6
+    if len(data) != nd:
+        raise NetworkXError(
+            f"Expected {n * (n - 1) // 2} bits but got {len(data) * 6} in graph6"
+        )
+
+    G = nx.Graph()
+    G.add_nodes_from(range(n))
+    for (i, j), b in zip(((i, j) for j in range(1, n) for i in range(j)), bits()):
+        if b:
+            G.add_edge(i, j)
+
+    return G
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+def to_graph6_bytes(G, nodes=None, header=True):
+    """Convert a simple undirected graph to bytes in graph6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by ``G.nodes()`` is used.
+
+    header: bool
+       If True add '>>graph6<<' bytes to head of data.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+
+    ValueError
+        If the graph has at least ``2 ** 36`` nodes; the graph6 format
+        is only defined for graphs of order less than ``2 ** 36``.
+
+    Examples
+    --------
+    >>> nx.to_graph6_bytes(nx.path_graph(2))
+    b'>>graph6<<A_\\n'
+
+    See Also
+    --------
+    from_graph6_bytes, read_graph6, write_graph6_bytes
+
+    Notes
+    -----
+    The returned bytes end with a newline character.
+
+    The format does not support edge or node labels, parallel edges or
+    self loops. If self loops are present they are silently ignored.
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <http://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    H = nx.convert_node_labels_to_integers(G)
+    nodes = sorted(H.nodes())
+    return b"".join(_generate_graph6_bytes(H, nodes, header))
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_graph6(path):
+    """Read simple undirected graphs in graph6 format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    Returns
+    -------
+    G : Graph or list of Graphs
+       If the file contains multiple lines then a list of graphs is returned
+
+    Raises
+    ------
+    NetworkXError
+        If the string is unable to be parsed in graph6 format
+
+    Examples
+    --------
+    You can read a graph6 file by giving the path to the file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     _ = f.write(b">>graph6<<A_\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_graph6(f.name)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    You can also read a graph6 file by giving an open file-like object::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     _ = f.write(b">>graph6<<A_\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_graph6(f)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    See Also
+    --------
+    from_graph6_bytes, write_graph6
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <http://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    glist = []
+    for line in path:
+        line = line.strip()
+        if not len(line):
+            continue
+        glist.append(from_graph6_bytes(line))
+    if len(glist) == 1:
+        return glist[0]
+    else:
+        return glist
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@open_file(1, mode="wb")
+def write_graph6(G, path, nodes=None, header=True):
+    """Write a simple undirected graph to a path in graph6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by ``G.nodes()`` is used.
+
+    header: bool
+       If True add '>>graph6<<' string to head of data
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+
+    ValueError
+        If the graph has at least ``2 ** 36`` nodes; the graph6 format
+        is only defined for graphs of order less than ``2 ** 36``.
+
+    Examples
+    --------
+    You can write a graph6 file by giving the path to a file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     nx.write_graph6(nx.path_graph(2), f.name)
+        ...     _ = f.seek(0)
+        ...     print(f.read())
+        b'>>graph6<<A_\\n'
+
+    See Also
+    --------
+    from_graph6_bytes, read_graph6
+
+    Notes
+    -----
+    The function writes a newline character after writing the encoding
+    of the graph.
+
+    The format does not support edge or node labels, parallel edges or
+    self loops.  If self loops are present they are silently ignored.
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <http://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    return write_graph6_file(G, path, nodes=nodes, header=header)
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+def write_graph6_file(G, f, nodes=None, header=True):
+    """Write a simple undirected graph to a file-like object in graph6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    f : file-like object
+       The file to write.
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by ``G.nodes()`` is used.
+
+    header: bool
+       If True add '>>graph6<<' string to head of data
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed or is a multigraph.
+
+    ValueError
+        If the graph has at least ``2 ** 36`` nodes; the graph6 format
+        is only defined for graphs of order less than ``2 ** 36``.
+
+    Examples
+    --------
+    You can write a graph6 file by giving an open file-like object::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     nx.write_graph6(nx.path_graph(2), f)
+        ...     _ = f.seek(0)
+        ...     print(f.read())
+        b'>>graph6<<A_\\n'
+
+    See Also
+    --------
+    from_graph6_bytes, read_graph6
+
+    Notes
+    -----
+    The function writes a newline character after writing the encoding
+    of the graph.
+
+    The format does not support edge or node labels, parallel edges or
+    self loops.  If self loops are present they are silently ignored.
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <http://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    H = nx.convert_node_labels_to_integers(G)
+    nodes = sorted(H.nodes())
+    for b in _generate_graph6_bytes(H, nodes, header):
+        f.write(b)
+
+
+def data_to_n(data):
+    """Read initial one-, four- or eight-unit value from graph6
+    integer sequence.
+
+    Return (value, rest of seq.)"""
+    if data[0] <= 62:
+        return data[0], data[1:]
+    if data[1] <= 62:
+        return (data[1] << 12) + (data[2] << 6) + data[3], data[4:]
+    return (
+        (data[2] << 30)
+        + (data[3] << 24)
+        + (data[4] << 18)
+        + (data[5] << 12)
+        + (data[6] << 6)
+        + data[7],
+        data[8:],
+    )
+
+
+def n_to_data(n):
+    """Convert an integer to one-, four- or eight-unit graph6 sequence.
+
+    This function is undefined if `n` is not in ``range(2 ** 36)``.
+
+    """
+    if n <= 62:
+        return [n]
+    elif n <= 258047:
+        return [63, (n >> 12) & 0x3F, (n >> 6) & 0x3F, n & 0x3F]
+    else:  # if n <= 68719476735:
+        return [
+            63,
+            63,
+            (n >> 30) & 0x3F,
+            (n >> 24) & 0x3F,
+            (n >> 18) & 0x3F,
+            (n >> 12) & 0x3F,
+            (n >> 6) & 0x3F,
+            n & 0x3F,
+        ]
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/graphml.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/graphml.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ca1741452c963d99ffb8bfad2fb09eab8badd6b
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/graphml.py
@@ -0,0 +1,1053 @@
+"""
+*******
+GraphML
+*******
+Read and write graphs in GraphML format.
+
+.. warning::
+
+    This parser uses the standard xml library present in Python, which is
+    insecure - see :external+python:mod:`xml` for additional information.
+    Only parse GraphML files you trust.
+
+This implementation does not support mixed graphs (directed and unidirected
+edges together), hyperedges, nested graphs, or ports.
+
+"GraphML is a comprehensive and easy-to-use file format for graphs. It
+consists of a language core to describe the structural properties of a
+graph and a flexible extension mechanism to add application-specific
+data. Its main features include support of
+
+    * directed, undirected, and mixed graphs,
+    * hypergraphs,
+    * hierarchical graphs,
+    * graphical representations,
+    * references to external data,
+    * application-specific attribute data, and
+    * light-weight parsers.
+
+Unlike many other file formats for graphs, GraphML does not use a
+custom syntax. Instead, it is based on XML and hence ideally suited as
+a common denominator for all kinds of services generating, archiving,
+or processing graphs."
+
+http://graphml.graphdrawing.org/
+
+Format
+------
+GraphML is an XML format.  See
+http://graphml.graphdrawing.org/specification.html for the specification and
+http://graphml.graphdrawing.org/primer/graphml-primer.html
+for examples.
+"""
+
+import warnings
+from collections import defaultdict
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = [
+    "write_graphml",
+    "read_graphml",
+    "generate_graphml",
+    "write_graphml_xml",
+    "write_graphml_lxml",
+    "parse_graphml",
+    "GraphMLWriter",
+    "GraphMLReader",
+]
+
+
+@open_file(1, mode="wb")
+def write_graphml_xml(
+    G,
+    path,
+    encoding="utf-8",
+    prettyprint=True,
+    infer_numeric_types=False,
+    named_key_ids=False,
+    edge_id_from_attribute=None,
+):
+    """Write G in GraphML XML format to path
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+    encoding : string (optional)
+       Encoding for text data.
+    prettyprint : bool (optional)
+       If True use line breaks and indenting in output XML.
+    infer_numeric_types : boolean
+       Determine if numeric types should be generalized.
+       For example, if edges have both int and float 'weight' attributes,
+       we infer in GraphML that both are floats.
+    named_key_ids : bool (optional)
+       If True use attr.name as value for key elements' id attribute.
+    edge_id_from_attribute : dict key (optional)
+        If provided, the graphml edge id is set by looking up the corresponding
+        edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
+        the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_graphml(G, "test.graphml")
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed
+    and unidirected edges together) hyperedges, nested graphs, or ports.
+    """
+    writer = GraphMLWriter(
+        encoding=encoding,
+        prettyprint=prettyprint,
+        infer_numeric_types=infer_numeric_types,
+        named_key_ids=named_key_ids,
+        edge_id_from_attribute=edge_id_from_attribute,
+    )
+    writer.add_graph_element(G)
+    writer.dump(path)
+
+
+@open_file(1, mode="wb")
+def write_graphml_lxml(
+    G,
+    path,
+    encoding="utf-8",
+    prettyprint=True,
+    infer_numeric_types=False,
+    named_key_ids=False,
+    edge_id_from_attribute=None,
+):
+    """Write G in GraphML XML format to path
+
+    This function uses the LXML framework and should be faster than
+    the version using the xml library.
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+    encoding : string (optional)
+       Encoding for text data.
+    prettyprint : bool (optional)
+       If True use line breaks and indenting in output XML.
+    infer_numeric_types : boolean
+       Determine if numeric types should be generalized.
+       For example, if edges have both int and float 'weight' attributes,
+       we infer in GraphML that both are floats.
+    named_key_ids : bool (optional)
+       If True use attr.name as value for key elements' id attribute.
+    edge_id_from_attribute : dict key (optional)
+        If provided, the graphml edge id is set by looking up the corresponding
+        edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
+        the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_graphml_lxml(G, "fourpath.graphml")
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed
+    and unidirected edges together) hyperedges, nested graphs, or ports.
+    """
+    try:
+        import lxml.etree as lxmletree
+    except ImportError:
+        return write_graphml_xml(
+            G,
+            path,
+            encoding,
+            prettyprint,
+            infer_numeric_types,
+            named_key_ids,
+            edge_id_from_attribute,
+        )
+
+    writer = GraphMLWriterLxml(
+        path,
+        graph=G,
+        encoding=encoding,
+        prettyprint=prettyprint,
+        infer_numeric_types=infer_numeric_types,
+        named_key_ids=named_key_ids,
+        edge_id_from_attribute=edge_id_from_attribute,
+    )
+    writer.dump()
+
+
+def generate_graphml(
+    G,
+    encoding="utf-8",
+    prettyprint=True,
+    named_key_ids=False,
+    edge_id_from_attribute=None,
+):
+    """Generate GraphML lines for G
+
+    Parameters
+    ----------
+    G : graph
+       A networkx graph
+    encoding : string (optional)
+       Encoding for text data.
+    prettyprint : bool (optional)
+       If True use line breaks and indenting in output XML.
+    named_key_ids : bool (optional)
+       If True use attr.name as value for key elements' id attribute.
+    edge_id_from_attribute : dict key (optional)
+        If provided, the graphml edge id is set by looking up the corresponding
+        edge data attribute keyed by this parameter. If `None` or the key does not exist in edge data,
+        the edge id is set by the edge key if `G` is a MultiGraph, else the edge id is left unset.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> linefeed = chr(10)  # linefeed = \n
+    >>> s = linefeed.join(nx.generate_graphml(G))
+    >>> for line in nx.generate_graphml(G):  # doctest: +SKIP
+    ...     print(line)
+
+    Notes
+    -----
+    This implementation does not support mixed graphs (directed and unidirected
+    edges together) hyperedges, nested graphs, or ports.
+    """
+    writer = GraphMLWriter(
+        encoding=encoding,
+        prettyprint=prettyprint,
+        named_key_ids=named_key_ids,
+        edge_id_from_attribute=edge_id_from_attribute,
+    )
+    writer.add_graph_element(G)
+    yield from str(writer).splitlines()
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_graphml(path, node_type=str, edge_key_type=int, force_multigraph=False):
+    """Read graph in GraphML format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    node_type: Python type (default: str)
+       Convert node ids to this type
+
+    edge_key_type: Python type (default: int)
+       Convert graphml edge ids to this type. Multigraphs use id as edge key.
+       Non-multigraphs add to edge attribute dict with name "id".
+
+    force_multigraph : bool (default: False)
+       If True, return a multigraph with edge keys. If False (the default)
+       return a multigraph when multiedges are in the graph.
+
+    Returns
+    -------
+    graph: NetworkX graph
+        If parallel edges are present or `force_multigraph=True` then
+        a MultiGraph or MultiDiGraph is returned. Otherwise a Graph/DiGraph.
+        The returned graph is directed if the file indicates it should be.
+
+    Notes
+    -----
+    Default node and edge attributes are not propagated to each node and edge.
+    They can be obtained from `G.graph` and applied to node and edge attributes
+    if desired using something like this:
+
+    >>> default_color = G.graph["node_default"]["color"]  # doctest: +SKIP
+    >>> for node, data in G.nodes(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+    >>> default_color = G.graph["edge_default"]["color"]  # doctest: +SKIP
+    >>> for u, v, data in G.edges(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+
+    This implementation does not support mixed graphs (directed and unidirected
+    edges together), hypergraphs, nested graphs, or ports.
+
+    For multigraphs the GraphML edge "id" will be used as the edge
+    key.  If not specified then they "key" attribute will be used.  If
+    there is no "key" attribute a default NetworkX multigraph edge key
+    will be provided.
+
+    Files with the yEd "yfiles" extension can be read. The type of the node's
+    shape is preserved in the `shape_type` node attribute.
+
+    yEd compressed files ("file.graphmlz" extension) can be read by renaming
+    the file to "file.graphml.gz".
+
+    """
+    reader = GraphMLReader(node_type, edge_key_type, force_multigraph)
+    # need to check for multiple graphs
+    glist = list(reader(path=path))
+    if len(glist) == 0:
+        # If no graph comes back, try looking for an incomplete header
+        header = b'<graphml xmlns="http://graphml.graphdrawing.org/xmlns">'
+        path.seek(0)
+        old_bytes = path.read()
+        new_bytes = old_bytes.replace(b"<graphml>", header)
+        glist = list(reader(string=new_bytes))
+        if len(glist) == 0:
+            raise nx.NetworkXError("file not successfully read as graphml")
+    return glist[0]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_graphml(
+    graphml_string, node_type=str, edge_key_type=int, force_multigraph=False
+):
+    """Read graph in GraphML format from string.
+
+    Parameters
+    ----------
+    graphml_string : string
+       String containing graphml information
+       (e.g., contents of a graphml file).
+
+    node_type: Python type (default: str)
+       Convert node ids to this type
+
+    edge_key_type: Python type (default: int)
+       Convert graphml edge ids to this type. Multigraphs use id as edge key.
+       Non-multigraphs add to edge attribute dict with name "id".
+
+    force_multigraph : bool (default: False)
+       If True, return a multigraph with edge keys. If False (the default)
+       return a multigraph when multiedges are in the graph.
+
+
+    Returns
+    -------
+    graph: NetworkX graph
+        If no parallel edges are found a Graph or DiGraph is returned.
+        Otherwise a MultiGraph or MultiDiGraph is returned.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> linefeed = chr(10)  # linefeed = \n
+    >>> s = linefeed.join(nx.generate_graphml(G))
+    >>> H = nx.parse_graphml(s)
+
+    Notes
+    -----
+    Default node and edge attributes are not propagated to each node and edge.
+    They can be obtained from `G.graph` and applied to node and edge attributes
+    if desired using something like this:
+
+    >>> default_color = G.graph["node_default"]["color"]  # doctest: +SKIP
+    >>> for node, data in G.nodes(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+    >>> default_color = G.graph["edge_default"]["color"]  # doctest: +SKIP
+    >>> for u, v, data in G.edges(data=True):  # doctest: +SKIP
+    ...     if "color" not in data:
+    ...         data["color"] = default_color
+
+    This implementation does not support mixed graphs (directed and unidirected
+    edges together), hypergraphs, nested graphs, or ports.
+
+    For multigraphs the GraphML edge "id" will be used as the edge
+    key.  If not specified then they "key" attribute will be used.  If
+    there is no "key" attribute a default NetworkX multigraph edge key
+    will be provided.
+
+    """
+    reader = GraphMLReader(node_type, edge_key_type, force_multigraph)
+    # need to check for multiple graphs
+    glist = list(reader(string=graphml_string))
+    if len(glist) == 0:
+        # If no graph comes back, try looking for an incomplete header
+        header = '<graphml xmlns="http://graphml.graphdrawing.org/xmlns">'
+        new_string = graphml_string.replace("<graphml>", header)
+        glist = list(reader(string=new_string))
+        if len(glist) == 0:
+            raise nx.NetworkXError("file not successfully read as graphml")
+    return glist[0]
+
+
+class GraphML:
+    NS_GRAPHML = "http://graphml.graphdrawing.org/xmlns"
+    NS_XSI = "http://www.w3.org/2001/XMLSchema-instance"
+    # xmlns:y="http://www.yworks.com/xml/graphml"
+    NS_Y = "http://www.yworks.com/xml/graphml"
+    SCHEMALOCATION = " ".join(
+        [
+            "http://graphml.graphdrawing.org/xmlns",
+            "http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd",
+        ]
+    )
+
+    def construct_types(self):
+        types = [
+            (int, "integer"),  # for Gephi GraphML bug
+            (str, "yfiles"),
+            (str, "string"),
+            (int, "int"),
+            (int, "long"),
+            (float, "float"),
+            (float, "double"),
+            (bool, "boolean"),
+        ]
+
+        # These additions to types allow writing numpy types
+        try:
+            import numpy as np
+        except:
+            pass
+        else:
+            # prepend so that python types are created upon read (last entry wins)
+            types = [
+                (np.float64, "float"),
+                (np.float32, "float"),
+                (np.float16, "float"),
+                (np.int_, "int"),
+                (np.int8, "int"),
+                (np.int16, "int"),
+                (np.int32, "int"),
+                (np.int64, "int"),
+                (np.uint8, "int"),
+                (np.uint16, "int"),
+                (np.uint32, "int"),
+                (np.uint64, "int"),
+                (np.int_, "int"),
+                (np.intc, "int"),
+                (np.intp, "int"),
+            ] + types
+
+        self.xml_type = dict(types)
+        self.python_type = dict(reversed(a) for a in types)
+
+    # This page says that data types in GraphML follow Java(TM).
+    #  http://graphml.graphdrawing.org/primer/graphml-primer.html#AttributesDefinition
+    # true and false are the only boolean literals:
+    #  http://en.wikibooks.org/wiki/Java_Programming/Literals#Boolean_Literals
+    convert_bool = {
+        # We use data.lower() in actual use.
+        "true": True,
+        "false": False,
+        # Include integer strings for convenience.
+        "0": False,
+        0: False,
+        "1": True,
+        1: True,
+    }
+
+    def get_xml_type(self, key):
+        """Wrapper around the xml_type dict that raises a more informative
+        exception message when a user attempts to use data of a type not
+        supported by GraphML."""
+        try:
+            return self.xml_type[key]
+        except KeyError as err:
+            raise TypeError(
+                f"GraphML does not support type {key} as data values."
+            ) from err
+
+
+class GraphMLWriter(GraphML):
+    def __init__(
+        self,
+        graph=None,
+        encoding="utf-8",
+        prettyprint=True,
+        infer_numeric_types=False,
+        named_key_ids=False,
+        edge_id_from_attribute=None,
+    ):
+        self.construct_types()
+        from xml.etree.ElementTree import Element
+
+        self.myElement = Element
+
+        self.infer_numeric_types = infer_numeric_types
+        self.prettyprint = prettyprint
+        self.named_key_ids = named_key_ids
+        self.edge_id_from_attribute = edge_id_from_attribute
+        self.encoding = encoding
+        self.xml = self.myElement(
+            "graphml",
+            {
+                "xmlns": self.NS_GRAPHML,
+                "xmlns:xsi": self.NS_XSI,
+                "xsi:schemaLocation": self.SCHEMALOCATION,
+            },
+        )
+        self.keys = {}
+        self.attributes = defaultdict(list)
+        self.attribute_types = defaultdict(set)
+
+        if graph is not None:
+            self.add_graph_element(graph)
+
+    def __str__(self):
+        from xml.etree.ElementTree import tostring
+
+        if self.prettyprint:
+            self.indent(self.xml)
+        s = tostring(self.xml).decode(self.encoding)
+        return s
+
+    def attr_type(self, name, scope, value):
+        """Infer the attribute type of data named name. Currently this only
+        supports inference of numeric types.
+
+        If self.infer_numeric_types is false, type is used. Otherwise, pick the
+        most general of types found across all values with name and scope. This
+        means edges with data named 'weight' are treated separately from nodes
+        with data named 'weight'.
+        """
+        if self.infer_numeric_types:
+            types = self.attribute_types[(name, scope)]
+
+            if len(types) > 1:
+                types = {self.get_xml_type(t) for t in types}
+                if "string" in types:
+                    return str
+                elif "float" in types or "double" in types:
+                    return float
+                else:
+                    return int
+            else:
+                return list(types)[0]
+        else:
+            return type(value)
+
+    def get_key(self, name, attr_type, scope, default):
+        keys_key = (name, attr_type, scope)
+        try:
+            return self.keys[keys_key]
+        except KeyError:
+            if self.named_key_ids:
+                new_id = name
+            else:
+                new_id = f"d{len(list(self.keys))}"
+
+            self.keys[keys_key] = new_id
+            key_kwargs = {
+                "id": new_id,
+                "for": scope,
+                "attr.name": name,
+                "attr.type": attr_type,
+            }
+            key_element = self.myElement("key", **key_kwargs)
+            # add subelement for data default value if present
+            if default is not None:
+                default_element = self.myElement("default")
+                default_element.text = str(default)
+                key_element.append(default_element)
+            self.xml.insert(0, key_element)
+        return new_id
+
+    def add_data(self, name, element_type, value, scope="all", default=None):
+        """
+        Make a data element for an edge or a node. Keep a log of the
+        type in the keys table.
+        """
+        if element_type not in self.xml_type:
+            raise nx.NetworkXError(
+                f"GraphML writer does not support {element_type} as data values."
+            )
+        keyid = self.get_key(name, self.get_xml_type(element_type), scope, default)
+        data_element = self.myElement("data", key=keyid)
+        data_element.text = str(value)
+        return data_element
+
+    def add_attributes(self, scope, xml_obj, data, default):
+        """Appends attribute data to edges or nodes, and stores type information
+        to be added later. See add_graph_element.
+        """
+        for k, v in data.items():
+            self.attribute_types[(str(k), scope)].add(type(v))
+            self.attributes[xml_obj].append([k, v, scope, default.get(k)])
+
+    def add_nodes(self, G, graph_element):
+        default = G.graph.get("node_default", {})
+        for node, data in G.nodes(data=True):
+            node_element = self.myElement("node", id=str(node))
+            self.add_attributes("node", node_element, data, default)
+            graph_element.append(node_element)
+
+    def add_edges(self, G, graph_element):
+        if G.is_multigraph():
+            for u, v, key, data in G.edges(data=True, keys=True):
+                edge_element = self.myElement(
+                    "edge",
+                    source=str(u),
+                    target=str(v),
+                    id=str(data.get(self.edge_id_from_attribute))
+                    if self.edge_id_from_attribute
+                    and self.edge_id_from_attribute in data
+                    else str(key),
+                )
+                default = G.graph.get("edge_default", {})
+                self.add_attributes("edge", edge_element, data, default)
+                graph_element.append(edge_element)
+        else:
+            for u, v, data in G.edges(data=True):
+                if self.edge_id_from_attribute and self.edge_id_from_attribute in data:
+                    # select attribute to be edge id
+                    edge_element = self.myElement(
+                        "edge",
+                        source=str(u),
+                        target=str(v),
+                        id=str(data.get(self.edge_id_from_attribute)),
+                    )
+                else:
+                    # default: no edge id
+                    edge_element = self.myElement("edge", source=str(u), target=str(v))
+                default = G.graph.get("edge_default", {})
+                self.add_attributes("edge", edge_element, data, default)
+                graph_element.append(edge_element)
+
+    def add_graph_element(self, G):
+        """
+        Serialize graph G in GraphML to the stream.
+        """
+        if G.is_directed():
+            default_edge_type = "directed"
+        else:
+            default_edge_type = "undirected"
+
+        graphid = G.graph.pop("id", None)
+        if graphid is None:
+            graph_element = self.myElement("graph", edgedefault=default_edge_type)
+        else:
+            graph_element = self.myElement(
+                "graph", edgedefault=default_edge_type, id=graphid
+            )
+        default = {}
+        data = {
+            k: v
+            for (k, v) in G.graph.items()
+            if k not in ["node_default", "edge_default"]
+        }
+        self.add_attributes("graph", graph_element, data, default)
+        self.add_nodes(G, graph_element)
+        self.add_edges(G, graph_element)
+
+        # self.attributes contains a mapping from XML Objects to a list of
+        # data that needs to be added to them.
+        # We postpone processing in order to do type inference/generalization.
+        # See self.attr_type
+        for xml_obj, data in self.attributes.items():
+            for k, v, scope, default in data:
+                xml_obj.append(
+                    self.add_data(
+                        str(k), self.attr_type(k, scope, v), str(v), scope, default
+                    )
+                )
+        self.xml.append(graph_element)
+
+    def add_graphs(self, graph_list):
+        """Add many graphs to this GraphML document."""
+        for G in graph_list:
+            self.add_graph_element(G)
+
+    def dump(self, stream):
+        from xml.etree.ElementTree import ElementTree
+
+        if self.prettyprint:
+            self.indent(self.xml)
+        document = ElementTree(self.xml)
+        document.write(stream, encoding=self.encoding, xml_declaration=True)
+
+    def indent(self, elem, level=0):
+        # in-place prettyprint formatter
+        i = "\n" + level * "  "
+        if len(elem):
+            if not elem.text or not elem.text.strip():
+                elem.text = i + "  "
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+            for elem in elem:
+                self.indent(elem, level + 1)
+            if not elem.tail or not elem.tail.strip():
+                elem.tail = i
+        else:
+            if level and (not elem.tail or not elem.tail.strip()):
+                elem.tail = i
+
+
+class IncrementalElement:
+    """Wrapper for _IncrementalWriter providing an Element like interface.
+
+    This wrapper does not intend to be a complete implementation but rather to
+    deal with those calls used in GraphMLWriter.
+    """
+
+    def __init__(self, xml, prettyprint):
+        self.xml = xml
+        self.prettyprint = prettyprint
+
+    def append(self, element):
+        self.xml.write(element, pretty_print=self.prettyprint)
+
+
+class GraphMLWriterLxml(GraphMLWriter):
+    def __init__(
+        self,
+        path,
+        graph=None,
+        encoding="utf-8",
+        prettyprint=True,
+        infer_numeric_types=False,
+        named_key_ids=False,
+        edge_id_from_attribute=None,
+    ):
+        self.construct_types()
+        import lxml.etree as lxmletree
+
+        self.myElement = lxmletree.Element
+
+        self._encoding = encoding
+        self._prettyprint = prettyprint
+        self.named_key_ids = named_key_ids
+        self.edge_id_from_attribute = edge_id_from_attribute
+        self.infer_numeric_types = infer_numeric_types
+
+        self._xml_base = lxmletree.xmlfile(path, encoding=encoding)
+        self._xml = self._xml_base.__enter__()
+        self._xml.write_declaration()
+
+        # We need to have a xml variable that support insertion. This call is
+        # used for adding the keys to the document.
+        # We will store those keys in a plain list, and then after the graph
+        # element is closed we will add them to the main graphml element.
+        self.xml = []
+        self._keys = self.xml
+        self._graphml = self._xml.element(
+            "graphml",
+            {
+                "xmlns": self.NS_GRAPHML,
+                "xmlns:xsi": self.NS_XSI,
+                "xsi:schemaLocation": self.SCHEMALOCATION,
+            },
+        )
+        self._graphml.__enter__()
+        self.keys = {}
+        self.attribute_types = defaultdict(set)
+
+        if graph is not None:
+            self.add_graph_element(graph)
+
+    def add_graph_element(self, G):
+        """
+        Serialize graph G in GraphML to the stream.
+        """
+        if G.is_directed():
+            default_edge_type = "directed"
+        else:
+            default_edge_type = "undirected"
+
+        graphid = G.graph.pop("id", None)
+        if graphid is None:
+            graph_element = self._xml.element("graph", edgedefault=default_edge_type)
+        else:
+            graph_element = self._xml.element(
+                "graph", edgedefault=default_edge_type, id=graphid
+            )
+
+        # gather attributes types for the whole graph
+        # to find the most general numeric format needed.
+        # Then pass through attributes to create key_id for each.
+        graphdata = {
+            k: v
+            for k, v in G.graph.items()
+            if k not in ("node_default", "edge_default")
+        }
+        node_default = G.graph.get("node_default", {})
+        edge_default = G.graph.get("edge_default", {})
+        # Graph attributes
+        for k, v in graphdata.items():
+            self.attribute_types[(str(k), "graph")].add(type(v))
+        for k, v in graphdata.items():
+            element_type = self.get_xml_type(self.attr_type(k, "graph", v))
+            self.get_key(str(k), element_type, "graph", None)
+        # Nodes and data
+        for node, d in G.nodes(data=True):
+            for k, v in d.items():
+                self.attribute_types[(str(k), "node")].add(type(v))
+        for node, d in G.nodes(data=True):
+            for k, v in d.items():
+                T = self.get_xml_type(self.attr_type(k, "node", v))
+                self.get_key(str(k), T, "node", node_default.get(k))
+        # Edges and data
+        if G.is_multigraph():
+            for u, v, ekey, d in G.edges(keys=True, data=True):
+                for k, v in d.items():
+                    self.attribute_types[(str(k), "edge")].add(type(v))
+            for u, v, ekey, d in G.edges(keys=True, data=True):
+                for k, v in d.items():
+                    T = self.get_xml_type(self.attr_type(k, "edge", v))
+                    self.get_key(str(k), T, "edge", edge_default.get(k))
+        else:
+            for u, v, d in G.edges(data=True):
+                for k, v in d.items():
+                    self.attribute_types[(str(k), "edge")].add(type(v))
+            for u, v, d in G.edges(data=True):
+                for k, v in d.items():
+                    T = self.get_xml_type(self.attr_type(k, "edge", v))
+                    self.get_key(str(k), T, "edge", edge_default.get(k))
+
+        # Now add attribute keys to the xml file
+        for key in self.xml:
+            self._xml.write(key, pretty_print=self._prettyprint)
+
+        # The incremental_writer writes each node/edge as it is created
+        incremental_writer = IncrementalElement(self._xml, self._prettyprint)
+        with graph_element:
+            self.add_attributes("graph", incremental_writer, graphdata, {})
+            self.add_nodes(G, incremental_writer)  # adds attributes too
+            self.add_edges(G, incremental_writer)  # adds attributes too
+
+    def add_attributes(self, scope, xml_obj, data, default):
+        """Appends attribute data."""
+        for k, v in data.items():
+            data_element = self.add_data(
+                str(k), self.attr_type(str(k), scope, v), str(v), scope, default.get(k)
+            )
+            xml_obj.append(data_element)
+
+    def __str__(self):
+        return object.__str__(self)
+
+    def dump(self, stream=None):
+        self._graphml.__exit__(None, None, None)
+        self._xml_base.__exit__(None, None, None)
+
+
+# default is lxml is present.
+write_graphml = write_graphml_lxml
+
+
+class GraphMLReader(GraphML):
+    """Read a GraphML document.  Produces NetworkX graph objects."""
+
+    def __init__(self, node_type=str, edge_key_type=int, force_multigraph=False):
+        self.construct_types()
+        self.node_type = node_type
+        self.edge_key_type = edge_key_type
+        self.multigraph = force_multigraph  # If False, test for multiedges
+        self.edge_ids = {}  # dict mapping (u,v) tuples to edge id attributes
+
+    def __call__(self, path=None, string=None):
+        from xml.etree.ElementTree import ElementTree, fromstring
+
+        if path is not None:
+            self.xml = ElementTree(file=path)
+        elif string is not None:
+            self.xml = fromstring(string)
+        else:
+            raise ValueError("Must specify either 'path' or 'string' as kwarg")
+        (keys, defaults) = self.find_graphml_keys(self.xml)
+        for g in self.xml.findall(f"{{{self.NS_GRAPHML}}}graph"):
+            yield self.make_graph(g, keys, defaults)
+
+    def make_graph(self, graph_xml, graphml_keys, defaults, G=None):
+        # set default graph type
+        edgedefault = graph_xml.get("edgedefault", None)
+        if G is None:
+            if edgedefault == "directed":
+                G = nx.MultiDiGraph()
+            else:
+                G = nx.MultiGraph()
+        # set defaults for graph attributes
+        G.graph["node_default"] = {}
+        G.graph["edge_default"] = {}
+        for key_id, value in defaults.items():
+            key_for = graphml_keys[key_id]["for"]
+            name = graphml_keys[key_id]["name"]
+            python_type = graphml_keys[key_id]["type"]
+            if key_for == "node":
+                G.graph["node_default"].update({name: python_type(value)})
+            if key_for == "edge":
+                G.graph["edge_default"].update({name: python_type(value)})
+        # hyperedges are not supported
+        hyperedge = graph_xml.find(f"{{{self.NS_GRAPHML}}}hyperedge")
+        if hyperedge is not None:
+            raise nx.NetworkXError("GraphML reader doesn't support hyperedges")
+        # add nodes
+        for node_xml in graph_xml.findall(f"{{{self.NS_GRAPHML}}}node"):
+            self.add_node(G, node_xml, graphml_keys, defaults)
+        # add edges
+        for edge_xml in graph_xml.findall(f"{{{self.NS_GRAPHML}}}edge"):
+            self.add_edge(G, edge_xml, graphml_keys)
+        # add graph data
+        data = self.decode_data_elements(graphml_keys, graph_xml)
+        G.graph.update(data)
+
+        # switch to Graph or DiGraph if no parallel edges were found
+        if self.multigraph:
+            return G
+
+        G = nx.DiGraph(G) if G.is_directed() else nx.Graph(G)
+        # add explicit edge "id" from file as attribute in NX graph.
+        nx.set_edge_attributes(G, values=self.edge_ids, name="id")
+        return G
+
+    def add_node(self, G, node_xml, graphml_keys, defaults):
+        """Add a node to the graph."""
+        # warn on finding unsupported ports tag
+        ports = node_xml.find(f"{{{self.NS_GRAPHML}}}port")
+        if ports is not None:
+            warnings.warn("GraphML port tag not supported.")
+        # find the node by id and cast it to the appropriate type
+        node_id = self.node_type(node_xml.get("id"))
+        # get data/attributes for node
+        data = self.decode_data_elements(graphml_keys, node_xml)
+        G.add_node(node_id, **data)
+        # get child nodes
+        if node_xml.attrib.get("yfiles.foldertype") == "group":
+            graph_xml = node_xml.find(f"{{{self.NS_GRAPHML}}}graph")
+            self.make_graph(graph_xml, graphml_keys, defaults, G)
+
+    def add_edge(self, G, edge_element, graphml_keys):
+        """Add an edge to the graph."""
+        # warn on finding unsupported ports tag
+        ports = edge_element.find(f"{{{self.NS_GRAPHML}}}port")
+        if ports is not None:
+            warnings.warn("GraphML port tag not supported.")
+
+        # raise error if we find mixed directed and undirected edges
+        directed = edge_element.get("directed")
+        if G.is_directed() and directed == "false":
+            msg = "directed=false edge found in directed graph."
+            raise nx.NetworkXError(msg)
+        if (not G.is_directed()) and directed == "true":
+            msg = "directed=true edge found in undirected graph."
+            raise nx.NetworkXError(msg)
+
+        source = self.node_type(edge_element.get("source"))
+        target = self.node_type(edge_element.get("target"))
+        data = self.decode_data_elements(graphml_keys, edge_element)
+        # GraphML stores edge ids as an attribute
+        # NetworkX uses them as keys in multigraphs too if no key
+        # attribute is specified
+        edge_id = edge_element.get("id")
+        if edge_id:
+            # self.edge_ids is used by `make_graph` method for non-multigraphs
+            self.edge_ids[source, target] = edge_id
+            try:
+                edge_id = self.edge_key_type(edge_id)
+            except ValueError:  # Could not convert.
+                pass
+        else:
+            edge_id = data.get("key")
+
+        if G.has_edge(source, target):
+            # mark this as a multigraph
+            self.multigraph = True
+
+        # Use add_edges_from to avoid error with add_edge when `'key' in data`
+        # Note there is only one edge here...
+        G.add_edges_from([(source, target, edge_id, data)])
+
+    def decode_data_elements(self, graphml_keys, obj_xml):
+        """Use the key information to decode the data XML if present."""
+        data = {}
+        for data_element in obj_xml.findall(f"{{{self.NS_GRAPHML}}}data"):
+            key = data_element.get("key")
+            try:
+                data_name = graphml_keys[key]["name"]
+                data_type = graphml_keys[key]["type"]
+            except KeyError as err:
+                raise nx.NetworkXError(f"Bad GraphML data: no key {key}") from err
+            text = data_element.text
+            # assume anything with subelements is a yfiles extension
+            if text is not None and len(list(data_element)) == 0:
+                if data_type is bool:
+                    # Ignore cases.
+                    # http://docs.oracle.com/javase/6/docs/api/java/lang/
+                    # Boolean.html#parseBoolean%28java.lang.String%29
+                    data[data_name] = self.convert_bool[text.lower()]
+                else:
+                    data[data_name] = data_type(text)
+            elif len(list(data_element)) > 0:
+                # Assume yfiles as subelements, try to extract node_label
+                node_label = None
+                # set GenericNode's configuration as shape type
+                gn = data_element.find(f"{{{self.NS_Y}}}GenericNode")
+                if gn is not None:
+                    data["shape_type"] = gn.get("configuration")
+                for node_type in ["GenericNode", "ShapeNode", "SVGNode", "ImageNode"]:
+                    pref = f"{{{self.NS_Y}}}{node_type}/{{{self.NS_Y}}}"
+                    geometry = data_element.find(f"{pref}Geometry")
+                    if geometry is not None:
+                        data["x"] = geometry.get("x")
+                        data["y"] = geometry.get("y")
+                    if node_label is None:
+                        node_label = data_element.find(f"{pref}NodeLabel")
+                    shape = data_element.find(f"{pref}Shape")
+                    if shape is not None:
+                        data["shape_type"] = shape.get("type")
+                if node_label is not None:
+                    data["label"] = node_label.text
+
+                # check all the different types of edges available in yEd.
+                for edge_type in [
+                    "PolyLineEdge",
+                    "SplineEdge",
+                    "QuadCurveEdge",
+                    "BezierEdge",
+                    "ArcEdge",
+                ]:
+                    pref = f"{{{self.NS_Y}}}{edge_type}/{{{self.NS_Y}}}"
+                    edge_label = data_element.find(f"{pref}EdgeLabel")
+                    if edge_label is not None:
+                        break
+                if edge_label is not None:
+                    data["label"] = edge_label.text
+            elif text is None:
+                data[data_name] = ""
+        return data
+
+    def find_graphml_keys(self, graph_element):
+        """Extracts all the keys and key defaults from the xml."""
+        graphml_keys = {}
+        graphml_key_defaults = {}
+        for k in graph_element.findall(f"{{{self.NS_GRAPHML}}}key"):
+            attr_id = k.get("id")
+            attr_type = k.get("attr.type")
+            attr_name = k.get("attr.name")
+            yfiles_type = k.get("yfiles.type")
+            if yfiles_type is not None:
+                attr_name = yfiles_type
+                attr_type = "yfiles"
+            if attr_type is None:
+                attr_type = "string"
+                warnings.warn(f"No key type for id {attr_id}. Using string")
+            if attr_name is None:
+                raise nx.NetworkXError(f"Unknown key for id {attr_id}.")
+            graphml_keys[attr_id] = {
+                "name": attr_name,
+                "type": self.python_type[attr_type],
+                "for": k.get("for"),
+            }
+            # check for "default" sub-element of key element
+            default = k.find(f"{{{self.NS_GRAPHML}}}default")
+            if default is not None:
+                # Handle default values identically to data element values
+                python_type = graphml_keys[attr_id]["type"]
+                if python_type is bool:
+                    graphml_key_defaults[attr_id] = self.convert_bool[
+                        default.text.lower()
+                    ]
+                else:
+                    graphml_key_defaults[attr_id] = python_type(default.text)
+        return graphml_keys, graphml_key_defaults
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/leda.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/leda.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d88a67d39d7ff27bf4af36895c52c9546cca329
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/leda.py
@@ -0,0 +1,108 @@
+"""
+Read graphs in LEDA format.
+
+LEDA is a C++ class library for efficient data types and algorithms.
+
+Format
+------
+See http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
+
+"""
+# Original author: D. Eppstein, UC Irvine, August 12, 2003.
+# The original code at http://www.ics.uci.edu/~eppstein/PADS/ is public domain.
+
+__all__ = ["read_leda", "parse_leda"]
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.utils import open_file
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_leda(path, encoding="UTF-8"):
+    """Read graph in LEDA format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    Returns
+    -------
+    G : NetworkX graph
+
+    Examples
+    --------
+    >>> G = nx.read_leda("file.leda")  # doctest: +SKIP
+
+    References
+    ----------
+    .. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
+    """
+    lines = (line.decode(encoding) for line in path)
+    G = parse_leda(lines)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_leda(lines):
+    """Read graph in LEDA format from string or iterable.
+
+    Parameters
+    ----------
+    lines : string or iterable
+       Data in LEDA format.
+
+    Returns
+    -------
+    G : NetworkX graph
+
+    Examples
+    --------
+    >>> G = nx.parse_leda(string)  # doctest: +SKIP
+
+    References
+    ----------
+    .. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html
+    """
+    if isinstance(lines, str):
+        lines = iter(lines.split("\n"))
+    lines = iter(
+        [
+            line.rstrip("\n")
+            for line in lines
+            if not (line.startswith(("#", "\n")) or line == "")
+        ]
+    )
+    for i in range(3):
+        next(lines)
+    # Graph
+    du = int(next(lines))  # -1=directed, -2=undirected
+    if du == -1:
+        G = nx.DiGraph()
+    else:
+        G = nx.Graph()
+
+    # Nodes
+    n = int(next(lines))  # number of nodes
+    node = {}
+    for i in range(1, n + 1):  # LEDA counts from 1 to n
+        symbol = next(lines).rstrip().strip("|{}|  ")
+        if symbol == "":
+            symbol = str(i)  # use int if no label - could be trouble
+        node[i] = symbol
+
+    G.add_nodes_from([s for i, s in node.items()])
+
+    # Edges
+    m = int(next(lines))  # number of edges
+    for i in range(m):
+        try:
+            s, t, reversal, label = next(lines).split()
+        except BaseException as err:
+            raise NetworkXError(f"Too few fields in LEDA.GRAPH edge {i + 1}") from err
+        # BEWARE: no handling of reversal edges
+        G.add_edge(node[int(s)], node[int(t)], label=label[2:-2])
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/multiline_adjlist.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/multiline_adjlist.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5b0b1c153b2f55f22f0fb0e8db9df8bdc0da18d
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/multiline_adjlist.py
@@ -0,0 +1,393 @@
+"""
+*************************
+Multi-line Adjacency List
+*************************
+Read and write NetworkX graphs as multi-line adjacency lists.
+
+The multi-line adjacency list format is useful for graphs with
+nodes that can be meaningfully represented as strings.  With this format
+simple edge data can be stored but node or graph data is not.
+
+Format
+------
+The first label in a line is the source node label followed by the node degree
+d.  The next d lines are target node labels and optional edge data.
+That pattern repeats for all nodes in the graph.
+
+The graph with edges a-b, a-c, d-e can be represented as the following
+adjacency list (anything following the # in a line is a comment)::
+
+     # example.multiline-adjlist
+     a 2
+     b
+     c
+     d 1
+     e
+"""
+
+__all__ = [
+    "generate_multiline_adjlist",
+    "write_multiline_adjlist",
+    "parse_multiline_adjlist",
+    "read_multiline_adjlist",
+]
+
+import networkx as nx
+from networkx.utils import open_file
+
+
+def generate_multiline_adjlist(G, delimiter=" "):
+    """Generate a single line of the graph G in multiline adjacency list format.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    delimiter : string, optional
+       Separator for node labels
+
+    Returns
+    -------
+    lines : string
+        Lines of data in multiline adjlist format.
+
+    Examples
+    --------
+    >>> G = nx.lollipop_graph(4, 3)
+    >>> for line in nx.generate_multiline_adjlist(G):
+    ...     print(line)
+    0 3
+    1 {}
+    2 {}
+    3 {}
+    1 2
+    2 {}
+    3 {}
+    2 1
+    3 {}
+    3 1
+    4 {}
+    4 1
+    5 {}
+    5 1
+    6 {}
+    6 0
+
+    See Also
+    --------
+    write_multiline_adjlist, read_multiline_adjlist
+    """
+    if G.is_directed():
+        if G.is_multigraph():
+            for s, nbrs in G.adjacency():
+                nbr_edges = [
+                    (u, data)
+                    for u, datadict in nbrs.items()
+                    for key, data in datadict.items()
+                ]
+                deg = len(nbr_edges)
+                yield str(s) + delimiter + str(deg)
+                for u, d in nbr_edges:
+                    if d is None:
+                        yield str(u)
+                    else:
+                        yield str(u) + delimiter + str(d)
+        else:  # directed single edges
+            for s, nbrs in G.adjacency():
+                deg = len(nbrs)
+                yield str(s) + delimiter + str(deg)
+                for u, d in nbrs.items():
+                    if d is None:
+                        yield str(u)
+                    else:
+                        yield str(u) + delimiter + str(d)
+    else:  # undirected
+        if G.is_multigraph():
+            seen = set()  # helper dict used to avoid duplicate edges
+            for s, nbrs in G.adjacency():
+                nbr_edges = [
+                    (u, data)
+                    for u, datadict in nbrs.items()
+                    if u not in seen
+                    for key, data in datadict.items()
+                ]
+                deg = len(nbr_edges)
+                yield str(s) + delimiter + str(deg)
+                for u, d in nbr_edges:
+                    if d is None:
+                        yield str(u)
+                    else:
+                        yield str(u) + delimiter + str(d)
+                seen.add(s)
+        else:  # undirected single edges
+            seen = set()  # helper dict used to avoid duplicate edges
+            for s, nbrs in G.adjacency():
+                nbr_edges = [(u, d) for u, d in nbrs.items() if u not in seen]
+                deg = len(nbr_edges)
+                yield str(s) + delimiter + str(deg)
+                for u, d in nbr_edges:
+                    if d is None:
+                        yield str(u)
+                    else:
+                        yield str(u) + delimiter + str(d)
+                seen.add(s)
+
+
+@open_file(1, mode="wb")
+def write_multiline_adjlist(G, path, delimiter=" ", comments="#", encoding="utf-8"):
+    """Write the graph G in multiline adjacency list format to path
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    path : string or file
+       Filename or file handle to write to.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels
+
+    encoding : string, optional
+       Text encoding.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_multiline_adjlist(G, "test.multi_adjlist")
+
+    The path can be a file handle or a string with the name of the file. If a
+    file handle is provided, it has to be opened in 'wb' mode.
+
+    >>> fh = open("test.multi_adjlist2", "wb")
+    >>> nx.write_multiline_adjlist(G, fh)
+
+    Filenames ending in .gz or .bz2 will be compressed.
+
+    >>> nx.write_multiline_adjlist(G, "test.multi_adjlist.gz")
+
+    See Also
+    --------
+    read_multiline_adjlist
+    """
+    import sys
+    import time
+
+    pargs = comments + " ".join(sys.argv)
+    header = (
+        f"{pargs}\n"
+        + comments
+        + f" GMT {time.asctime(time.gmtime())}\n"
+        + comments
+        + f" {G.name}\n"
+    )
+    path.write(header.encode(encoding))
+
+    for multiline in generate_multiline_adjlist(G, delimiter):
+        multiline += "\n"
+        path.write(multiline.encode(encoding))
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_multiline_adjlist(
+    lines, comments="#", delimiter=None, create_using=None, nodetype=None, edgetype=None
+):
+    """Parse lines of a multiline adjacency list representation of a graph.
+
+    Parameters
+    ----------
+    lines : list or iterator of strings
+        Input data in multiline adjlist format
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    nodetype : Python type, optional
+       Convert nodes to this type.
+
+    edgetype : Python type, optional
+       Convert edges to this type.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels.  The default is whitespace.
+
+    Returns
+    -------
+    G: NetworkX graph
+        The graph corresponding to the lines in multiline adjacency list format.
+
+    Examples
+    --------
+    >>> lines = [
+    ...     "1 2",
+    ...     "2 {'weight':3, 'name': 'Frodo'}",
+    ...     "3 {}",
+    ...     "2 1",
+    ...     "5 {'weight':6, 'name': 'Saruman'}",
+    ... ]
+    >>> G = nx.parse_multiline_adjlist(iter(lines), nodetype=int)
+    >>> list(G)
+    [1, 2, 3, 5]
+
+    """
+    from ast import literal_eval
+
+    G = nx.empty_graph(0, create_using)
+    for line in lines:
+        p = line.find(comments)
+        if p >= 0:
+            line = line[:p]
+        if not line:
+            continue
+        try:
+            (u, deg) = line.rstrip("\n").split(delimiter)
+            deg = int(deg)
+        except BaseException as err:
+            raise TypeError(f"Failed to read node and degree on line ({line})") from err
+        if nodetype is not None:
+            try:
+                u = nodetype(u)
+            except BaseException as err:
+                raise TypeError(
+                    f"Failed to convert node ({u}) to type {nodetype}"
+                ) from err
+        G.add_node(u)
+        for i in range(deg):
+            while True:
+                try:
+                    line = next(lines)
+                except StopIteration as err:
+                    msg = f"Failed to find neighbor for node ({u})"
+                    raise TypeError(msg) from err
+                p = line.find(comments)
+                if p >= 0:
+                    line = line[:p]
+                if line:
+                    break
+            vlist = line.rstrip("\n").split(delimiter)
+            numb = len(vlist)
+            if numb < 1:
+                continue  # isolated node
+            v = vlist.pop(0)
+            data = "".join(vlist)
+            if nodetype is not None:
+                try:
+                    v = nodetype(v)
+                except BaseException as err:
+                    raise TypeError(
+                        f"Failed to convert node ({v}) to type {nodetype}"
+                    ) from err
+            if edgetype is not None:
+                try:
+                    edgedata = {"weight": edgetype(data)}
+                except BaseException as err:
+                    raise TypeError(
+                        f"Failed to convert edge data ({data}) to type {edgetype}"
+                    ) from err
+            else:
+                try:  # try to evaluate
+                    edgedata = literal_eval(data)
+                except:
+                    edgedata = {}
+            G.add_edge(u, v, **edgedata)
+
+    return G
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_multiline_adjlist(
+    path,
+    comments="#",
+    delimiter=None,
+    create_using=None,
+    nodetype=None,
+    edgetype=None,
+    encoding="utf-8",
+):
+    """Read graph in multi-line adjacency list format from path.
+
+    Parameters
+    ----------
+    path : string or file
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    nodetype : Python type, optional
+       Convert nodes to this type.
+
+    edgetype : Python type, optional
+       Convert edge data to this type.
+
+    comments : string, optional
+       Marker for comment lines
+
+    delimiter : string, optional
+       Separator for node labels.  The default is whitespace.
+
+    Returns
+    -------
+    G: NetworkX graph
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_multiline_adjlist(G, "test.multi_adjlistP4")
+    >>> G = nx.read_multiline_adjlist("test.multi_adjlistP4")
+
+    The path can be a file or a string with the name of the file. If a
+    file s provided, it has to be opened in 'rb' mode.
+
+    >>> fh = open("test.multi_adjlistP4", "rb")
+    >>> G = nx.read_multiline_adjlist(fh)
+
+    Filenames ending in .gz or .bz2 will be compressed.
+
+    >>> nx.write_multiline_adjlist(G, "test.multi_adjlistP4.gz")
+    >>> G = nx.read_multiline_adjlist("test.multi_adjlistP4.gz")
+
+    The optional nodetype is a function to convert node strings to nodetype.
+
+    For example
+
+    >>> G = nx.read_multiline_adjlist("test.multi_adjlistP4", nodetype=int)
+
+    will attempt to convert all nodes to integer type.
+
+    The optional edgetype is a function to convert edge data strings to
+    edgetype.
+
+    >>> G = nx.read_multiline_adjlist("test.multi_adjlistP4")
+
+    The optional create_using parameter is a NetworkX graph container.
+    The default is Graph(), an undirected graph.  To read the data as
+    a directed graph use
+
+    >>> G = nx.read_multiline_adjlist("test.multi_adjlistP4", create_using=nx.DiGraph)
+
+    Notes
+    -----
+    This format does not store graph, node, or edge data.
+
+    See Also
+    --------
+    write_multiline_adjlist
+    """
+    lines = (line.decode(encoding) for line in path)
+    return parse_multiline_adjlist(
+        lines,
+        comments=comments,
+        delimiter=delimiter,
+        create_using=create_using,
+        nodetype=nodetype,
+        edgetype=edgetype,
+    )
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/p2g.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/p2g.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bde362ea8d511e603ce3b8d95a6206d74091663
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/p2g.py
@@ -0,0 +1,113 @@
+"""
+This module provides the following: read and write of p2g format
+used in metabolic pathway studies.
+
+See:
+<https://web.archive.org/web/20080626113807/http://www.cs.purdue.edu/homes/koyuturk/pathway/>
+for a description.
+
+The summary is included here:
+
+A file that describes a uniquely labeled graph (with extension ".gr")
+format looks like the following:
+
+
+name
+3 4
+a
+1 2
+b
+
+c
+0 2
+
+"name" is simply a description of what the graph corresponds to. The
+second line displays the number of nodes and number of edges,
+respectively. This sample graph contains three nodes labeled "a", "b",
+and "c". The rest of the graph contains two lines for each node. The
+first line for a node contains the node label. After the declaration
+of the node label, the out-edges of that node in the graph are
+provided. For instance, "a" is linked to nodes 1 and 2, which are
+labeled "b" and "c", while the node labeled "b" has no outgoing
+edges. Observe that node labeled "c" has an outgoing edge to
+itself. Indeed, self-loops are allowed. Node index starts from 0.
+
+"""
+
+import networkx as nx
+from networkx.utils import open_file
+
+
+@open_file(1, mode="w")
+def write_p2g(G, path, encoding="utf-8"):
+    """Write NetworkX graph in p2g format.
+
+    Notes
+    -----
+    This format is meant to be used with directed graphs with
+    possible self loops.
+    """
+    path.write((f"{G.name}\n").encode(encoding))
+    path.write((f"{G.order()} {G.size()}\n").encode(encoding))
+    nodes = list(G)
+    # make dictionary mapping nodes to integers
+    nodenumber = dict(zip(nodes, range(len(nodes))))
+    for n in nodes:
+        path.write((f"{n}\n").encode(encoding))
+        for nbr in G.neighbors(n):
+            path.write((f"{nodenumber[nbr]} ").encode(encoding))
+        path.write("\n".encode(encoding))
+
+
+@open_file(0, mode="r")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_p2g(path, encoding="utf-8"):
+    """Read graph in p2g format from path.
+
+    Parameters
+    ----------
+    path : string or file
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    Returns
+    -------
+    MultiDiGraph
+
+    Notes
+    -----
+    If you want a DiGraph (with no self loops allowed and no edge data)
+    use D=nx.DiGraph(read_p2g(path))
+    """
+    lines = (line.decode(encoding) for line in path)
+    G = parse_p2g(lines)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_p2g(lines):
+    """Parse p2g format graph from string or iterable.
+
+    Returns
+    -------
+    MultiDiGraph
+    """
+    description = next(lines).strip()
+    # are multiedges (parallel edges) allowed?
+    G = nx.MultiDiGraph(name=description, selfloops=True)
+    nnodes, nedges = map(int, next(lines).split())
+    nodelabel = {}
+    nbrs = {}
+    # loop over the nodes keeping track of node labels and out neighbors
+    # defer adding edges until all node labels are known
+    for i in range(nnodes):
+        n = next(lines).strip()
+        nodelabel[i] = n
+        G.add_node(n)
+        nbrs[n] = map(int, next(lines).split())
+    # now we know all of the node labels so we can add the edges
+    # with the correct labels
+    for n in G:
+        for nbr in nbrs[n]:
+            G.add_edge(n, nodelabel[nbr])
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/pajek.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/pajek.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cab6b9ae68ba9c9869f0452394838925ff2a7db
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/pajek.py
@@ -0,0 +1,286 @@
+"""
+*****
+Pajek
+*****
+Read graphs in Pajek format.
+
+This implementation handles directed and undirected graphs including
+those with self loops and parallel edges.
+
+Format
+------
+See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+for format information.
+
+"""
+
+import warnings
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = ["read_pajek", "parse_pajek", "generate_pajek", "write_pajek"]
+
+
+def generate_pajek(G):
+    """Generate lines in Pajek graph format.
+
+    Parameters
+    ----------
+    G : graph
+       A Networkx graph
+
+    References
+    ----------
+    See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+    for format information.
+    """
+    if G.name == "":
+        name = "NetworkX"
+    else:
+        name = G.name
+    # Apparently many Pajek format readers can't process this line
+    # So we'll leave it out for now.
+    # yield '*network %s'%name
+
+    # write nodes with attributes
+    yield f"*vertices {G.order()}"
+    nodes = list(G)
+    # make dictionary mapping nodes to integers
+    nodenumber = dict(zip(nodes, range(1, len(nodes) + 1)))
+    for n in nodes:
+        # copy node attributes and pop mandatory attributes
+        # to avoid duplication.
+        na = G.nodes.get(n, {}).copy()
+        x = na.pop("x", 0.0)
+        y = na.pop("y", 0.0)
+        try:
+            id = int(na.pop("id", nodenumber[n]))
+        except ValueError as err:
+            err.args += (
+                (
+                    "Pajek format requires 'id' to be an int()."
+                    " Refer to the 'Relabeling nodes' section."
+                ),
+            )
+            raise
+        nodenumber[n] = id
+        shape = na.pop("shape", "ellipse")
+        s = " ".join(map(make_qstr, (id, n, x, y, shape)))
+        # only optional attributes are left in na.
+        for k, v in na.items():
+            if isinstance(v, str) and v.strip() != "":
+                s += f" {make_qstr(k)} {make_qstr(v)}"
+            else:
+                warnings.warn(
+                    f"Node attribute {k} is not processed. {('Empty attribute' if isinstance(v, str) else 'Non-string attribute')}."
+                )
+        yield s
+
+    # write edges with attributes
+    if G.is_directed():
+        yield "*arcs"
+    else:
+        yield "*edges"
+    for u, v, edgedata in G.edges(data=True):
+        d = edgedata.copy()
+        value = d.pop("weight", 1.0)  # use 1 as default edge value
+        s = " ".join(map(make_qstr, (nodenumber[u], nodenumber[v], value)))
+        for k, v in d.items():
+            if isinstance(v, str) and v.strip() != "":
+                s += f" {make_qstr(k)} {make_qstr(v)}"
+            else:
+                warnings.warn(
+                    f"Edge attribute {k} is not processed. {('Empty attribute' if isinstance(v, str) else 'Non-string attribute')}."
+                )
+        yield s
+
+
+@open_file(1, mode="wb")
+def write_pajek(G, path, encoding="UTF-8"):
+    """Write graph in Pajek format to path.
+
+    Parameters
+    ----------
+    G : graph
+       A Networkx graph
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_pajek(G, "test.netP4")
+
+    Warnings
+    --------
+    Optional node attributes and edge attributes must be non-empty strings.
+    Otherwise it will not be written into the file. You will need to
+    convert those attributes to strings if you want to keep them.
+
+    References
+    ----------
+    See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+    for format information.
+    """
+    for line in generate_pajek(G):
+        line += "\n"
+        path.write(line.encode(encoding))
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_pajek(path, encoding="UTF-8"):
+    """Read graph in Pajek format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    Returns
+    -------
+    G : NetworkX MultiGraph or MultiDiGraph.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> nx.write_pajek(G, "test.net")
+    >>> G = nx.read_pajek("test.net")
+
+    To create a Graph instead of a MultiGraph use
+
+    >>> G1 = nx.Graph(G)
+
+    References
+    ----------
+    See http://vlado.fmf.uni-lj.si/pub/networks/pajek/doc/draweps.htm
+    for format information.
+    """
+    lines = (line.decode(encoding) for line in path)
+    return parse_pajek(lines)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def parse_pajek(lines):
+    """Parse Pajek format graph from string or iterable.
+
+    Parameters
+    ----------
+    lines : string or iterable
+       Data in Pajek format.
+
+    Returns
+    -------
+    G : NetworkX graph
+
+    See Also
+    --------
+    read_pajek
+
+    """
+    import shlex
+
+    # multigraph=False
+    if isinstance(lines, str):
+        lines = iter(lines.split("\n"))
+    lines = iter([line.rstrip("\n") for line in lines])
+    G = nx.MultiDiGraph()  # are multiedges allowed in Pajek? assume yes
+    labels = []  # in the order of the file, needed for matrix
+    while lines:
+        try:
+            l = next(lines)
+        except:  # EOF
+            break
+        if l.lower().startswith("*network"):
+            try:
+                label, name = l.split(None, 1)
+            except ValueError:
+                # Line was not of the form:  *network NAME
+                pass
+            else:
+                G.graph["name"] = name
+        elif l.lower().startswith("*vertices"):
+            nodelabels = {}
+            l, nnodes = l.split()
+            for i in range(int(nnodes)):
+                l = next(lines)
+                try:
+                    splitline = [
+                        x.decode("utf-8") for x in shlex.split(str(l).encode("utf-8"))
+                    ]
+                except AttributeError:
+                    splitline = shlex.split(str(l))
+                id, label = splitline[0:2]
+                labels.append(label)
+                G.add_node(label)
+                nodelabels[id] = label
+                G.nodes[label]["id"] = id
+                try:
+                    x, y, shape = splitline[2:5]
+                    G.nodes[label].update(
+                        {"x": float(x), "y": float(y), "shape": shape}
+                    )
+                except:
+                    pass
+                extra_attr = zip(splitline[5::2], splitline[6::2])
+                G.nodes[label].update(extra_attr)
+        elif l.lower().startswith("*edges") or l.lower().startswith("*arcs"):
+            if l.lower().startswith("*edge"):
+                # switch from multidigraph to multigraph
+                G = nx.MultiGraph(G)
+            if l.lower().startswith("*arcs"):
+                # switch to directed with multiple arcs for each existing edge
+                G = G.to_directed()
+            for l in lines:
+                try:
+                    splitline = [
+                        x.decode("utf-8") for x in shlex.split(str(l).encode("utf-8"))
+                    ]
+                except AttributeError:
+                    splitline = shlex.split(str(l))
+
+                if len(splitline) < 2:
+                    continue
+                ui, vi = splitline[0:2]
+                u = nodelabels.get(ui, ui)
+                v = nodelabels.get(vi, vi)
+                # parse the data attached to this edge and put in a dictionary
+                edge_data = {}
+                try:
+                    # there should always be a single value on the edge?
+                    w = splitline[2:3]
+                    edge_data.update({"weight": float(w[0])})
+                except:
+                    pass
+                    # if there isn't, just assign a 1
+                #                    edge_data.update({'value':1})
+                extra_attr = zip(splitline[3::2], splitline[4::2])
+                edge_data.update(extra_attr)
+                # if G.has_edge(u,v):
+                #     multigraph=True
+                G.add_edge(u, v, **edge_data)
+        elif l.lower().startswith("*matrix"):
+            G = nx.DiGraph(G)
+            adj_list = (
+                (labels[row], labels[col], {"weight": int(data)})
+                for (row, line) in enumerate(lines)
+                for (col, data) in enumerate(line.split())
+                if int(data) != 0
+            )
+            G.add_edges_from(adj_list)
+
+    return G
+
+
+def make_qstr(t):
+    """Returns the string representation of t.
+    Add outer double-quotes if the string has a space.
+    """
+    if not isinstance(t, str):
+        t = str(t)
+    if " " in t:
+        t = f'"{t}"'
+    return t
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/sparse6.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/sparse6.py
new file mode 100644
index 0000000000000000000000000000000000000000..b82ae5c34d30e1a1e72f0e94e6845072d18296f9
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/sparse6.py
@@ -0,0 +1,379 @@
+# Original author: D. Eppstein, UC Irvine, August 12, 2003.
+# The original code at https://www.ics.uci.edu/~eppstein/PADS/ is public domain.
+"""Functions for reading and writing graphs in the *sparse6* format.
+
+The *sparse6* file format is a space-efficient format for large sparse
+graphs. For small graphs or large dense graphs, use the *graph6* file
+format.
+
+For more information, see the `sparse6`_ homepage.
+
+.. _sparse6: https://users.cecs.anu.edu.au/~bdm/data/formats.html
+
+"""
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.readwrite.graph6 import data_to_n, n_to_data
+from networkx.utils import not_implemented_for, open_file
+
+__all__ = ["from_sparse6_bytes", "read_sparse6", "to_sparse6_bytes", "write_sparse6"]
+
+
+def _generate_sparse6_bytes(G, nodes, header):
+    """Yield bytes in the sparse6 encoding of a graph.
+
+    `G` is an undirected simple graph. `nodes` is the list of nodes for
+    which the node-induced subgraph will be encoded; if `nodes` is the
+    list of all nodes in the graph, the entire graph will be
+    encoded. `header` is a Boolean that specifies whether to generate
+    the header ``b'>>sparse6<<'`` before the remaining data.
+
+    This function generates `bytes` objects in the following order:
+
+    1. the header (if requested),
+    2. the encoding of the number of nodes,
+    3. each character, one-at-a-time, in the encoding of the requested
+       node-induced subgraph,
+    4. a newline character.
+
+    This function raises :exc:`ValueError` if the graph is too large for
+    the graph6 format (that is, greater than ``2 ** 36`` nodes).
+
+    """
+    n = len(G)
+    if n >= 2**36:
+        raise ValueError(
+            "sparse6 is only defined if number of nodes is less than 2 ** 36"
+        )
+    if header:
+        yield b">>sparse6<<"
+    yield b":"
+    for d in n_to_data(n):
+        yield str.encode(chr(d + 63))
+
+    k = 1
+    while 1 << k < n:
+        k += 1
+
+    def enc(x):
+        """Big endian k-bit encoding of x"""
+        return [1 if (x & 1 << (k - 1 - i)) else 0 for i in range(k)]
+
+    edges = sorted((max(u, v), min(u, v)) for u, v in G.edges())
+    bits = []
+    curv = 0
+    for v, u in edges:
+        if v == curv:  # current vertex edge
+            bits.append(0)
+            bits.extend(enc(u))
+        elif v == curv + 1:  # next vertex edge
+            curv += 1
+            bits.append(1)
+            bits.extend(enc(u))
+        else:  # skip to vertex v and then add edge to u
+            curv = v
+            bits.append(1)
+            bits.extend(enc(v))
+            bits.append(0)
+            bits.extend(enc(u))
+    if k < 6 and n == (1 << k) and ((-len(bits)) % 6) >= k and curv < (n - 1):
+        # Padding special case: small k, n=2^k,
+        # more than k bits of padding needed,
+        # current vertex is not (n-1) --
+        # appending 1111... would add a loop on (n-1)
+        bits.append(0)
+        bits.extend([1] * ((-len(bits)) % 6))
+    else:
+        bits.extend([1] * ((-len(bits)) % 6))
+
+    data = [
+        (bits[i + 0] << 5)
+        + (bits[i + 1] << 4)
+        + (bits[i + 2] << 3)
+        + (bits[i + 3] << 2)
+        + (bits[i + 4] << 1)
+        + (bits[i + 5] << 0)
+        for i in range(0, len(bits), 6)
+    ]
+
+    for d in data:
+        yield str.encode(chr(d + 63))
+    yield b"\n"
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_sparse6_bytes(string):
+    """Read an undirected graph in sparse6 format from string.
+
+    Parameters
+    ----------
+    string : string
+       Data in sparse6 format
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If the string is unable to be parsed in sparse6 format
+
+    Examples
+    --------
+    >>> G = nx.from_sparse6_bytes(b":A_")
+    >>> sorted(G.edges())
+    [(0, 1), (0, 1), (0, 1)]
+
+    See Also
+    --------
+    read_sparse6, write_sparse6
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if string.startswith(b">>sparse6<<"):
+        string = string[11:]
+    if not string.startswith(b":"):
+        raise NetworkXError("Expected leading colon in sparse6")
+
+    chars = [c - 63 for c in string[1:]]
+    n, data = data_to_n(chars)
+    k = 1
+    while 1 << k < n:
+        k += 1
+
+    def parseData():
+        """Returns stream of pairs b[i], x[i] for sparse6 format."""
+        chunks = iter(data)
+        d = None  # partial data word
+        dLen = 0  # how many unparsed bits are left in d
+
+        while 1:
+            if dLen < 1:
+                try:
+                    d = next(chunks)
+                except StopIteration:
+                    return
+                dLen = 6
+            dLen -= 1
+            b = (d >> dLen) & 1  # grab top remaining bit
+
+            x = d & ((1 << dLen) - 1)  # partially built up value of x
+            xLen = dLen  # how many bits included so far in x
+            while xLen < k:  # now grab full chunks until we have enough
+                try:
+                    d = next(chunks)
+                except StopIteration:
+                    return
+                dLen = 6
+                x = (x << 6) + d
+                xLen += 6
+            x = x >> (xLen - k)  # shift back the extra bits
+            dLen = xLen - k
+            yield b, x
+
+    v = 0
+
+    G = nx.MultiGraph()
+    G.add_nodes_from(range(n))
+
+    multigraph = False
+    for b, x in parseData():
+        if b == 1:
+            v += 1
+        # padding with ones can cause overlarge number here
+        if x >= n or v >= n:
+            break
+        elif x > v:
+            v = x
+        else:
+            if G.has_edge(x, v):
+                multigraph = True
+            G.add_edge(x, v)
+    if not multigraph:
+        G = nx.Graph(G)
+    return G
+
+
+def to_sparse6_bytes(G, nodes=None, header=True):
+    """Convert an undirected graph to bytes in sparse6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by ``G.nodes()`` is used.
+
+    header: bool
+       If True add '>>sparse6<<' bytes to head of data.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed.
+
+    ValueError
+        If the graph has at least ``2 ** 36`` nodes; the sparse6 format
+        is only defined for graphs of order less than ``2 ** 36``.
+
+    Examples
+    --------
+    >>> nx.to_sparse6_bytes(nx.path_graph(2))
+    b'>>sparse6<<:An\\n'
+
+    See Also
+    --------
+    to_sparse6_bytes, read_sparse6, write_sparse6_bytes
+
+    Notes
+    -----
+    The returned bytes end with a newline character.
+
+    The format does not support edge or node labels.
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    G = nx.convert_node_labels_to_integers(G, ordering="sorted")
+    return b"".join(_generate_sparse6_bytes(G, nodes, header))
+
+
+@open_file(0, mode="rb")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def read_sparse6(path):
+    """Read an undirected graph in sparse6 format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       Filename or file handle to read.
+       Filenames ending in .gz or .bz2 will be decompressed.
+
+    Returns
+    -------
+    G : Graph/Multigraph or list of Graphs/MultiGraphs
+       If the file contains multiple lines then a list of graphs is returned
+
+    Raises
+    ------
+    NetworkXError
+        If the string is unable to be parsed in sparse6 format
+
+    Examples
+    --------
+    You can read a sparse6 file by giving the path to the file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     _ = f.write(b">>sparse6<<:An\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_sparse6(f.name)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    You can also read a sparse6 file by giving an open file-like object::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     _ = f.write(b">>sparse6<<:An\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_sparse6(f)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    See Also
+    --------
+    read_sparse6, from_sparse6_bytes
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    glist = []
+    for line in path:
+        line = line.strip()
+        if not len(line):
+            continue
+        glist.append(from_sparse6_bytes(line))
+    if len(glist) == 1:
+        return glist[0]
+    else:
+        return glist
+
+
+@not_implemented_for("directed")
+@open_file(1, mode="wb")
+def write_sparse6(G, path, nodes=None, header=True):
+    """Write graph G to given path in sparse6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    path : file or string
+       File or filename to write.
+       Filenames ending in .gz or .bz2 will be compressed.
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by G.nodes() is used.
+
+    header: bool
+       If True add '>>sparse6<<' string to head of data
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is directed
+
+    Examples
+    --------
+    You can write a sparse6 file by giving the path to the file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     nx.write_sparse6(nx.path_graph(2), f.name)
+        ...     print(f.read())
+        b'>>sparse6<<:An\\n'
+
+    You can also write a sparse6 file by giving an open file-like object::
+
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     nx.write_sparse6(nx.path_graph(2), f)
+        ...     _ = f.seek(0)
+        ...     print(f.read())
+        b'>>sparse6<<:An\\n'
+
+    See Also
+    --------
+    read_sparse6, from_sparse6_bytes
+
+    Notes
+    -----
+    The format does not support edge or node labels.
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           <https://users.cecs.anu.edu.au/~bdm/data/formats.html>
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    G = nx.convert_node_labels_to_integers(G, ordering="sorted")
+    for b in _generate_sparse6_bytes(G, nodes, header):
+        path.write(b)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/text.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/text.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fce220764d0e3aab0dff0200f3d7b601d03d007
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/readwrite/text.py
@@ -0,0 +1,851 @@
+"""
+Text-based visual representations of graphs
+"""
+
+import sys
+from collections import defaultdict
+
+import networkx as nx
+from networkx.utils import open_file
+
+__all__ = ["generate_network_text", "write_network_text"]
+
+
+class BaseGlyphs:
+    @classmethod
+    def as_dict(cls):
+        return {
+            a: getattr(cls, a)
+            for a in dir(cls)
+            if not a.startswith("_") and a != "as_dict"
+        }
+
+
+class AsciiBaseGlyphs(BaseGlyphs):
+    empty: str = "+"
+    newtree_last: str = "+-- "
+    newtree_mid: str = "+-- "
+    endof_forest: str = "    "
+    within_forest: str = ":   "
+    within_tree: str = "|   "
+
+
+class AsciiDirectedGlyphs(AsciiBaseGlyphs):
+    last: str = "L-> "
+    mid: str = "|-> "
+    backedge: str = "<-"
+    vertical_edge: str = "!"
+
+
+class AsciiUndirectedGlyphs(AsciiBaseGlyphs):
+    last: str = "L-- "
+    mid: str = "|-- "
+    backedge: str = "-"
+    vertical_edge: str = "|"
+
+
+class UtfBaseGlyphs(BaseGlyphs):
+    # Notes on available box and arrow characters
+    # https://en.wikipedia.org/wiki/Box-drawing_character
+    # https://stackoverflow.com/questions/2701192/triangle-arrow
+    empty: str = "╙"
+    newtree_last: str = "╙── "
+    newtree_mid: str = "╟── "
+    endof_forest: str = "    "
+    within_forest: str = "╎   "
+    within_tree: str = "│   "
+
+
+class UtfDirectedGlyphs(UtfBaseGlyphs):
+    last: str = "└─╼ "
+    mid: str = "├─╼ "
+    backedge: str = "╾"
+    vertical_edge: str = "╽"
+
+
+class UtfUndirectedGlyphs(UtfBaseGlyphs):
+    last: str = "└── "
+    mid: str = "├── "
+    backedge: str = "─"
+    vertical_edge: str = "│"
+
+
+def generate_network_text(
+    graph,
+    with_labels=True,
+    sources=None,
+    max_depth=None,
+    ascii_only=False,
+    vertical_chains=False,
+):
+    """Generate lines in the "network text" format
+
+    This works via a depth-first traversal of the graph and writing a line for
+    each unique node encountered. Non-tree edges are written to the right of
+    each node, and connection to a non-tree edge is indicated with an ellipsis.
+    This representation works best when the input graph is a forest, but any
+    graph can be represented.
+
+    This notation is original to networkx, although it is simple enough that it
+    may be known in existing literature. See #5602 for details. The procedure
+    is summarized as follows:
+
+    1. Given a set of source nodes (which can be specified, or automatically
+    discovered via finding the (strongly) connected components and choosing one
+    node with minimum degree from each), we traverse the graph in depth first
+    order.
+
+    2. Each reachable node will be printed exactly once on it's own line.
+
+    3. Edges are indicated in one of four ways:
+
+        a. a parent "L-style" connection on the upper left. This corresponds to
+        a traversal in the directed DFS tree.
+
+        b. a backref "<-style" connection shown directly on the right. For
+        directed graphs, these are drawn for any incoming edges to a node that
+        is not a parent edge. For undirected graphs, these are drawn for only
+        the non-parent edges that have already been represented (The edges that
+        have not been represented will be handled in the recursive case).
+
+        c. a child "L-style" connection on the lower right. Drawing of the
+        children are handled recursively.
+
+        d. if ``vertical_chains`` is true, and a parent node only has one child
+        a "vertical-style" edge is drawn between them.
+
+    4. The children of each node (wrt the directed DFS tree) are drawn
+    underneath and to the right of it. In the case that a child node has already
+    been drawn the connection is replaced with an ellipsis ("...") to indicate
+    that there is one or more connections represented elsewhere.
+
+    5. If a maximum depth is specified, an edge to nodes past this maximum
+    depth will be represented by an ellipsis.
+
+    6. If a node has a truthy "collapse" value, then we do not traverse past
+    that node.
+
+    Parameters
+    ----------
+    graph : nx.DiGraph | nx.Graph
+        Graph to represent
+
+    with_labels : bool | str
+        If True will use the "label" attribute of a node to display if it
+        exists otherwise it will use the node value itself. If given as a
+        string, then that attribute name will be used instead of "label".
+        Defaults to True.
+
+    sources : List
+        Specifies which nodes to start traversal from. Note: nodes that are not
+        reachable from one of these sources may not be shown. If unspecified,
+        the minimal set of nodes needed to reach all others will be used.
+
+    max_depth : int | None
+        The maximum depth to traverse before stopping. Defaults to None.
+
+    ascii_only : Boolean
+        If True only ASCII characters are used to construct the visualization
+
+    vertical_chains : Boolean
+        If True, chains of nodes will be drawn vertically when possible.
+
+    Yields
+    ------
+    str : a line of generated text
+
+    Examples
+    --------
+    >>> graph = nx.path_graph(10)
+    >>> graph.add_node("A")
+    >>> graph.add_node("B")
+    >>> graph.add_node("C")
+    >>> graph.add_node("D")
+    >>> graph.add_edge(9, "A")
+    >>> graph.add_edge(9, "B")
+    >>> graph.add_edge(9, "C")
+    >>> graph.add_edge("C", "D")
+    >>> graph.add_edge("C", "E")
+    >>> graph.add_edge("C", "F")
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        └── 1
+            └── 2
+                └── 3
+                    └── 4
+                        └── 5
+                            └── 6
+                                └── 7
+                                    └── 8
+                                        └── 9
+                                            ├── A
+                                            ├── B
+                                            └── C
+                                                ├── D
+                                                ├── E
+                                                └── F
+    >>> nx.write_network_text(graph, vertical_chains=True)
+    ╙── 0
+        │
+        1
+        │
+        2
+        │
+        3
+        │
+        4
+        │
+        5
+        │
+        6
+        │
+        7
+        │
+        8
+        │
+        9
+        ├── A
+        ├── B
+        └── C
+            ├── D
+            ├── E
+            └── F
+    """
+    from typing import Any, NamedTuple
+
+    class StackFrame(NamedTuple):
+        parent: Any
+        node: Any
+        indents: list
+        this_islast: bool
+        this_vertical: bool
+
+    collapse_attr = "collapse"
+
+    is_directed = graph.is_directed()
+
+    if is_directed:
+        glyphs = AsciiDirectedGlyphs if ascii_only else UtfDirectedGlyphs
+        succ = graph.succ
+        pred = graph.pred
+    else:
+        glyphs = AsciiUndirectedGlyphs if ascii_only else UtfUndirectedGlyphs
+        succ = graph.adj
+        pred = graph.adj
+
+    if isinstance(with_labels, str):
+        label_attr = with_labels
+    elif with_labels:
+        label_attr = "label"
+    else:
+        label_attr = None
+
+    if max_depth == 0:
+        yield glyphs.empty + " ..."
+    elif len(graph.nodes) == 0:
+        yield glyphs.empty
+    else:
+        # If the nodes to traverse are unspecified, find the minimal set of
+        # nodes that will reach the entire graph
+        if sources is None:
+            sources = _find_sources(graph)
+
+        # Populate the stack with each:
+        # 1. parent node in the DFS tree (or None for root nodes),
+        # 2. the current node in the DFS tree
+        # 2. a list of indentations indicating depth
+        # 3. a flag indicating if the node is the final one to be written.
+        # Reverse the stack so sources are popped in the correct order.
+        last_idx = len(sources) - 1
+        stack = [
+            StackFrame(None, node, [], (idx == last_idx), False)
+            for idx, node in enumerate(sources)
+        ][::-1]
+
+        num_skipped_children = defaultdict(lambda: 0)
+        seen_nodes = set()
+        while stack:
+            parent, node, indents, this_islast, this_vertical = stack.pop()
+
+            if node is not Ellipsis:
+                skip = node in seen_nodes
+                if skip:
+                    # Mark that we skipped a parent's child
+                    num_skipped_children[parent] += 1
+
+                if this_islast:
+                    # If we reached the last child of a parent, and we skipped
+                    # any of that parents children, then we should emit an
+                    # ellipsis at the end after this.
+                    if num_skipped_children[parent] and parent is not None:
+                        # Append the ellipsis to be emitted last
+                        next_islast = True
+                        try_frame = StackFrame(
+                            node, Ellipsis, indents, next_islast, False
+                        )
+                        stack.append(try_frame)
+
+                        # Redo this frame, but not as a last object
+                        next_islast = False
+                        try_frame = StackFrame(
+                            parent, node, indents, next_islast, this_vertical
+                        )
+                        stack.append(try_frame)
+                        continue
+
+                if skip:
+                    continue
+                seen_nodes.add(node)
+
+            if not indents:
+                # Top level items (i.e. trees in the forest) get different
+                # glyphs to indicate they are not actually connected
+                if this_islast:
+                    this_vertical = False
+                    this_prefix = indents + [glyphs.newtree_last]
+                    next_prefix = indents + [glyphs.endof_forest]
+                else:
+                    this_prefix = indents + [glyphs.newtree_mid]
+                    next_prefix = indents + [glyphs.within_forest]
+
+            else:
+                # Non-top-level items
+                if this_vertical:
+                    this_prefix = indents
+                    next_prefix = indents
+                else:
+                    if this_islast:
+                        this_prefix = indents + [glyphs.last]
+                        next_prefix = indents + [glyphs.endof_forest]
+                    else:
+                        this_prefix = indents + [glyphs.mid]
+                        next_prefix = indents + [glyphs.within_tree]
+
+            if node is Ellipsis:
+                label = " ..."
+                suffix = ""
+                children = []
+            else:
+                if label_attr is not None:
+                    label = str(graph.nodes[node].get(label_attr, node))
+                else:
+                    label = str(node)
+
+                # Determine if we want to show the children of this node.
+                if collapse_attr is not None:
+                    collapse = graph.nodes[node].get(collapse_attr, False)
+                else:
+                    collapse = False
+
+                # Determine:
+                # (1) children to traverse into after showing this node.
+                # (2) parents to immediately show to the right of this node.
+                if is_directed:
+                    # In the directed case we must show every successor node
+                    # note: it may be skipped later, but we don't have that
+                    # information here.
+                    children = list(succ[node])
+                    # In the directed case we must show every predecessor
+                    # except for parent we directly traversed from.
+                    handled_parents = {parent}
+                else:
+                    # Showing only the unseen children results in a more
+                    # concise representation for the undirected case.
+                    children = [
+                        child for child in succ[node] if child not in seen_nodes
+                    ]
+
+                    # In the undirected case, parents are also children, so we
+                    # only need to immediately show the ones we can no longer
+                    # traverse
+                    handled_parents = {*children, parent}
+
+                if max_depth is not None and len(indents) == max_depth - 1:
+                    # Use ellipsis to indicate we have reached maximum depth
+                    if children:
+                        children = [Ellipsis]
+                    handled_parents = {parent}
+
+                if collapse:
+                    # Collapsing a node is the same as reaching maximum depth
+                    if children:
+                        children = [Ellipsis]
+                    handled_parents = {parent}
+
+                # The other parents are other predecessors of this node that
+                # are not handled elsewhere.
+                other_parents = [p for p in pred[node] if p not in handled_parents]
+                if other_parents:
+                    if label_attr is not None:
+                        other_parents_labels = ", ".join(
+                            [
+                                str(graph.nodes[p].get(label_attr, p))
+                                for p in other_parents
+                            ]
+                        )
+                    else:
+                        other_parents_labels = ", ".join(
+                            [str(p) for p in other_parents]
+                        )
+                    suffix = " ".join(["", glyphs.backedge, other_parents_labels])
+                else:
+                    suffix = ""
+
+            # Emit the line for this node, this will be called for each node
+            # exactly once.
+            if this_vertical:
+                yield "".join(this_prefix + [glyphs.vertical_edge])
+
+            yield "".join(this_prefix + [label, suffix])
+
+            if vertical_chains:
+                if is_directed:
+                    num_children = len(set(children))
+                else:
+                    num_children = len(set(children) - {parent})
+                # The next node can be drawn vertically if it is the only
+                # remaining child of this node.
+                next_is_vertical = num_children == 1
+            else:
+                next_is_vertical = False
+
+            # Push children on the stack in reverse order so they are popped in
+            # the original order.
+            for idx, child in enumerate(children[::-1]):
+                next_islast = idx == 0
+                try_frame = StackFrame(
+                    node, child, next_prefix, next_islast, next_is_vertical
+                )
+                stack.append(try_frame)
+
+
+@open_file(1, "w")
+def write_network_text(
+    graph,
+    path=None,
+    with_labels=True,
+    sources=None,
+    max_depth=None,
+    ascii_only=False,
+    end="\n",
+    vertical_chains=False,
+):
+    """Creates a nice text representation of a graph
+
+    This works via a depth-first traversal of the graph and writing a line for
+    each unique node encountered. Non-tree edges are written to the right of
+    each node, and connection to a non-tree edge is indicated with an ellipsis.
+    This representation works best when the input graph is a forest, but any
+    graph can be represented.
+
+    Parameters
+    ----------
+    graph : nx.DiGraph | nx.Graph
+        Graph to represent
+
+    path : string or file or callable or None
+       Filename or file handle for data output.
+       if a function, then it will be called for each generated line.
+       if None, this will default to "sys.stdout.write"
+
+    with_labels : bool | str
+        If True will use the "label" attribute of a node to display if it
+        exists otherwise it will use the node value itself. If given as a
+        string, then that attribute name will be used instead of "label".
+        Defaults to True.
+
+    sources : List
+        Specifies which nodes to start traversal from. Note: nodes that are not
+        reachable from one of these sources may not be shown. If unspecified,
+        the minimal set of nodes needed to reach all others will be used.
+
+    max_depth : int | None
+        The maximum depth to traverse before stopping. Defaults to None.
+
+    ascii_only : Boolean
+        If True only ASCII characters are used to construct the visualization
+
+    end : string
+        The line ending character
+
+    vertical_chains : Boolean
+        If True, chains of nodes will be drawn vertically when possible.
+
+    Examples
+    --------
+    >>> graph = nx.balanced_tree(r=2, h=2, create_using=nx.DiGraph)
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        ├─╼ 1
+        │   ├─╼ 3
+        │   └─╼ 4
+        └─╼ 2
+            ├─╼ 5
+            └─╼ 6
+
+    >>> # A near tree with one non-tree edge
+    >>> graph.add_edge(5, 1)
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        ├─╼ 1 ╾ 5
+        │   ├─╼ 3
+        │   └─╼ 4
+        └─╼ 2
+            ├─╼ 5
+            │   └─╼  ...
+            └─╼ 6
+
+    >>> graph = nx.cycle_graph(5)
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        ├── 1
+        │   └── 2
+        │       └── 3
+        │           └── 4 ─ 0
+        └──  ...
+
+    >>> graph = nx.cycle_graph(5, nx.DiGraph)
+    >>> nx.write_network_text(graph, vertical_chains=True)
+    ╙── 0 ╾ 4
+        ╽
+        1
+        ╽
+        2
+        ╽
+        3
+        ╽
+        4
+        └─╼  ...
+
+    >>> nx.write_network_text(graph, vertical_chains=True, ascii_only=True)
+    +-- 0 <- 4
+        !
+        1
+        !
+        2
+        !
+        3
+        !
+        4
+        L->  ...
+
+    >>> graph = nx.generators.barbell_graph(4, 2)
+    >>> nx.write_network_text(graph, vertical_chains=False)
+    ╙── 4
+        ├── 5
+        │   └── 6
+        │       ├── 7
+        │       │   ├── 8 ─ 6
+        │       │   │   └── 9 ─ 6, 7
+        │       │   └──  ...
+        │       └──  ...
+        └── 3
+            ├── 0
+            │   ├── 1 ─ 3
+            │   │   └── 2 ─ 0, 3
+            │   └──  ...
+            └──  ...
+    >>> nx.write_network_text(graph, vertical_chains=True)
+    ╙── 4
+        ├── 5
+        │   │
+        │   6
+        │   ├── 7
+        │   │   ├── 8 ─ 6
+        │   │   │   │
+        │   │   │   9 ─ 6, 7
+        │   │   └──  ...
+        │   └──  ...
+        └── 3
+            ├── 0
+            │   ├── 1 ─ 3
+            │   │   │
+            │   │   2 ─ 0, 3
+            │   └──  ...
+            └──  ...
+
+    >>> graph = nx.complete_graph(5, create_using=nx.Graph)
+    >>> nx.write_network_text(graph)
+    ╙── 0
+        ├── 1
+        │   ├── 2 ─ 0
+        │   │   ├── 3 ─ 0, 1
+        │   │   │   └── 4 ─ 0, 1, 2
+        │   │   └──  ...
+        │   └──  ...
+        └──  ...
+
+    >>> graph = nx.complete_graph(3, create_using=nx.DiGraph)
+    >>> nx.write_network_text(graph)
+    ╙── 0 ╾ 1, 2
+        ├─╼ 1 ╾ 2
+        │   ├─╼ 2 ╾ 0
+        │   │   └─╼  ...
+        │   └─╼  ...
+        └─╼  ...
+    """
+    if path is None:
+        # The path is unspecified, write to stdout
+        _write = sys.stdout.write
+    elif hasattr(path, "write"):
+        # The path is already an open file
+        _write = path.write
+    elif callable(path):
+        # The path is a custom callable
+        _write = path
+    else:
+        raise TypeError(type(path))
+
+    for line in generate_network_text(
+        graph,
+        with_labels=with_labels,
+        sources=sources,
+        max_depth=max_depth,
+        ascii_only=ascii_only,
+        vertical_chains=vertical_chains,
+    ):
+        _write(line + end)
+
+
+def _find_sources(graph):
+    """
+    Determine a minimal set of nodes such that the entire graph is reachable
+    """
+    # For each connected part of the graph, choose at least
+    # one node as a starting point, preferably without a parent
+    if graph.is_directed():
+        # Choose one node from each SCC with minimum in_degree
+        sccs = list(nx.strongly_connected_components(graph))
+        # condensing the SCCs forms a dag, the nodes in this graph with
+        # 0 in-degree correspond to the SCCs from which the minimum set
+        # of nodes from which all other nodes can be reached.
+        scc_graph = nx.condensation(graph, sccs)
+        supernode_to_nodes = {sn: [] for sn in scc_graph.nodes()}
+        # Note: the order of mapping differs between pypy and cpython
+        # so we have to loop over graph nodes for consistency
+        mapping = scc_graph.graph["mapping"]
+        for n in graph.nodes:
+            sn = mapping[n]
+            supernode_to_nodes[sn].append(n)
+        sources = []
+        for sn in scc_graph.nodes():
+            if scc_graph.in_degree[sn] == 0:
+                scc = supernode_to_nodes[sn]
+                node = min(scc, key=lambda n: graph.in_degree[n])
+                sources.append(node)
+    else:
+        # For undirected graph, the entire graph will be reachable as
+        # long as we consider one node from every connected component
+        sources = [
+            min(cc, key=lambda n: graph.degree[n])
+            for cc in nx.connected_components(graph)
+        ]
+        sources = sorted(sources, key=lambda n: graph.degree[n])
+    return sources
+
+
+def _parse_network_text(lines):
+    """Reconstructs a graph from a network text representation.
+
+    This is mainly used for testing.  Network text is for display, not
+    serialization, as such this cannot parse all network text representations
+    because node labels can be ambiguous with the glyphs and indentation used
+    to represent edge structure. Additionally, there is no way to determine if
+    disconnected graphs were originally directed or undirected.
+
+    Parameters
+    ----------
+    lines : list or iterator of strings
+        Input data in network text format
+
+    Returns
+    -------
+    G: NetworkX graph
+        The graph corresponding to the lines in network text format.
+    """
+    from itertools import chain
+    from typing import Any, NamedTuple
+
+    class ParseStackFrame(NamedTuple):
+        node: Any
+        indent: int
+        has_vertical_child: int | None
+
+    initial_line_iter = iter(lines)
+
+    is_ascii = None
+    is_directed = None
+
+    ##############
+    # Initial Pass
+    ##############
+
+    # Do an initial pass over the lines to determine what type of graph it is.
+    # Remember what these lines were, so we can reiterate over them in the
+    # parsing pass.
+    initial_lines = []
+    try:
+        first_line = next(initial_line_iter)
+    except StopIteration:
+        ...
+    else:
+        initial_lines.append(first_line)
+        # The first character indicates if it is an ASCII or UTF graph
+        first_char = first_line[0]
+        if first_char in {
+            UtfBaseGlyphs.empty,
+            UtfBaseGlyphs.newtree_mid[0],
+            UtfBaseGlyphs.newtree_last[0],
+        }:
+            is_ascii = False
+        elif first_char in {
+            AsciiBaseGlyphs.empty,
+            AsciiBaseGlyphs.newtree_mid[0],
+            AsciiBaseGlyphs.newtree_last[0],
+        }:
+            is_ascii = True
+        else:
+            raise AssertionError(f"Unexpected first character: {first_char}")
+
+    if is_ascii:
+        directed_glyphs = AsciiDirectedGlyphs.as_dict()
+        undirected_glyphs = AsciiUndirectedGlyphs.as_dict()
+    else:
+        directed_glyphs = UtfDirectedGlyphs.as_dict()
+        undirected_glyphs = UtfUndirectedGlyphs.as_dict()
+
+    # For both directed / undirected glyphs, determine which glyphs never
+    # appear as substrings in the other undirected / directed glyphs.  Glyphs
+    # with this property unambiguously indicates if a graph is directed /
+    # undirected.
+    directed_items = set(directed_glyphs.values())
+    undirected_items = set(undirected_glyphs.values())
+    unambiguous_directed_items = []
+    for item in directed_items:
+        other_items = undirected_items
+        other_supersets = [other for other in other_items if item in other]
+        if not other_supersets:
+            unambiguous_directed_items.append(item)
+    unambiguous_undirected_items = []
+    for item in undirected_items:
+        other_items = directed_items
+        other_supersets = [other for other in other_items if item in other]
+        if not other_supersets:
+            unambiguous_undirected_items.append(item)
+
+    for line in initial_line_iter:
+        initial_lines.append(line)
+        if any(item in line for item in unambiguous_undirected_items):
+            is_directed = False
+            break
+        elif any(item in line for item in unambiguous_directed_items):
+            is_directed = True
+            break
+
+    if is_directed is None:
+        # Not enough information to determine, choose undirected by default
+        is_directed = False
+
+    glyphs = directed_glyphs if is_directed else undirected_glyphs
+
+    # the backedge symbol by itself can be ambiguous, but with spaces around it
+    # becomes unambiguous.
+    backedge_symbol = " " + glyphs["backedge"] + " "
+
+    # Reconstruct an iterator over all of the lines.
+    parsing_line_iter = chain(initial_lines, initial_line_iter)
+
+    ##############
+    # Parsing Pass
+    ##############
+
+    edges = []
+    nodes = []
+    is_empty = None
+
+    noparent = object()  # sentinel value
+
+    # keep a stack of previous nodes that could be parents of subsequent nodes
+    stack = [ParseStackFrame(noparent, -1, None)]
+
+    for line in parsing_line_iter:
+        if line == glyphs["empty"]:
+            # If the line is the empty glyph, we are done.
+            # There shouldn't be anything else after this.
+            is_empty = True
+            continue
+
+        if backedge_symbol in line:
+            # This line has one or more backedges, separate those out
+            node_part, backedge_part = line.split(backedge_symbol)
+            backedge_nodes = [u.strip() for u in backedge_part.split(", ")]
+            # Now the node can be parsed
+            node_part = node_part.rstrip()
+            prefix, node = node_part.rsplit(" ", 1)
+            node = node.strip()
+            # Add the backedges to the edge list
+            edges.extend([(u, node) for u in backedge_nodes])
+        else:
+            # No backedge, the tail of this line is the node
+            prefix, node = line.rsplit(" ", 1)
+            node = node.strip()
+
+        prev = stack.pop()
+
+        if node in glyphs["vertical_edge"]:
+            # Previous node is still the previous node, but we know it will
+            # have exactly one child, which will need to have its nesting level
+            # adjusted.
+            modified_prev = ParseStackFrame(
+                prev.node,
+                prev.indent,
+                True,
+            )
+            stack.append(modified_prev)
+            continue
+
+        # The length of the string before the node characters give us a hint
+        # about our nesting level. The only case where this doesn't work is
+        # when there are vertical chains, which is handled explicitly.
+        indent = len(prefix)
+        curr = ParseStackFrame(node, indent, None)
+
+        if prev.has_vertical_child:
+            # In this case we know prev must be the parent of our current line,
+            # so we don't have to search the stack. (which is good because the
+            # indentation check wouldn't work in this case).
+            ...
+        else:
+            # If the previous node nesting-level is greater than the current
+            # nodes nesting-level than the previous node was the end of a path,
+            # and is not our parent. We can safely pop nodes off the stack
+            # until we find one with a comparable nesting-level, which is our
+            # parent.
+            while curr.indent <= prev.indent:
+                prev = stack.pop()
+
+        if node == "...":
+            # The current previous node is no longer a valid parent,
+            # keep it popped from the stack.
+            stack.append(prev)
+        else:
+            # The previous and current nodes may still be parents, so add them
+            # back onto the stack.
+            stack.append(prev)
+            stack.append(curr)
+
+            # Add the node and the edge to its parent to the node / edge lists.
+            nodes.append(curr.node)
+            if prev.node is not noparent:
+                edges.append((prev.node, curr.node))
+
+    if is_empty:
+        # Sanity check
+        assert len(nodes) == 0
+
+    # Reconstruct the graph
+    cls = nx.DiGraph if is_directed else nx.Graph
+    new = cls()
+    new.add_nodes_from(nodes)
+    new.add_edges_from(edges)
+    return new
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/__init__.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_all_random_functions.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_all_random_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb59159229f5808ea975002ced97201bef075d26
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_all_random_functions.py
@@ -0,0 +1,250 @@
+import random
+
+import pytest
+
+import networkx as nx
+from networkx.algorithms import approximation as approx
+from networkx.algorithms import threshold
+
+np = pytest.importorskip("numpy")
+
+progress = 0
+
+# store the random numbers after setting a global seed
+np.random.seed(42)
+np_rv = np.random.rand()
+random.seed(42)
+py_rv = random.random()
+
+
+def t(f, *args, **kwds):
+    """call one function and check if global RNG changed"""
+    global progress
+    progress += 1
+    print(progress, ",", end="")
+
+    f(*args, **kwds)
+
+    after_np_rv = np.random.rand()
+    # if np_rv != after_np_rv:
+    #    print(np_rv, after_np_rv, "don't match np!")
+    assert np_rv == after_np_rv
+    np.random.seed(42)
+
+    after_py_rv = random.random()
+    # if py_rv != after_py_rv:
+    #    print(py_rv, after_py_rv, "don't match py!")
+    assert py_rv == after_py_rv
+    random.seed(42)
+
+
+def run_all_random_functions(seed):
+    n = 20
+    m = 10
+    k = l = 2
+    s = v = 10
+    p = q = p1 = p2 = p_in = p_out = 0.4
+    alpha = radius = theta = 0.75
+    sizes = (20, 20, 10)
+    colors = [1, 2, 3]
+    G = nx.barbell_graph(12, 20)
+    H = nx.cycle_graph(3)
+    H.add_weighted_edges_from((u, v, 0.2) for u, v in H.edges)
+    deg_sequence = [3, 2, 1, 3, 2, 1, 3, 2, 1, 2, 1, 2, 1]
+    in_degree_sequence = w = sequence = aseq = bseq = deg_sequence
+
+    # print("starting...")
+    t(nx.maximal_independent_set, G, seed=seed)
+    t(nx.rich_club_coefficient, G, seed=seed, normalized=False)
+    t(nx.random_reference, G, seed=seed)
+    t(nx.lattice_reference, G, seed=seed)
+    t(nx.sigma, G, 1, 2, seed=seed)
+    t(nx.omega, G, 1, 2, seed=seed)
+    # print("out of smallworld.py")
+    t(nx.double_edge_swap, G, seed=seed)
+    # print("starting connected_double_edge_swap")
+    t(nx.connected_double_edge_swap, nx.complete_graph(9), seed=seed)
+    # print("ending connected_double_edge_swap")
+    t(nx.random_layout, G, seed=seed)
+    t(nx.fruchterman_reingold_layout, G, seed=seed)
+    t(nx.algebraic_connectivity, G, seed=seed)
+    t(nx.fiedler_vector, G, seed=seed)
+    t(nx.spectral_ordering, G, seed=seed)
+    # print('starting average_clustering')
+    t(approx.average_clustering, G, seed=seed)
+    t(approx.simulated_annealing_tsp, H, "greedy", source=1, seed=seed)
+    t(approx.threshold_accepting_tsp, H, "greedy", source=1, seed=seed)
+    t(
+        approx.traveling_salesman_problem,
+        H,
+        method=lambda G, weight: approx.simulated_annealing_tsp(
+            G, "greedy", weight, seed=seed
+        ),
+    )
+    t(
+        approx.traveling_salesman_problem,
+        H,
+        method=lambda G, weight: approx.threshold_accepting_tsp(
+            G, "greedy", weight, seed=seed
+        ),
+    )
+    t(nx.betweenness_centrality, G, seed=seed)
+    t(nx.edge_betweenness_centrality, G, seed=seed)
+    t(nx.approximate_current_flow_betweenness_centrality, G, seed=seed)
+    # print("kernighan")
+    t(nx.algorithms.community.kernighan_lin_bisection, G, seed=seed)
+    # nx.algorithms.community.asyn_lpa_communities(G, seed=seed)
+    t(nx.algorithms.tree.greedy_branching, G, seed=seed)
+    # print('done with graph argument functions')
+
+    t(nx.spectral_graph_forge, G, alpha, seed=seed)
+    t(nx.algorithms.community.asyn_fluidc, G, k, max_iter=1, seed=seed)
+    t(
+        nx.algorithms.connectivity.edge_augmentation.greedy_k_edge_augmentation,
+        G,
+        k,
+        seed=seed,
+    )
+    t(nx.algorithms.coloring.strategy_random_sequential, G, colors, seed=seed)
+
+    cs = ["d", "i", "i", "d", "d", "i"]
+    t(threshold.swap_d, cs, seed=seed)
+    t(nx.configuration_model, deg_sequence, seed=seed)
+    t(
+        nx.directed_configuration_model,
+        in_degree_sequence,
+        in_degree_sequence,
+        seed=seed,
+    )
+    t(nx.expected_degree_graph, w, seed=seed)
+    t(nx.random_degree_sequence_graph, sequence, seed=seed)
+    joint_degrees = {
+        1: {4: 1},
+        2: {2: 2, 3: 2, 4: 2},
+        3: {2: 2, 4: 1},
+        4: {1: 1, 2: 2, 3: 1},
+    }
+    t(nx.joint_degree_graph, joint_degrees, seed=seed)
+    joint_degree_sequence = [
+        (1, 0),
+        (1, 0),
+        (1, 0),
+        (2, 0),
+        (1, 0),
+        (2, 1),
+        (0, 1),
+        (0, 1),
+    ]
+    t(nx.random_clustered_graph, joint_degree_sequence, seed=seed)
+    constructor = [(3, 3, 0.5), (10, 10, 0.7)]
+    t(nx.random_shell_graph, constructor, seed=seed)
+    mapping = {1: 0.4, 2: 0.3, 3: 0.3}
+    t(nx.utils.random_weighted_sample, mapping, k, seed=seed)
+    t(nx.utils.weighted_choice, mapping, seed=seed)
+    t(nx.algorithms.bipartite.configuration_model, aseq, bseq, seed=seed)
+    t(nx.algorithms.bipartite.preferential_attachment_graph, aseq, p, seed=seed)
+
+    def kernel_integral(u, w, z):
+        return z - w
+
+    t(nx.random_kernel_graph, n, kernel_integral, seed=seed)
+
+    sizes = [75, 75, 300]
+    probs = [[0.25, 0.05, 0.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]]
+    t(nx.stochastic_block_model, sizes, probs, seed=seed)
+    t(nx.random_partition_graph, sizes, p_in, p_out, seed=seed)
+
+    # print("starting generator functions")
+    t(threshold.random_threshold_sequence, n, p, seed=seed)
+    t(nx.tournament.random_tournament, n, seed=seed)
+    t(nx.relaxed_caveman_graph, l, k, p, seed=seed)
+    t(nx.planted_partition_graph, l, k, p_in, p_out, seed=seed)
+    t(nx.gaussian_random_partition_graph, n, s, v, p_in, p_out, seed=seed)
+    t(nx.gn_graph, n, seed=seed)
+    t(nx.gnr_graph, n, p, seed=seed)
+    t(nx.gnc_graph, n, seed=seed)
+    t(nx.scale_free_graph, n, seed=seed)
+    t(nx.directed.random_uniform_k_out_graph, n, k, seed=seed)
+    t(nx.random_k_out_graph, n, k, alpha, seed=seed)
+    N = 1000
+    t(nx.partial_duplication_graph, N, n, p, q, seed=seed)
+    t(nx.duplication_divergence_graph, n, p, seed=seed)
+    t(nx.random_geometric_graph, n, radius, seed=seed)
+    t(nx.soft_random_geometric_graph, n, radius, seed=seed)
+    t(nx.geographical_threshold_graph, n, theta, seed=seed)
+    t(nx.waxman_graph, n, seed=seed)
+    t(nx.navigable_small_world_graph, n, seed=seed)
+    t(nx.thresholded_random_geometric_graph, n, radius, theta, seed=seed)
+    t(nx.uniform_random_intersection_graph, n, m, p, seed=seed)
+    t(nx.k_random_intersection_graph, n, m, k, seed=seed)
+
+    t(nx.general_random_intersection_graph, n, 2, [0.1, 0.5], seed=seed)
+    t(nx.fast_gnp_random_graph, n, p, seed=seed)
+    t(nx.gnp_random_graph, n, p, seed=seed)
+    t(nx.dense_gnm_random_graph, n, m, seed=seed)
+    t(nx.gnm_random_graph, n, m, seed=seed)
+    t(nx.newman_watts_strogatz_graph, n, k, p, seed=seed)
+    t(nx.watts_strogatz_graph, n, k, p, seed=seed)
+    t(nx.connected_watts_strogatz_graph, n, k, p, seed=seed)
+    t(nx.random_regular_graph, 3, n, seed=seed)
+    t(nx.barabasi_albert_graph, n, m, seed=seed)
+    t(nx.extended_barabasi_albert_graph, n, m, p, q, seed=seed)
+    t(nx.powerlaw_cluster_graph, n, m, p, seed=seed)
+    t(nx.random_lobster, n, p1, p2, seed=seed)
+    t(nx.random_powerlaw_tree, n, seed=seed, tries=5000)
+    t(nx.random_powerlaw_tree_sequence, 10, seed=seed, tries=5000)
+    t(nx.random_labeled_tree, n, seed=seed)
+    t(nx.utils.powerlaw_sequence, n, seed=seed)
+    t(nx.utils.zipf_rv, 2.3, seed=seed)
+    cdist = [0.2, 0.4, 0.5, 0.7, 0.9, 1.0]
+    t(nx.utils.discrete_sequence, n, cdistribution=cdist, seed=seed)
+    t(nx.algorithms.bipartite.random_graph, n, m, p, seed=seed)
+    t(nx.algorithms.bipartite.gnmk_random_graph, n, m, k, seed=seed)
+    LFR = nx.generators.LFR_benchmark_graph
+    t(
+        LFR,
+        25,
+        3,
+        1.5,
+        0.1,
+        average_degree=3,
+        min_community=10,
+        seed=seed,
+        max_community=20,
+    )
+    t(nx.random_internet_as_graph, n, seed=seed)
+    # print("done")
+
+
+# choose to test an integer seed, or whether a single RNG can be everywhere
+# np_rng = np.random.RandomState(14)
+# seed = np_rng
+# seed = 14
+
+
+@pytest.mark.slow
+# print("NetworkX Version:", nx.__version__)
+def test_rng_interface():
+    global progress
+
+    # try different kinds of seeds
+    for seed in [14, np.random.RandomState(14)]:
+        np.random.seed(42)
+        random.seed(42)
+        run_all_random_functions(seed)
+        progress = 0
+
+        # check that both global RNGs are unaffected
+        after_np_rv = np.random.rand()
+        #        if np_rv != after_np_rv:
+        #            print(np_rv, after_np_rv, "don't match np!")
+        assert np_rv == after_np_rv
+        after_py_rv = random.random()
+        #        if py_rv != after_py_rv:
+        #            print(py_rv, after_py_rv, "don't match py!")
+        assert py_rv == after_py_rv
+
+
+#        print("\nDone testing seed:", seed)
+
+# test_rng_interface()
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_convert.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..44bed9438945a39bb5eb85477301f58cfcd70cf0
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_convert.py
@@ -0,0 +1,321 @@
+import pytest
+
+import networkx as nx
+from networkx.convert import (
+    from_dict_of_dicts,
+    from_dict_of_lists,
+    to_dict_of_dicts,
+    to_dict_of_lists,
+    to_networkx_graph,
+)
+from networkx.generators.classic import barbell_graph, cycle_graph
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+
+class TestConvert:
+    def edgelists_equal(self, e1, e2):
+        return sorted(sorted(e) for e in e1) == sorted(sorted(e) for e in e2)
+
+    def test_simple_graphs(self):
+        for dest, source in [
+            (to_dict_of_dicts, from_dict_of_dicts),
+            (to_dict_of_lists, from_dict_of_lists),
+        ]:
+            G = barbell_graph(10, 3)
+            G.graph = {}
+            dod = dest(G)
+
+            # Dict of [dicts, lists]
+            GG = source(dod)
+            assert graphs_equal(G, GG)
+            GW = to_networkx_graph(dod)
+            assert graphs_equal(G, GW)
+            GI = nx.Graph(dod)
+            assert graphs_equal(G, GI)
+
+            # With nodelist keyword
+            P4 = nx.path_graph(4)
+            P3 = nx.path_graph(3)
+            P4.graph = {}
+            P3.graph = {}
+            dod = dest(P4, nodelist=[0, 1, 2])
+            Gdod = nx.Graph(dod)
+            assert graphs_equal(Gdod, P3)
+
+    def test_exceptions(self):
+        # NX graph
+        class G:
+            adj = None
+
+        pytest.raises(nx.NetworkXError, to_networkx_graph, G)
+
+        # pygraphviz  agraph
+        class G:
+            is_strict = None
+
+        pytest.raises(nx.NetworkXError, to_networkx_graph, G)
+
+        # Dict of [dicts, lists]
+        G = {"a": 0}
+        pytest.raises(TypeError, to_networkx_graph, G)
+
+        # list or generator of edges
+        class G:
+            next = None
+
+        pytest.raises(nx.NetworkXError, to_networkx_graph, G)
+
+        # no match
+        pytest.raises(nx.NetworkXError, to_networkx_graph, "a")
+
+    def test_digraphs(self):
+        for dest, source in [
+            (to_dict_of_dicts, from_dict_of_dicts),
+            (to_dict_of_lists, from_dict_of_lists),
+        ]:
+            G = cycle_graph(10)
+
+            # Dict of [dicts, lists]
+            dod = dest(G)
+            GG = source(dod)
+            assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
+            assert edges_equal(sorted(G.edges()), sorted(GG.edges()))
+            GW = to_networkx_graph(dod)
+            assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
+            assert edges_equal(sorted(G.edges()), sorted(GW.edges()))
+            GI = nx.Graph(dod)
+            assert nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
+            assert edges_equal(sorted(G.edges()), sorted(GI.edges()))
+
+            G = cycle_graph(10, create_using=nx.DiGraph)
+            dod = dest(G)
+            GG = source(dod, create_using=nx.DiGraph)
+            assert sorted(G.nodes()) == sorted(GG.nodes())
+            assert sorted(G.edges()) == sorted(GG.edges())
+            GW = to_networkx_graph(dod, create_using=nx.DiGraph)
+            assert sorted(G.nodes()) == sorted(GW.nodes())
+            assert sorted(G.edges()) == sorted(GW.edges())
+            GI = nx.DiGraph(dod)
+            assert sorted(G.nodes()) == sorted(GI.nodes())
+            assert sorted(G.edges()) == sorted(GI.edges())
+
+    def test_graph(self):
+        g = nx.cycle_graph(10)
+        G = nx.Graph()
+        G.add_nodes_from(g)
+        G.add_weighted_edges_from((u, v, u) for u, v in g.edges())
+
+        # Dict of dicts
+        dod = to_dict_of_dicts(G)
+        GG = from_dict_of_dicts(dod, create_using=nx.Graph)
+        assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(GG.edges()))
+        GW = to_networkx_graph(dod, create_using=nx.Graph)
+        assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(GW.edges()))
+        GI = nx.Graph(dod)
+        assert sorted(G.nodes()) == sorted(GI.nodes())
+        assert sorted(G.edges()) == sorted(GI.edges())
+
+        # Dict of lists
+        dol = to_dict_of_lists(G)
+        GG = from_dict_of_lists(dol, create_using=nx.Graph)
+        # dict of lists throws away edge data so set it to none
+        enone = [(u, v, {}) for (u, v, d) in G.edges(data=True)]
+        assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
+        assert edges_equal(enone, sorted(GG.edges(data=True)))
+        GW = to_networkx_graph(dol, create_using=nx.Graph)
+        assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
+        assert edges_equal(enone, sorted(GW.edges(data=True)))
+        GI = nx.Graph(dol)
+        assert nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
+        assert edges_equal(enone, sorted(GI.edges(data=True)))
+
+    def test_with_multiedges_self_loops(self):
+        G = cycle_graph(10)
+        XG = nx.Graph()
+        XG.add_nodes_from(G)
+        XG.add_weighted_edges_from((u, v, u) for u, v in G.edges())
+        XGM = nx.MultiGraph()
+        XGM.add_nodes_from(G)
+        XGM.add_weighted_edges_from((u, v, u) for u, v in G.edges())
+        XGM.add_edge(0, 1, weight=2)  # multiedge
+        XGS = nx.Graph()
+        XGS.add_nodes_from(G)
+        XGS.add_weighted_edges_from((u, v, u) for u, v in G.edges())
+        XGS.add_edge(0, 0, weight=100)  # self loop
+
+        # Dict of dicts
+        # with self loops, OK
+        dod = to_dict_of_dicts(XGS)
+        GG = from_dict_of_dicts(dod, create_using=nx.Graph)
+        assert nodes_equal(XGS.nodes(), GG.nodes())
+        assert edges_equal(XGS.edges(), GG.edges())
+        GW = to_networkx_graph(dod, create_using=nx.Graph)
+        assert nodes_equal(XGS.nodes(), GW.nodes())
+        assert edges_equal(XGS.edges(), GW.edges())
+        GI = nx.Graph(dod)
+        assert nodes_equal(XGS.nodes(), GI.nodes())
+        assert edges_equal(XGS.edges(), GI.edges())
+
+        # Dict of lists
+        # with self loops, OK
+        dol = to_dict_of_lists(XGS)
+        GG = from_dict_of_lists(dol, create_using=nx.Graph)
+        # dict of lists throws away edge data so set it to none
+        enone = [(u, v, {}) for (u, v, d) in XGS.edges(data=True)]
+        assert nodes_equal(sorted(XGS.nodes()), sorted(GG.nodes()))
+        assert edges_equal(enone, sorted(GG.edges(data=True)))
+        GW = to_networkx_graph(dol, create_using=nx.Graph)
+        assert nodes_equal(sorted(XGS.nodes()), sorted(GW.nodes()))
+        assert edges_equal(enone, sorted(GW.edges(data=True)))
+        GI = nx.Graph(dol)
+        assert nodes_equal(sorted(XGS.nodes()), sorted(GI.nodes()))
+        assert edges_equal(enone, sorted(GI.edges(data=True)))
+
+        # Dict of dicts
+        # with multiedges, OK
+        dod = to_dict_of_dicts(XGM)
+        GG = from_dict_of_dicts(dod, create_using=nx.MultiGraph, multigraph_input=True)
+        assert nodes_equal(sorted(XGM.nodes()), sorted(GG.nodes()))
+        assert edges_equal(sorted(XGM.edges()), sorted(GG.edges()))
+        GW = to_networkx_graph(dod, create_using=nx.MultiGraph, multigraph_input=True)
+        assert nodes_equal(sorted(XGM.nodes()), sorted(GW.nodes()))
+        assert edges_equal(sorted(XGM.edges()), sorted(GW.edges()))
+        GI = nx.MultiGraph(dod)
+        assert nodes_equal(sorted(XGM.nodes()), sorted(GI.nodes()))
+        assert sorted(XGM.edges()) == sorted(GI.edges())
+        GE = from_dict_of_dicts(dod, create_using=nx.MultiGraph, multigraph_input=False)
+        assert nodes_equal(sorted(XGM.nodes()), sorted(GE.nodes()))
+        assert sorted(XGM.edges()) != sorted(GE.edges())
+        GI = nx.MultiGraph(XGM)
+        assert nodes_equal(sorted(XGM.nodes()), sorted(GI.nodes()))
+        assert edges_equal(sorted(XGM.edges()), sorted(GI.edges()))
+        GM = nx.MultiGraph(G)
+        assert nodes_equal(sorted(GM.nodes()), sorted(G.nodes()))
+        assert edges_equal(sorted(GM.edges()), sorted(G.edges()))
+
+        # Dict of lists
+        # with multiedges, OK, but better write as DiGraph else you'll
+        # get double edges
+        dol = to_dict_of_lists(G)
+        GG = from_dict_of_lists(dol, create_using=nx.MultiGraph)
+        assert nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(GG.edges()))
+        GW = to_networkx_graph(dol, create_using=nx.MultiGraph)
+        assert nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(GW.edges()))
+        GI = nx.MultiGraph(dol)
+        assert nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(GI.edges()))
+
+    def test_edgelists(self):
+        P = nx.path_graph(4)
+        e = [(0, 1), (1, 2), (2, 3)]
+        G = nx.Graph(e)
+        assert nodes_equal(sorted(G.nodes()), sorted(P.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(P.edges()))
+        assert edges_equal(sorted(G.edges(data=True)), sorted(P.edges(data=True)))
+
+        e = [(0, 1, {}), (1, 2, {}), (2, 3, {})]
+        G = nx.Graph(e)
+        assert nodes_equal(sorted(G.nodes()), sorted(P.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(P.edges()))
+        assert edges_equal(sorted(G.edges(data=True)), sorted(P.edges(data=True)))
+
+        e = ((n, n + 1) for n in range(3))
+        G = nx.Graph(e)
+        assert nodes_equal(sorted(G.nodes()), sorted(P.nodes()))
+        assert edges_equal(sorted(G.edges()), sorted(P.edges()))
+        assert edges_equal(sorted(G.edges(data=True)), sorted(P.edges(data=True)))
+
+    def test_directed_to_undirected(self):
+        edges1 = [(0, 1), (1, 2), (2, 0)]
+        edges2 = [(0, 1), (1, 2), (0, 2)]
+        assert self.edgelists_equal(nx.Graph(nx.DiGraph(edges1)).edges(), edges1)
+        assert self.edgelists_equal(nx.Graph(nx.DiGraph(edges2)).edges(), edges1)
+        assert self.edgelists_equal(nx.MultiGraph(nx.DiGraph(edges1)).edges(), edges1)
+        assert self.edgelists_equal(nx.MultiGraph(nx.DiGraph(edges2)).edges(), edges1)
+
+        assert self.edgelists_equal(
+            nx.MultiGraph(nx.MultiDiGraph(edges1)).edges(), edges1
+        )
+        assert self.edgelists_equal(
+            nx.MultiGraph(nx.MultiDiGraph(edges2)).edges(), edges1
+        )
+
+        assert self.edgelists_equal(nx.Graph(nx.MultiDiGraph(edges1)).edges(), edges1)
+        assert self.edgelists_equal(nx.Graph(nx.MultiDiGraph(edges2)).edges(), edges1)
+
+    def test_attribute_dict_integrity(self):
+        # we must not replace dict-like graph data structures with dicts
+        G = nx.Graph()
+        G.add_nodes_from("abc")
+        H = to_networkx_graph(G, create_using=nx.Graph)
+        assert list(H.nodes) == list(G.nodes)
+        H = nx.DiGraph(G)
+        assert list(H.nodes) == list(G.nodes)
+
+    def test_to_edgelist(self):
+        G = nx.Graph([(1, 1)])
+        elist = nx.to_edgelist(G, nodelist=list(G))
+        assert edges_equal(G.edges(data=True), elist)
+
+    def test_custom_node_attr_dict_safekeeping(self):
+        class custom_dict(dict):
+            pass
+
+        class Custom(nx.Graph):
+            node_attr_dict_factory = custom_dict
+
+        g = nx.Graph()
+        g.add_node(1, weight=1)
+
+        h = Custom(g)
+        assert isinstance(g._node[1], dict)
+        assert isinstance(h._node[1], custom_dict)
+
+        # this raise exception
+        # h._node.update((n, dd.copy()) for n, dd in g.nodes.items())
+        # assert isinstance(h._node[1], custom_dict)
+
+
+@pytest.mark.parametrize(
+    "edgelist",
+    (
+        # Graph with no edge data
+        [(0, 1), (1, 2)],
+        # Graph with edge data
+        [(0, 1, {"weight": 1.0}), (1, 2, {"weight": 2.0})],
+    ),
+)
+def test_to_dict_of_dicts_with_edgedata_param(edgelist):
+    G = nx.Graph()
+    G.add_edges_from(edgelist)
+    # Innermost dict value == edge_data when edge_data != None.
+    # In the case when G has edge data, it is overwritten
+    expected = {0: {1: 10}, 1: {0: 10, 2: 10}, 2: {1: 10}}
+    assert nx.to_dict_of_dicts(G, edge_data=10) == expected
+
+
+def test_to_dict_of_dicts_with_edgedata_and_nodelist():
+    G = nx.path_graph(5)
+    nodelist = [2, 3, 4]
+    expected = {2: {3: 10}, 3: {2: 10, 4: 10}, 4: {3: 10}}
+    assert nx.to_dict_of_dicts(G, nodelist=nodelist, edge_data=10) == expected
+
+
+def test_to_dict_of_dicts_with_edgedata_multigraph():
+    """Multi edge data overwritten when edge_data != None"""
+    G = nx.MultiGraph()
+    G.add_edge(0, 1, key="a")
+    G.add_edge(0, 1, key="b")
+    # Multi edge data lost when edge_data is not None
+    expected = {0: {1: 10}, 1: {0: 10}}
+    assert nx.to_dict_of_dicts(G, edge_data=10) == expected
+
+
+def test_to_networkx_graph_non_edgelist():
+    invalid_edgelist = [1, 2, 3]
+    with pytest.raises(nx.NetworkXError, match="Input is not a valid edge list"):
+        nx.to_networkx_graph(invalid_edgelist)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_convert_numpy.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_convert_numpy.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a554fd4b2d5e00e69a0537fbd04e877a95b6593
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_convert_numpy.py
@@ -0,0 +1,531 @@
+import itertools
+
+import pytest
+
+import networkx as nx
+from networkx.utils import graphs_equal
+
+np = pytest.importorskip("numpy")
+npt = pytest.importorskip("numpy.testing")
+
+
+class TestConvertNumpyArray:
+    def setup_method(self):
+        self.G1 = nx.barbell_graph(10, 3)
+        self.G2 = nx.cycle_graph(10, create_using=nx.DiGraph)
+        self.G3 = self.create_weighted(nx.Graph())
+        self.G4 = self.create_weighted(nx.DiGraph())
+
+    def create_weighted(self, G):
+        g = nx.cycle_graph(4)
+        G.add_nodes_from(g)
+        G.add_weighted_edges_from((u, v, 10 + u) for u, v in g.edges())
+        return G
+
+    def assert_equal(self, G1, G2):
+        assert sorted(G1.nodes()) == sorted(G2.nodes())
+        assert sorted(G1.edges()) == sorted(G2.edges())
+
+    def identity_conversion(self, G, A, create_using):
+        assert A.sum() > 0
+        GG = nx.from_numpy_array(A, create_using=create_using)
+        self.assert_equal(G, GG)
+        GW = nx.to_networkx_graph(A, create_using=create_using)
+        self.assert_equal(G, GW)
+        GI = nx.empty_graph(0, create_using).__class__(A)
+        self.assert_equal(G, GI)
+
+    def test_shape(self):
+        "Conversion from non-square array."
+        A = np.array([[1, 2, 3], [4, 5, 6]])
+        pytest.raises(nx.NetworkXError, nx.from_numpy_array, A)
+
+    def test_identity_graph_array(self):
+        "Conversion from graph to array to graph."
+        A = nx.to_numpy_array(self.G1)
+        self.identity_conversion(self.G1, A, nx.Graph())
+
+    def test_identity_digraph_array(self):
+        """Conversion from digraph to array to digraph."""
+        A = nx.to_numpy_array(self.G2)
+        self.identity_conversion(self.G2, A, nx.DiGraph())
+
+    def test_identity_weighted_graph_array(self):
+        """Conversion from weighted graph to array to weighted graph."""
+        A = nx.to_numpy_array(self.G3)
+        self.identity_conversion(self.G3, A, nx.Graph())
+
+    def test_identity_weighted_digraph_array(self):
+        """Conversion from weighted digraph to array to weighted digraph."""
+        A = nx.to_numpy_array(self.G4)
+        self.identity_conversion(self.G4, A, nx.DiGraph())
+
+    def test_nodelist(self):
+        """Conversion from graph to array to graph with nodelist."""
+        P4 = nx.path_graph(4)
+        P3 = nx.path_graph(3)
+        nodelist = list(P3)
+        A = nx.to_numpy_array(P4, nodelist=nodelist)
+        GA = nx.Graph(A)
+        self.assert_equal(GA, P3)
+
+        # Make nodelist ambiguous by containing duplicates.
+        nodelist += [nodelist[0]]
+        pytest.raises(nx.NetworkXError, nx.to_numpy_array, P3, nodelist=nodelist)
+
+        # Make nodelist invalid by including nonexistent nodes
+        nodelist = [-1, 0, 1]
+        with pytest.raises(
+            nx.NetworkXError,
+            match=f"Nodes {nodelist - P3.nodes} in nodelist is not in G",
+        ):
+            nx.to_numpy_array(P3, nodelist=nodelist)
+
+    def test_weight_keyword(self):
+        WP4 = nx.Graph()
+        WP4.add_edges_from((n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3))
+        P4 = nx.path_graph(4)
+        A = nx.to_numpy_array(P4)
+        np.testing.assert_equal(A, nx.to_numpy_array(WP4, weight=None))
+        np.testing.assert_equal(0.5 * A, nx.to_numpy_array(WP4))
+        np.testing.assert_equal(0.3 * A, nx.to_numpy_array(WP4, weight="other"))
+
+    def test_from_numpy_array_type(self):
+        A = np.array([[1]])
+        G = nx.from_numpy_array(A)
+        assert isinstance(G[0][0]["weight"], int)
+
+        A = np.array([[1]]).astype(float)
+        G = nx.from_numpy_array(A)
+        assert isinstance(G[0][0]["weight"], float)
+
+        A = np.array([[1]]).astype(str)
+        G = nx.from_numpy_array(A)
+        assert isinstance(G[0][0]["weight"], str)
+
+        A = np.array([[1]]).astype(bool)
+        G = nx.from_numpy_array(A)
+        assert isinstance(G[0][0]["weight"], bool)
+
+        A = np.array([[1]]).astype(complex)
+        G = nx.from_numpy_array(A)
+        assert isinstance(G[0][0]["weight"], complex)
+
+        A = np.array([[1]]).astype(object)
+        pytest.raises(TypeError, nx.from_numpy_array, A)
+
+        A = np.array([[[1, 1, 1], [1, 1, 1]], [[1, 1, 1], [1, 1, 1]]])
+        with pytest.raises(
+            nx.NetworkXError, match=f"Input array must be 2D, not {A.ndim}"
+        ):
+            g = nx.from_numpy_array(A)
+
+    def test_from_numpy_array_dtype(self):
+        dt = [("weight", float), ("cost", int)]
+        A = np.array([[(1.0, 2)]], dtype=dt)
+        G = nx.from_numpy_array(A)
+        assert isinstance(G[0][0]["weight"], float)
+        assert isinstance(G[0][0]["cost"], int)
+        assert G[0][0]["cost"] == 2
+        assert G[0][0]["weight"] == 1.0
+
+    def test_from_numpy_array_parallel_edges(self):
+        """Tests that the :func:`networkx.from_numpy_array` function
+        interprets integer weights as the number of parallel edges when
+        creating a multigraph.
+
+        """
+        A = np.array([[1, 1], [1, 2]])
+        # First, with a simple graph, each integer entry in the adjacency
+        # matrix is interpreted as the weight of a single edge in the graph.
+        expected = nx.DiGraph()
+        edges = [(0, 0), (0, 1), (1, 0)]
+        expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
+        expected.add_edge(1, 1, weight=2)
+        actual = nx.from_numpy_array(A, parallel_edges=True, create_using=nx.DiGraph)
+        assert graphs_equal(actual, expected)
+        actual = nx.from_numpy_array(A, parallel_edges=False, create_using=nx.DiGraph)
+        assert graphs_equal(actual, expected)
+        # Now each integer entry in the adjacency matrix is interpreted as the
+        # number of parallel edges in the graph if the appropriate keyword
+        # argument is specified.
+        edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)]
+        expected = nx.MultiDiGraph()
+        expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
+        actual = nx.from_numpy_array(
+            A, parallel_edges=True, create_using=nx.MultiDiGraph
+        )
+        assert graphs_equal(actual, expected)
+        expected = nx.MultiDiGraph()
+        expected.add_edges_from(set(edges), weight=1)
+        # The sole self-loop (edge 0) on vertex 1 should have weight 2.
+        expected[1][1][0]["weight"] = 2
+        actual = nx.from_numpy_array(
+            A, parallel_edges=False, create_using=nx.MultiDiGraph
+        )
+        assert graphs_equal(actual, expected)
+
+    @pytest.mark.parametrize(
+        "dt",
+        (
+            None,  # default
+            int,  # integer dtype
+            np.dtype(
+                [("weight", "f8"), ("color", "i1")]
+            ),  # Structured dtype with named fields
+        ),
+    )
+    def test_from_numpy_array_no_edge_attr(self, dt):
+        A = np.array([[0, 1], [1, 0]], dtype=dt)
+        G = nx.from_numpy_array(A, edge_attr=None)
+        assert "weight" not in G.edges[0, 1]
+        assert len(G.edges[0, 1]) == 0
+
+    def test_from_numpy_array_multiedge_no_edge_attr(self):
+        A = np.array([[0, 2], [2, 0]])
+        G = nx.from_numpy_array(A, create_using=nx.MultiDiGraph, edge_attr=None)
+        assert all("weight" not in e for _, e in G[0][1].items())
+        assert len(G[0][1][0]) == 0
+
+    def test_from_numpy_array_custom_edge_attr(self):
+        A = np.array([[0, 2], [3, 0]])
+        G = nx.from_numpy_array(A, edge_attr="cost")
+        assert "weight" not in G.edges[0, 1]
+        assert G.edges[0, 1]["cost"] == 3
+
+    def test_symmetric(self):
+        """Tests that a symmetric array has edges added only once to an
+        undirected multigraph when using :func:`networkx.from_numpy_array`.
+
+        """
+        A = np.array([[0, 1], [1, 0]])
+        G = nx.from_numpy_array(A, create_using=nx.MultiGraph)
+        expected = nx.MultiGraph()
+        expected.add_edge(0, 1, weight=1)
+        assert graphs_equal(G, expected)
+
+    def test_dtype_int_graph(self):
+        """Test that setting dtype int actually gives an integer array.
+
+        For more information, see GitHub pull request #1363.
+
+        """
+        G = nx.complete_graph(3)
+        A = nx.to_numpy_array(G, dtype=int)
+        assert A.dtype == int
+
+    def test_dtype_int_multigraph(self):
+        """Test that setting dtype int actually gives an integer array.
+
+        For more information, see GitHub pull request #1363.
+
+        """
+        G = nx.MultiGraph(nx.complete_graph(3))
+        A = nx.to_numpy_array(G, dtype=int)
+        assert A.dtype == int
+
+
+@pytest.fixture
+def multigraph_test_graph():
+    G = nx.MultiGraph()
+    G.add_edge(1, 2, weight=7)
+    G.add_edge(1, 2, weight=70)
+    return G
+
+
+@pytest.mark.parametrize(("operator", "expected"), ((sum, 77), (min, 7), (max, 70)))
+def test_numpy_multigraph(multigraph_test_graph, operator, expected):
+    A = nx.to_numpy_array(multigraph_test_graph, multigraph_weight=operator)
+    assert A[1, 0] == expected
+
+
+def test_to_numpy_array_multigraph_nodelist(multigraph_test_graph):
+    G = multigraph_test_graph
+    G.add_edge(0, 1, weight=3)
+    A = nx.to_numpy_array(G, nodelist=[1, 2])
+    assert A.shape == (2, 2)
+    assert A[1, 0] == 77
+
+
+@pytest.mark.parametrize(
+    "G, expected",
+    [
+        (nx.Graph(), np.array([[0, 1 + 2j], [1 + 2j, 0]], dtype=complex)),
+        (nx.DiGraph(), np.array([[0, 1 + 2j], [0, 0]], dtype=complex)),
+    ],
+)
+def test_to_numpy_array_complex_weights(G, expected):
+    G.add_edge(0, 1, weight=1 + 2j)
+    A = nx.to_numpy_array(G, dtype=complex)
+    npt.assert_array_equal(A, expected)
+
+
+def test_to_numpy_array_arbitrary_weights():
+    G = nx.DiGraph()
+    w = 922337203685477580102  # Out of range for int64
+    G.add_edge(0, 1, weight=922337203685477580102)  # val not representable by int64
+    A = nx.to_numpy_array(G, dtype=object)
+    expected = np.array([[0, w], [0, 0]], dtype=object)
+    npt.assert_array_equal(A, expected)
+
+    # Undirected
+    A = nx.to_numpy_array(G.to_undirected(), dtype=object)
+    expected = np.array([[0, w], [w, 0]], dtype=object)
+    npt.assert_array_equal(A, expected)
+
+
+@pytest.mark.parametrize(
+    "func, expected",
+    ((min, -1), (max, 10), (sum, 11), (np.mean, 11 / 3), (np.median, 2)),
+)
+def test_to_numpy_array_multiweight_reduction(func, expected):
+    """Test various functions for reducing multiedge weights."""
+    G = nx.MultiDiGraph()
+    weights = [-1, 2, 10.0]
+    for w in weights:
+        G.add_edge(0, 1, weight=w)
+    A = nx.to_numpy_array(G, multigraph_weight=func, dtype=float)
+    assert np.allclose(A, [[0, expected], [0, 0]])
+
+    # Undirected case
+    A = nx.to_numpy_array(G.to_undirected(), multigraph_weight=func, dtype=float)
+    assert np.allclose(A, [[0, expected], [expected, 0]])
+
+
+@pytest.mark.parametrize(
+    ("G, expected"),
+    [
+        (nx.Graph(), [[(0, 0), (10, 5)], [(10, 5), (0, 0)]]),
+        (nx.DiGraph(), [[(0, 0), (10, 5)], [(0, 0), (0, 0)]]),
+    ],
+)
+def test_to_numpy_array_structured_dtype_attrs_from_fields(G, expected):
+    """When `dtype` is structured (i.e. has names) and `weight` is None, use
+    the named fields of the dtype to look up edge attributes."""
+    G.add_edge(0, 1, weight=10, cost=5.0)
+    dtype = np.dtype([("weight", int), ("cost", int)])
+    A = nx.to_numpy_array(G, dtype=dtype, weight=None)
+    expected = np.asarray(expected, dtype=dtype)
+    npt.assert_array_equal(A, expected)
+
+
+def test_to_numpy_array_structured_dtype_single_attr_default():
+    G = nx.path_graph(3)
+    dtype = np.dtype([("weight", float)])  # A single named field
+    A = nx.to_numpy_array(G, dtype=dtype, weight=None)
+    expected = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]], dtype=float)
+    npt.assert_array_equal(A["weight"], expected)
+
+
+@pytest.mark.parametrize(
+    ("field_name", "expected_attr_val"),
+    [
+        ("weight", 1),
+        ("cost", 3),
+    ],
+)
+def test_to_numpy_array_structured_dtype_single_attr(field_name, expected_attr_val):
+    G = nx.Graph()
+    G.add_edge(0, 1, cost=3)
+    dtype = np.dtype([(field_name, float)])
+    A = nx.to_numpy_array(G, dtype=dtype, weight=None)
+    expected = np.array([[0, expected_attr_val], [expected_attr_val, 0]], dtype=float)
+    npt.assert_array_equal(A[field_name], expected)
+
+
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph))
+@pytest.mark.parametrize(
+    "edge",
+    [
+        (0, 1),  # No edge attributes
+        (0, 1, {"weight": 10}),  # One edge attr
+        (0, 1, {"weight": 5, "flow": -4}),  # Multiple but not all edge attrs
+        (0, 1, {"weight": 2.0, "cost": 10, "flow": -45}),  # All attrs
+    ],
+)
+def test_to_numpy_array_structured_dtype_multiple_fields(graph_type, edge):
+    G = graph_type([edge])
+    dtype = np.dtype([("weight", float), ("cost", float), ("flow", float)])
+    A = nx.to_numpy_array(G, dtype=dtype, weight=None)
+    for attr in dtype.names:
+        expected = nx.to_numpy_array(G, dtype=float, weight=attr)
+        npt.assert_array_equal(A[attr], expected)
+
+
+@pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
+def test_to_numpy_array_structured_dtype_scalar_nonedge(G):
+    G.add_edge(0, 1, weight=10)
+    dtype = np.dtype([("weight", float), ("cost", float)])
+    A = nx.to_numpy_array(G, dtype=dtype, weight=None, nonedge=np.nan)
+    for attr in dtype.names:
+        expected = nx.to_numpy_array(G, dtype=float, weight=attr, nonedge=np.nan)
+        npt.assert_array_equal(A[attr], expected)
+
+
+@pytest.mark.parametrize("G", (nx.Graph(), nx.DiGraph()))
+def test_to_numpy_array_structured_dtype_nonedge_ary(G):
+    """Similar to the scalar case, except has a different non-edge value for
+    each named field."""
+    G.add_edge(0, 1, weight=10)
+    dtype = np.dtype([("weight", float), ("cost", float)])
+    nonedges = np.array([(0, np.inf)], dtype=dtype)
+    A = nx.to_numpy_array(G, dtype=dtype, weight=None, nonedge=nonedges)
+    for attr in dtype.names:
+        nonedge = nonedges[attr]
+        expected = nx.to_numpy_array(G, dtype=float, weight=attr, nonedge=nonedge)
+        npt.assert_array_equal(A[attr], expected)
+
+
+def test_to_numpy_array_structured_dtype_with_weight_raises():
+    """Using both a structured dtype (with named fields) and specifying a `weight`
+    parameter is ambiguous."""
+    G = nx.path_graph(3)
+    dtype = np.dtype([("weight", int), ("cost", int)])
+    exception_msg = "Specifying `weight` not supported for structured dtypes"
+    with pytest.raises(ValueError, match=exception_msg):
+        nx.to_numpy_array(G, dtype=dtype)  # Default is weight="weight"
+    with pytest.raises(ValueError, match=exception_msg):
+        nx.to_numpy_array(G, dtype=dtype, weight="cost")
+
+
+@pytest.mark.parametrize("graph_type", (nx.MultiGraph, nx.MultiDiGraph))
+def test_to_numpy_array_structured_multigraph_raises(graph_type):
+    G = nx.path_graph(3, create_using=graph_type)
+    dtype = np.dtype([("weight", int), ("cost", int)])
+    with pytest.raises(nx.NetworkXError, match="Structured arrays are not supported"):
+        nx.to_numpy_array(G, dtype=dtype, weight=None)
+
+
+def test_from_numpy_array_nodelist_bad_size():
+    """An exception is raised when `len(nodelist) != A.shape[0]`."""
+    n = 5  # Number of nodes
+    A = np.diag(np.ones(n - 1), k=1)  # Adj. matrix for P_n
+    expected = nx.path_graph(n)
+
+    assert graphs_equal(nx.from_numpy_array(A, edge_attr=None), expected)
+    nodes = list(range(n))
+    assert graphs_equal(
+        nx.from_numpy_array(A, edge_attr=None, nodelist=nodes), expected
+    )
+
+    # Too many node labels
+    nodes = list(range(n + 1))
+    with pytest.raises(ValueError, match="nodelist must have the same length as A"):
+        nx.from_numpy_array(A, nodelist=nodes)
+
+    # Too few node labels
+    nodes = list(range(n - 1))
+    with pytest.raises(ValueError, match="nodelist must have the same length as A"):
+        nx.from_numpy_array(A, nodelist=nodes)
+
+
+@pytest.mark.parametrize(
+    "nodes",
+    (
+        [4, 3, 2, 1, 0],
+        [9, 7, 1, 2, 8],
+        ["a", "b", "c", "d", "e"],
+        [(0, 0), (1, 1), (2, 3), (0, 2), (3, 1)],
+        ["A", 2, 7, "spam", (1, 3)],
+    ),
+)
+def test_from_numpy_array_nodelist(nodes):
+    A = np.diag(np.ones(4), k=1)
+    # Without edge attributes
+    expected = nx.relabel_nodes(
+        nx.path_graph(5), mapping=dict(enumerate(nodes)), copy=True
+    )
+    G = nx.from_numpy_array(A, edge_attr=None, nodelist=nodes)
+    assert graphs_equal(G, expected)
+
+    # With edge attributes
+    nx.set_edge_attributes(expected, 1.0, name="weight")
+    G = nx.from_numpy_array(A, nodelist=nodes)
+    assert graphs_equal(G, expected)
+
+
+@pytest.mark.parametrize(
+    "nodes",
+    (
+        [4, 3, 2, 1, 0],
+        [9, 7, 1, 2, 8],
+        ["a", "b", "c", "d", "e"],
+        [(0, 0), (1, 1), (2, 3), (0, 2), (3, 1)],
+        ["A", 2, 7, "spam", (1, 3)],
+    ),
+)
+def test_from_numpy_array_nodelist_directed(nodes):
+    A = np.diag(np.ones(4), k=1)
+    # Without edge attributes
+    H = nx.DiGraph([(0, 1), (1, 2), (2, 3), (3, 4)])
+    expected = nx.relabel_nodes(H, mapping=dict(enumerate(nodes)), copy=True)
+    G = nx.from_numpy_array(A, create_using=nx.DiGraph, edge_attr=None, nodelist=nodes)
+    assert graphs_equal(G, expected)
+
+    # With edge attributes
+    nx.set_edge_attributes(expected, 1.0, name="weight")
+    G = nx.from_numpy_array(A, create_using=nx.DiGraph, nodelist=nodes)
+    assert graphs_equal(G, expected)
+
+
+@pytest.mark.parametrize(
+    "nodes",
+    (
+        [4, 3, 2, 1, 0],
+        [9, 7, 1, 2, 8],
+        ["a", "b", "c", "d", "e"],
+        [(0, 0), (1, 1), (2, 3), (0, 2), (3, 1)],
+        ["A", 2, 7, "spam", (1, 3)],
+    ),
+)
+def test_from_numpy_array_nodelist_multigraph(nodes):
+    A = np.array(
+        [
+            [0, 1, 0, 0, 0],
+            [1, 0, 2, 0, 0],
+            [0, 2, 0, 3, 0],
+            [0, 0, 3, 0, 4],
+            [0, 0, 0, 4, 0],
+        ]
+    )
+
+    H = nx.MultiGraph()
+    for i, edge in enumerate(((0, 1), (1, 2), (2, 3), (3, 4))):
+        H.add_edges_from(itertools.repeat(edge, i + 1))
+    expected = nx.relabel_nodes(H, mapping=dict(enumerate(nodes)), copy=True)
+
+    G = nx.from_numpy_array(
+        A,
+        parallel_edges=True,
+        create_using=nx.MultiGraph,
+        edge_attr=None,
+        nodelist=nodes,
+    )
+    assert graphs_equal(G, expected)
+
+
+@pytest.mark.parametrize(
+    "nodes",
+    (
+        [4, 3, 2, 1, 0],
+        [9, 7, 1, 2, 8],
+        ["a", "b", "c", "d", "e"],
+        [(0, 0), (1, 1), (2, 3), (0, 2), (3, 1)],
+        ["A", 2, 7, "spam", (1, 3)],
+    ),
+)
+@pytest.mark.parametrize("graph", (nx.complete_graph, nx.cycle_graph, nx.wheel_graph))
+def test_from_numpy_array_nodelist_rountrip(graph, nodes):
+    G = graph(5)
+    A = nx.to_numpy_array(G)
+    expected = nx.relabel_nodes(G, mapping=dict(enumerate(nodes)), copy=True)
+    H = nx.from_numpy_array(A, edge_attr=None, nodelist=nodes)
+    assert graphs_equal(H, expected)
+
+    # With an isolated node
+    G = graph(4)
+    G.add_node("foo")
+    A = nx.to_numpy_array(G)
+    expected = nx.relabel_nodes(G, mapping=dict(zip(G.nodes, nodes)), copy=True)
+    H = nx.from_numpy_array(A, edge_attr=None, nodelist=nodes)
+    assert graphs_equal(H, expected)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_convert_pandas.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_convert_pandas.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c3f02a04300f0c2b316147925d164a7959d656b
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_convert_pandas.py
@@ -0,0 +1,349 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import edges_equal, graphs_equal, nodes_equal
+
+np = pytest.importorskip("numpy")
+pd = pytest.importorskip("pandas")
+
+
+class TestConvertPandas:
+    def setup_method(self):
+        self.rng = np.random.RandomState(seed=5)
+        ints = self.rng.randint(1, 11, size=(3, 2))
+        a = ["A", "B", "C"]
+        b = ["D", "A", "E"]
+        df = pd.DataFrame(ints, columns=["weight", "cost"])
+        df[0] = a  # Column label 0 (int)
+        df["b"] = b  # Column label 'b' (str)
+        self.df = df
+
+        mdf = pd.DataFrame([[4, 16, "A", "D"]], columns=["weight", "cost", 0, "b"])
+        self.mdf = pd.concat([df, mdf])
+
+    def test_exceptions(self):
+        G = pd.DataFrame(["a"])  # adj
+        pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G)
+        G = pd.DataFrame(["a", 0.0])  # elist
+        pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G)
+        df = pd.DataFrame([[1, 1], [1, 0]], dtype=int, index=[1, 2], columns=["a", "b"])
+        pytest.raises(nx.NetworkXError, nx.from_pandas_adjacency, df)
+
+    def test_from_edgelist_all_attr(self):
+        Gtrue = nx.Graph(
+            [
+                ("E", "C", {"cost": 9, "weight": 10}),
+                ("B", "A", {"cost": 1, "weight": 7}),
+                ("A", "D", {"cost": 7, "weight": 4}),
+            ]
+        )
+        G = nx.from_pandas_edgelist(self.df, 0, "b", True)
+        assert graphs_equal(G, Gtrue)
+        # MultiGraph
+        MGtrue = nx.MultiGraph(Gtrue)
+        MGtrue.add_edge("A", "D", cost=16, weight=4)
+        MG = nx.from_pandas_edgelist(self.mdf, 0, "b", True, nx.MultiGraph())
+        assert graphs_equal(MG, MGtrue)
+
+    def test_from_edgelist_multi_attr(self):
+        Gtrue = nx.Graph(
+            [
+                ("E", "C", {"cost": 9, "weight": 10}),
+                ("B", "A", {"cost": 1, "weight": 7}),
+                ("A", "D", {"cost": 7, "weight": 4}),
+            ]
+        )
+        G = nx.from_pandas_edgelist(self.df, 0, "b", ["weight", "cost"])
+        assert graphs_equal(G, Gtrue)
+
+    def test_from_edgelist_multi_attr_incl_target(self):
+        Gtrue = nx.Graph(
+            [
+                ("E", "C", {0: "C", "b": "E", "weight": 10}),
+                ("B", "A", {0: "B", "b": "A", "weight": 7}),
+                ("A", "D", {0: "A", "b": "D", "weight": 4}),
+            ]
+        )
+        G = nx.from_pandas_edgelist(self.df, 0, "b", [0, "b", "weight"])
+        assert graphs_equal(G, Gtrue)
+
+    def test_from_edgelist_multidigraph_and_edge_attr(self):
+        # example from issue #2374
+        edges = [
+            ("X1", "X4", {"Co": "zA", "Mi": 0, "St": "X1"}),
+            ("X1", "X4", {"Co": "zB", "Mi": 54, "St": "X2"}),
+            ("X1", "X4", {"Co": "zB", "Mi": 49, "St": "X3"}),
+            ("X1", "X4", {"Co": "zB", "Mi": 44, "St": "X4"}),
+            ("Y1", "Y3", {"Co": "zC", "Mi": 0, "St": "Y1"}),
+            ("Y1", "Y3", {"Co": "zC", "Mi": 34, "St": "Y2"}),
+            ("Y1", "Y3", {"Co": "zC", "Mi": 29, "St": "X2"}),
+            ("Y1", "Y3", {"Co": "zC", "Mi": 24, "St": "Y3"}),
+            ("Z1", "Z3", {"Co": "zD", "Mi": 0, "St": "Z1"}),
+            ("Z1", "Z3", {"Co": "zD", "Mi": 14, "St": "X3"}),
+        ]
+        Gtrue = nx.MultiDiGraph(edges)
+        data = {
+            "O": ["X1", "X1", "X1", "X1", "Y1", "Y1", "Y1", "Y1", "Z1", "Z1"],
+            "D": ["X4", "X4", "X4", "X4", "Y3", "Y3", "Y3", "Y3", "Z3", "Z3"],
+            "St": ["X1", "X2", "X3", "X4", "Y1", "Y2", "X2", "Y3", "Z1", "X3"],
+            "Co": ["zA", "zB", "zB", "zB", "zC", "zC", "zC", "zC", "zD", "zD"],
+            "Mi": [0, 54, 49, 44, 0, 34, 29, 24, 0, 14],
+        }
+        df = pd.DataFrame.from_dict(data)
+        G1 = nx.from_pandas_edgelist(
+            df, source="O", target="D", edge_attr=True, create_using=nx.MultiDiGraph
+        )
+        G2 = nx.from_pandas_edgelist(
+            df,
+            source="O",
+            target="D",
+            edge_attr=["St", "Co", "Mi"],
+            create_using=nx.MultiDiGraph,
+        )
+        assert graphs_equal(G1, Gtrue)
+        assert graphs_equal(G2, Gtrue)
+
+    def test_from_edgelist_one_attr(self):
+        Gtrue = nx.Graph(
+            [
+                ("E", "C", {"weight": 10}),
+                ("B", "A", {"weight": 7}),
+                ("A", "D", {"weight": 4}),
+            ]
+        )
+        G = nx.from_pandas_edgelist(self.df, 0, "b", "weight")
+        assert graphs_equal(G, Gtrue)
+
+    def test_from_edgelist_int_attr_name(self):
+        # note: this also tests that edge_attr can be `source`
+        Gtrue = nx.Graph(
+            [("E", "C", {0: "C"}), ("B", "A", {0: "B"}), ("A", "D", {0: "A"})]
+        )
+        G = nx.from_pandas_edgelist(self.df, 0, "b", 0)
+        assert graphs_equal(G, Gtrue)
+
+    def test_from_edgelist_invalid_attr(self):
+        pytest.raises(
+            nx.NetworkXError, nx.from_pandas_edgelist, self.df, 0, "b", "misspell"
+        )
+        pytest.raises(nx.NetworkXError, nx.from_pandas_edgelist, self.df, 0, "b", 1)
+        # see Issue #3562
+        edgeframe = pd.DataFrame([[0, 1], [1, 2], [2, 0]], columns=["s", "t"])
+        pytest.raises(
+            nx.NetworkXError, nx.from_pandas_edgelist, edgeframe, "s", "t", True
+        )
+        pytest.raises(
+            nx.NetworkXError, nx.from_pandas_edgelist, edgeframe, "s", "t", "weight"
+        )
+        pytest.raises(
+            nx.NetworkXError,
+            nx.from_pandas_edgelist,
+            edgeframe,
+            "s",
+            "t",
+            ["weight", "size"],
+        )
+
+    def test_from_edgelist_no_attr(self):
+        Gtrue = nx.Graph([("E", "C", {}), ("B", "A", {}), ("A", "D", {})])
+        G = nx.from_pandas_edgelist(self.df, 0, "b")
+        assert graphs_equal(G, Gtrue)
+
+    def test_from_edgelist(self):
+        # Pandas DataFrame
+        G = nx.cycle_graph(10)
+        G.add_weighted_edges_from((u, v, u) for u, v in list(G.edges))
+
+        edgelist = nx.to_edgelist(G)
+        source = [s for s, t, d in edgelist]
+        target = [t for s, t, d in edgelist]
+        weight = [d["weight"] for s, t, d in edgelist]
+        edges = pd.DataFrame({"source": source, "target": target, "weight": weight})
+
+        GG = nx.from_pandas_edgelist(edges, edge_attr="weight")
+        assert nodes_equal(G.nodes(), GG.nodes())
+        assert edges_equal(G.edges(), GG.edges())
+        GW = nx.to_networkx_graph(edges, create_using=nx.Graph)
+        assert nodes_equal(G.nodes(), GW.nodes())
+        assert edges_equal(G.edges(), GW.edges())
+
+    def test_to_edgelist_default_source_or_target_col_exists(self):
+        G = nx.path_graph(10)
+        G.add_weighted_edges_from((u, v, u) for u, v in list(G.edges))
+        nx.set_edge_attributes(G, 0, name="source")
+        pytest.raises(nx.NetworkXError, nx.to_pandas_edgelist, G)
+
+        # drop source column to test an exception raised for the target column
+        for u, v, d in G.edges(data=True):
+            d.pop("source", None)
+
+        nx.set_edge_attributes(G, 0, name="target")
+        pytest.raises(nx.NetworkXError, nx.to_pandas_edgelist, G)
+
+    def test_to_edgelist_custom_source_or_target_col_exists(self):
+        G = nx.path_graph(10)
+        G.add_weighted_edges_from((u, v, u) for u, v in list(G.edges))
+        nx.set_edge_attributes(G, 0, name="source_col_name")
+        pytest.raises(
+            nx.NetworkXError, nx.to_pandas_edgelist, G, source="source_col_name"
+        )
+
+        # drop source column to test an exception raised for the target column
+        for u, v, d in G.edges(data=True):
+            d.pop("source_col_name", None)
+
+        nx.set_edge_attributes(G, 0, name="target_col_name")
+        pytest.raises(
+            nx.NetworkXError, nx.to_pandas_edgelist, G, target="target_col_name"
+        )
+
+    def test_to_edgelist_edge_key_col_exists(self):
+        G = nx.path_graph(10, create_using=nx.MultiGraph)
+        G.add_weighted_edges_from((u, v, u) for u, v in list(G.edges()))
+        nx.set_edge_attributes(G, 0, name="edge_key_name")
+        pytest.raises(
+            nx.NetworkXError, nx.to_pandas_edgelist, G, edge_key="edge_key_name"
+        )
+
+    def test_from_adjacency(self):
+        nodelist = [1, 2]
+        dftrue = pd.DataFrame(
+            [[1, 1], [1, 0]], dtype=int, index=nodelist, columns=nodelist
+        )
+        G = nx.Graph([(1, 1), (1, 2)])
+        df = nx.to_pandas_adjacency(G, dtype=int)
+        pd.testing.assert_frame_equal(df, dftrue)
+
+    @pytest.mark.parametrize("graph", [nx.Graph, nx.MultiGraph])
+    def test_roundtrip(self, graph):
+        # edgelist
+        Gtrue = graph([(1, 1), (1, 2)])
+        df = nx.to_pandas_edgelist(Gtrue)
+        G = nx.from_pandas_edgelist(df, create_using=graph)
+        assert graphs_equal(Gtrue, G)
+        # adjacency
+        adj = {1: {1: {"weight": 1}, 2: {"weight": 1}}, 2: {1: {"weight": 1}}}
+        Gtrue = graph(adj)
+        df = nx.to_pandas_adjacency(Gtrue, dtype=int)
+        G = nx.from_pandas_adjacency(df, create_using=graph)
+        assert graphs_equal(Gtrue, G)
+
+    def test_from_adjacency_named(self):
+        # example from issue #3105
+        data = {
+            "A": {"A": 0, "B": 0, "C": 0},
+            "B": {"A": 1, "B": 0, "C": 0},
+            "C": {"A": 0, "B": 1, "C": 0},
+        }
+        dftrue = pd.DataFrame(data, dtype=np.intp)
+        df = dftrue[["A", "C", "B"]]
+        G = nx.from_pandas_adjacency(df, create_using=nx.DiGraph())
+        df = nx.to_pandas_adjacency(G, dtype=np.intp)
+        pd.testing.assert_frame_equal(df, dftrue)
+
+    @pytest.mark.parametrize("edge_attr", [["attr2", "attr3"], True])
+    def test_edgekey_with_multigraph(self, edge_attr):
+        df = pd.DataFrame(
+            {
+                "source": {"A": "N1", "B": "N2", "C": "N1", "D": "N1"},
+                "target": {"A": "N2", "B": "N3", "C": "N1", "D": "N2"},
+                "attr1": {"A": "F1", "B": "F2", "C": "F3", "D": "F4"},
+                "attr2": {"A": 1, "B": 0, "C": 0, "D": 0},
+                "attr3": {"A": 0, "B": 1, "C": 0, "D": 1},
+            }
+        )
+        Gtrue = nx.MultiGraph(
+            [
+                ("N1", "N2", "F1", {"attr2": 1, "attr3": 0}),
+                ("N2", "N3", "F2", {"attr2": 0, "attr3": 1}),
+                ("N1", "N1", "F3", {"attr2": 0, "attr3": 0}),
+                ("N1", "N2", "F4", {"attr2": 0, "attr3": 1}),
+            ]
+        )
+        # example from issue #4065
+        G = nx.from_pandas_edgelist(
+            df,
+            source="source",
+            target="target",
+            edge_attr=edge_attr,
+            edge_key="attr1",
+            create_using=nx.MultiGraph(),
+        )
+        assert graphs_equal(G, Gtrue)
+
+        df_roundtrip = nx.to_pandas_edgelist(G, edge_key="attr1")
+        df_roundtrip = df_roundtrip.sort_values("attr1")
+        df_roundtrip.index = ["A", "B", "C", "D"]
+        pd.testing.assert_frame_equal(
+            df, df_roundtrip[["source", "target", "attr1", "attr2", "attr3"]]
+        )
+
+    def test_edgekey_with_normal_graph_no_action(self):
+        Gtrue = nx.Graph(
+            [
+                ("E", "C", {"cost": 9, "weight": 10}),
+                ("B", "A", {"cost": 1, "weight": 7}),
+                ("A", "D", {"cost": 7, "weight": 4}),
+            ]
+        )
+        G = nx.from_pandas_edgelist(self.df, 0, "b", True, edge_key="weight")
+        assert graphs_equal(G, Gtrue)
+
+    def test_nonexisting_edgekey_raises(self):
+        with pytest.raises(nx.exception.NetworkXError):
+            nx.from_pandas_edgelist(
+                self.df,
+                source="source",
+                target="target",
+                edge_key="Not_real",
+                edge_attr=True,
+                create_using=nx.MultiGraph(),
+            )
+
+    def test_multigraph_with_edgekey_no_edgeattrs(self):
+        Gtrue = nx.MultiGraph()
+        Gtrue.add_edge(0, 1, key=0)
+        Gtrue.add_edge(0, 1, key=3)
+        df = nx.to_pandas_edgelist(Gtrue, edge_key="key")
+        expected = pd.DataFrame({"source": [0, 0], "target": [1, 1], "key": [0, 3]})
+        pd.testing.assert_frame_equal(expected, df)
+        G = nx.from_pandas_edgelist(df, edge_key="key", create_using=nx.MultiGraph)
+        assert graphs_equal(Gtrue, G)
+
+
+def test_to_pandas_adjacency_with_nodelist():
+    G = nx.complete_graph(5)
+    nodelist = [1, 4]
+    expected = pd.DataFrame(
+        [[0, 1], [1, 0]], dtype=int, index=nodelist, columns=nodelist
+    )
+    pd.testing.assert_frame_equal(
+        expected, nx.to_pandas_adjacency(G, nodelist, dtype=int)
+    )
+
+
+def test_to_pandas_edgelist_with_nodelist():
+    G = nx.Graph()
+    G.add_edges_from([(0, 1), (1, 2), (1, 3)], weight=2.0)
+    G.add_edge(0, 5, weight=100)
+    df = nx.to_pandas_edgelist(G, nodelist=[1, 2])
+    assert 0 not in df["source"].to_numpy()
+    assert 100 not in df["weight"].to_numpy()
+
+
+def test_from_pandas_adjacency_with_index_collisions():
+    """See gh-7407"""
+    df = pd.DataFrame(
+        [
+            [0, 1, 0, 0],
+            [0, 0, 1, 0],
+            [0, 0, 0, 1],
+            [0, 0, 0, 0],
+        ],
+        index=[1010001, 2, 1, 1010002],
+        columns=[1010001, 2, 1, 1010002],
+    )
+    G = nx.from_pandas_adjacency(df, create_using=nx.DiGraph)
+    expected = nx.DiGraph([(1010001, 2), (2, 1), (1, 1010002)])
+    assert nodes_equal(G.nodes, expected.nodes)
+    assert edges_equal(G.edges, expected.edges)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_convert_scipy.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_convert_scipy.py
new file mode 100644
index 0000000000000000000000000000000000000000..2575027046c61612c1be3c36a5b7877c28e71d92
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_convert_scipy.py
@@ -0,0 +1,281 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import graphs_equal
+
+np = pytest.importorskip("numpy")
+sp = pytest.importorskip("scipy")
+
+
+class TestConvertScipy:
+    def setup_method(self):
+        self.G1 = nx.barbell_graph(10, 3)
+        self.G2 = nx.cycle_graph(10, create_using=nx.DiGraph)
+
+        self.G3 = self.create_weighted(nx.Graph())
+        self.G4 = self.create_weighted(nx.DiGraph())
+
+    def test_exceptions(self):
+        class G:
+            format = None
+
+        pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G)
+
+    def create_weighted(self, G):
+        g = nx.cycle_graph(4)
+        e = list(g.edges())
+        source = [u for u, v in e]
+        dest = [v for u, v in e]
+        weight = [s + 10 for s in source]
+        ex = zip(source, dest, weight)
+        G.add_weighted_edges_from(ex)
+        return G
+
+    def identity_conversion(self, G, A, create_using):
+        GG = nx.from_scipy_sparse_array(A, create_using=create_using)
+        assert nx.is_isomorphic(G, GG)
+
+        GW = nx.to_networkx_graph(A, create_using=create_using)
+        assert nx.is_isomorphic(G, GW)
+
+        GI = nx.empty_graph(0, create_using).__class__(A)
+        assert nx.is_isomorphic(G, GI)
+
+        ACSR = A.tocsr()
+        GI = nx.empty_graph(0, create_using).__class__(ACSR)
+        assert nx.is_isomorphic(G, GI)
+
+        ACOO = A.tocoo()
+        GI = nx.empty_graph(0, create_using).__class__(ACOO)
+        assert nx.is_isomorphic(G, GI)
+
+        ACSC = A.tocsc()
+        GI = nx.empty_graph(0, create_using).__class__(ACSC)
+        assert nx.is_isomorphic(G, GI)
+
+        AD = A.todense()
+        GI = nx.empty_graph(0, create_using).__class__(AD)
+        assert nx.is_isomorphic(G, GI)
+
+        AA = A.toarray()
+        GI = nx.empty_graph(0, create_using).__class__(AA)
+        assert nx.is_isomorphic(G, GI)
+
+    def test_shape(self):
+        "Conversion from non-square sparse array."
+        A = sp.sparse.lil_array([[1, 2, 3], [4, 5, 6]])
+        pytest.raises(nx.NetworkXError, nx.from_scipy_sparse_array, A)
+
+    def test_identity_graph_matrix(self):
+        "Conversion from graph to sparse matrix to graph."
+        A = nx.to_scipy_sparse_array(self.G1)
+        self.identity_conversion(self.G1, A, nx.Graph())
+
+    def test_identity_digraph_matrix(self):
+        "Conversion from digraph to sparse matrix to digraph."
+        A = nx.to_scipy_sparse_array(self.G2)
+        self.identity_conversion(self.G2, A, nx.DiGraph())
+
+    def test_identity_weighted_graph_matrix(self):
+        """Conversion from weighted graph to sparse matrix to weighted graph."""
+        A = nx.to_scipy_sparse_array(self.G3)
+        self.identity_conversion(self.G3, A, nx.Graph())
+
+    def test_identity_weighted_digraph_matrix(self):
+        """Conversion from weighted digraph to sparse matrix to weighted digraph."""
+        A = nx.to_scipy_sparse_array(self.G4)
+        self.identity_conversion(self.G4, A, nx.DiGraph())
+
+    def test_nodelist(self):
+        """Conversion from graph to sparse matrix to graph with nodelist."""
+        P4 = nx.path_graph(4)
+        P3 = nx.path_graph(3)
+        nodelist = list(P3.nodes())
+        A = nx.to_scipy_sparse_array(P4, nodelist=nodelist)
+        GA = nx.Graph(A)
+        assert nx.is_isomorphic(GA, P3)
+
+        pytest.raises(nx.NetworkXError, nx.to_scipy_sparse_array, P3, nodelist=[])
+        # Test nodelist duplicates.
+        long_nl = nodelist + [0]
+        pytest.raises(nx.NetworkXError, nx.to_scipy_sparse_array, P3, nodelist=long_nl)
+
+        # Test nodelist contains non-nodes
+        non_nl = [-1, 0, 1, 2]
+        pytest.raises(nx.NetworkXError, nx.to_scipy_sparse_array, P3, nodelist=non_nl)
+
+    def test_weight_keyword(self):
+        WP4 = nx.Graph()
+        WP4.add_edges_from((n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3))
+        P4 = nx.path_graph(4)
+        A = nx.to_scipy_sparse_array(P4)
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+        np.testing.assert_equal(
+            0.5 * A.todense(), nx.to_scipy_sparse_array(WP4).todense()
+        )
+        np.testing.assert_equal(
+            0.3 * A.todense(), nx.to_scipy_sparse_array(WP4, weight="other").todense()
+        )
+
+    def test_format_keyword(self):
+        WP4 = nx.Graph()
+        WP4.add_edges_from((n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3))
+        P4 = nx.path_graph(4)
+        A = nx.to_scipy_sparse_array(P4, format="csr")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="csc")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="coo")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="bsr")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="lil")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="dia")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+        A = nx.to_scipy_sparse_array(P4, format="dok")
+        np.testing.assert_equal(
+            A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
+        )
+
+    def test_format_keyword_raise(self):
+        with pytest.raises(nx.NetworkXError):
+            WP4 = nx.Graph()
+            WP4.add_edges_from(
+                (n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3)
+            )
+            P4 = nx.path_graph(4)
+            nx.to_scipy_sparse_array(P4, format="any_other")
+
+    def test_null_raise(self):
+        with pytest.raises(nx.NetworkXError):
+            nx.to_scipy_sparse_array(nx.Graph())
+
+    def test_empty(self):
+        G = nx.Graph()
+        G.add_node(1)
+        M = nx.to_scipy_sparse_array(G)
+        np.testing.assert_equal(M.toarray(), np.array([[0]]))
+
+    def test_ordering(self):
+        G = nx.DiGraph()
+        G.add_edge(1, 2)
+        G.add_edge(2, 3)
+        G.add_edge(3, 1)
+        M = nx.to_scipy_sparse_array(G, nodelist=[3, 2, 1])
+        np.testing.assert_equal(
+            M.toarray(), np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
+        )
+
+    def test_selfloop_graph(self):
+        G = nx.Graph([(1, 1)])
+        M = nx.to_scipy_sparse_array(G)
+        np.testing.assert_equal(M.toarray(), np.array([[1]]))
+
+        G.add_edges_from([(2, 3), (3, 4)])
+        M = nx.to_scipy_sparse_array(G, nodelist=[2, 3, 4])
+        np.testing.assert_equal(
+            M.toarray(), np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])
+        )
+
+    def test_selfloop_digraph(self):
+        G = nx.DiGraph([(1, 1)])
+        M = nx.to_scipy_sparse_array(G)
+        np.testing.assert_equal(M.toarray(), np.array([[1]]))
+
+        G.add_edges_from([(2, 3), (3, 4)])
+        M = nx.to_scipy_sparse_array(G, nodelist=[2, 3, 4])
+        np.testing.assert_equal(
+            M.toarray(), np.array([[0, 1, 0], [0, 0, 1], [0, 0, 0]])
+        )
+
+    def test_from_scipy_sparse_array_parallel_edges(self):
+        """Tests that the :func:`networkx.from_scipy_sparse_array` function
+        interprets integer weights as the number of parallel edges when
+        creating a multigraph.
+
+        """
+        A = sp.sparse.csr_array([[1, 1], [1, 2]])
+        # First, with a simple graph, each integer entry in the adjacency
+        # matrix is interpreted as the weight of a single edge in the graph.
+        expected = nx.DiGraph()
+        edges = [(0, 0), (0, 1), (1, 0)]
+        expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
+        expected.add_edge(1, 1, weight=2)
+        actual = nx.from_scipy_sparse_array(
+            A, parallel_edges=True, create_using=nx.DiGraph
+        )
+        assert graphs_equal(actual, expected)
+        actual = nx.from_scipy_sparse_array(
+            A, parallel_edges=False, create_using=nx.DiGraph
+        )
+        assert graphs_equal(actual, expected)
+        # Now each integer entry in the adjacency matrix is interpreted as the
+        # number of parallel edges in the graph if the appropriate keyword
+        # argument is specified.
+        edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)]
+        expected = nx.MultiDiGraph()
+        expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
+        actual = nx.from_scipy_sparse_array(
+            A, parallel_edges=True, create_using=nx.MultiDiGraph
+        )
+        assert graphs_equal(actual, expected)
+        expected = nx.MultiDiGraph()
+        expected.add_edges_from(set(edges), weight=1)
+        # The sole self-loop (edge 0) on vertex 1 should have weight 2.
+        expected[1][1][0]["weight"] = 2
+        actual = nx.from_scipy_sparse_array(
+            A, parallel_edges=False, create_using=nx.MultiDiGraph
+        )
+        assert graphs_equal(actual, expected)
+
+    def test_symmetric(self):
+        """Tests that a symmetric matrix has edges added only once to an
+        undirected multigraph when using
+        :func:`networkx.from_scipy_sparse_array`.
+
+        """
+        A = sp.sparse.csr_array([[0, 1], [1, 0]])
+        G = nx.from_scipy_sparse_array(A, create_using=nx.MultiGraph)
+        expected = nx.MultiGraph()
+        expected.add_edge(0, 1, weight=1)
+        assert graphs_equal(G, expected)
+
+
+@pytest.mark.parametrize("sparse_format", ("csr", "csc", "dok"))
+def test_from_scipy_sparse_array_formats(sparse_format):
+    """Test all formats supported by _generate_weighted_edges."""
+    # trinode complete graph with non-uniform edge weights
+    expected = nx.Graph()
+    expected.add_edges_from(
+        [
+            (0, 1, {"weight": 3}),
+            (0, 2, {"weight": 2}),
+            (1, 0, {"weight": 3}),
+            (1, 2, {"weight": 1}),
+            (2, 0, {"weight": 2}),
+            (2, 1, {"weight": 1}),
+        ]
+    )
+    A = sp.sparse.coo_array([[0, 3, 2], [3, 0, 1], [2, 1, 0]]).asformat(sparse_format)
+    assert graphs_equal(expected, nx.from_scipy_sparse_array(A))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_exceptions.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_exceptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf59983cb8d12a119f5744ebc8b11e7cb9075366
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_exceptions.py
@@ -0,0 +1,40 @@
+import pytest
+
+import networkx as nx
+
+# smoke tests for exceptions
+
+
+def test_raises_networkxexception():
+    with pytest.raises(nx.NetworkXException):
+        raise nx.NetworkXException
+
+
+def test_raises_networkxerr():
+    with pytest.raises(nx.NetworkXError):
+        raise nx.NetworkXError
+
+
+def test_raises_networkx_pointless_concept():
+    with pytest.raises(nx.NetworkXPointlessConcept):
+        raise nx.NetworkXPointlessConcept
+
+
+def test_raises_networkxalgorithmerr():
+    with pytest.raises(nx.NetworkXAlgorithmError):
+        raise nx.NetworkXAlgorithmError
+
+
+def test_raises_networkx_unfeasible():
+    with pytest.raises(nx.NetworkXUnfeasible):
+        raise nx.NetworkXUnfeasible
+
+
+def test_raises_networkx_no_path():
+    with pytest.raises(nx.NetworkXNoPath):
+        raise nx.NetworkXNoPath
+
+
+def test_raises_networkx_unbounded():
+    with pytest.raises(nx.NetworkXUnbounded):
+        raise nx.NetworkXUnbounded
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_import.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_import.py
new file mode 100644
index 0000000000000000000000000000000000000000..32aafdf2a4dafc85cee088138590b84f4c627b5e
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_import.py
@@ -0,0 +1,11 @@
+import pytest
+
+
+def test_namespace_alias():
+    with pytest.raises(ImportError):
+        from networkx import nx
+
+
+def test_namespace_nesting():
+    with pytest.raises(ImportError):
+        from networkx import networkx
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_lazy_imports.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_lazy_imports.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec09ac2fcda5c9ce8b1f9c6f1d0fba58eb54742e
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_lazy_imports.py
@@ -0,0 +1,96 @@
+import sys
+import types
+
+import pytest
+
+import networkx.lazy_imports as lazy
+
+
+def test_lazy_import_basics():
+    math = lazy._lazy_import("math")
+    anything_not_real = lazy._lazy_import("anything_not_real")
+
+    # Now test that accessing attributes does what it should
+    assert math.sin(math.pi) == pytest.approx(0, 1e-6)
+    # poor-mans pytest.raises for testing errors on attribute access
+    try:
+        anything_not_real.pi
+        assert False  # Should not get here
+    except ModuleNotFoundError:
+        pass
+    assert isinstance(anything_not_real, lazy.DelayedImportErrorModule)
+    # see if it changes for second access
+    try:
+        anything_not_real.pi
+        assert False  # Should not get here
+    except ModuleNotFoundError:
+        pass
+
+
+def test_lazy_import_impact_on_sys_modules():
+    math = lazy._lazy_import("math")
+    anything_not_real = lazy._lazy_import("anything_not_real")
+
+    assert isinstance(math, types.ModuleType)
+    assert "math" in sys.modules
+    assert type(anything_not_real) is lazy.DelayedImportErrorModule
+    assert "anything_not_real" not in sys.modules
+
+    # only do this if numpy is installed
+    np_test = pytest.importorskip("numpy")
+    np = lazy._lazy_import("numpy")
+    assert isinstance(np, types.ModuleType)
+    assert "numpy" in sys.modules
+
+    np.pi  # trigger load of numpy
+
+    assert isinstance(np, types.ModuleType)
+    assert "numpy" in sys.modules
+
+
+def test_lazy_import_nonbuiltins():
+    sp = lazy._lazy_import("scipy")
+    np = lazy._lazy_import("numpy")
+    if isinstance(sp, lazy.DelayedImportErrorModule):
+        try:
+            sp.special.erf
+            assert False
+        except ModuleNotFoundError:
+            pass
+    elif isinstance(np, lazy.DelayedImportErrorModule):
+        try:
+            np.sin(np.pi)
+            assert False
+        except ModuleNotFoundError:
+            pass
+    else:
+        assert sp.special.erf(np.pi) == pytest.approx(1, 1e-4)
+
+
+def test_lazy_attach():
+    name = "mymod"
+    submods = ["mysubmodule", "anothersubmodule"]
+    myall = {"not_real_submod": ["some_var_or_func"]}
+
+    locls = {
+        "attach": lazy.attach,
+        "name": name,
+        "submods": submods,
+        "myall": myall,
+    }
+    s = "__getattr__, __lazy_dir__, __all__ = attach(name, submods, myall)"
+
+    exec(s, {}, locls)
+    expected = {
+        "attach": lazy.attach,
+        "name": name,
+        "submods": submods,
+        "myall": myall,
+        "__getattr__": None,
+        "__lazy_dir__": None,
+        "__all__": None,
+    }
+    assert locls.keys() == expected.keys()
+    for k, v in expected.items():
+        if v is not None:
+            assert locls[k] == v
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_relabel.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_relabel.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ebf4d3ef490afce48e3e1298412edb05a385cdc
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/tests/test_relabel.py
@@ -0,0 +1,347 @@
+import pytest
+
+import networkx as nx
+from networkx.generators.classic import empty_graph
+from networkx.utils import edges_equal, nodes_equal
+
+
+class TestRelabel:
+    def test_convert_node_labels_to_integers(self):
+        # test that empty graph converts fine for all options
+        G = empty_graph()
+        H = nx.convert_node_labels_to_integers(G, 100)
+        assert list(H.nodes()) == []
+        assert list(H.edges()) == []
+
+        for opt in ["default", "sorted", "increasing degree", "decreasing degree"]:
+            G = empty_graph()
+            H = nx.convert_node_labels_to_integers(G, 100, ordering=opt)
+            assert list(H.nodes()) == []
+            assert list(H.edges()) == []
+
+        G = empty_graph()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "C"), ("C", "D")])
+        H = nx.convert_node_labels_to_integers(G)
+        degH = (d for n, d in H.degree())
+        degG = (d for n, d in G.degree())
+        assert sorted(degH) == sorted(degG)
+
+        H = nx.convert_node_labels_to_integers(G, 1000)
+        degH = (d for n, d in H.degree())
+        degG = (d for n, d in G.degree())
+        assert sorted(degH) == sorted(degG)
+        assert nodes_equal(H.nodes(), [1000, 1001, 1002, 1003])
+
+        H = nx.convert_node_labels_to_integers(G, ordering="increasing degree")
+        degH = (d for n, d in H.degree())
+        degG = (d for n, d in G.degree())
+        assert sorted(degH) == sorted(degG)
+        assert H.degree(0) == 1
+        assert H.degree(1) == 2
+        assert H.degree(2) == 2
+        assert H.degree(3) == 3
+
+        H = nx.convert_node_labels_to_integers(G, ordering="decreasing degree")
+        degH = (d for n, d in H.degree())
+        degG = (d for n, d in G.degree())
+        assert sorted(degH) == sorted(degG)
+        assert H.degree(0) == 3
+        assert H.degree(1) == 2
+        assert H.degree(2) == 2
+        assert H.degree(3) == 1
+
+        H = nx.convert_node_labels_to_integers(
+            G, ordering="increasing degree", label_attribute="label"
+        )
+        degH = (d for n, d in H.degree())
+        degG = (d for n, d in G.degree())
+        assert sorted(degH) == sorted(degG)
+        assert H.degree(0) == 1
+        assert H.degree(1) == 2
+        assert H.degree(2) == 2
+        assert H.degree(3) == 3
+
+        # check mapping
+        assert H.nodes[3]["label"] == "C"
+        assert H.nodes[0]["label"] == "D"
+        assert H.nodes[1]["label"] == "A" or H.nodes[2]["label"] == "A"
+        assert H.nodes[1]["label"] == "B" or H.nodes[2]["label"] == "B"
+
+    def test_convert_to_integers2(self):
+        G = empty_graph()
+        G.add_edges_from([("C", "D"), ("A", "B"), ("A", "C"), ("B", "C")])
+        H = nx.convert_node_labels_to_integers(G, ordering="sorted")
+        degH = (d for n, d in H.degree())
+        degG = (d for n, d in G.degree())
+        assert sorted(degH) == sorted(degG)
+
+        H = nx.convert_node_labels_to_integers(
+            G, ordering="sorted", label_attribute="label"
+        )
+        assert H.nodes[0]["label"] == "A"
+        assert H.nodes[1]["label"] == "B"
+        assert H.nodes[2]["label"] == "C"
+        assert H.nodes[3]["label"] == "D"
+
+    def test_convert_to_integers_raise(self):
+        with pytest.raises(nx.NetworkXError):
+            G = nx.Graph()
+            H = nx.convert_node_labels_to_integers(G, ordering="increasing age")
+
+    def test_relabel_nodes_copy(self):
+        G = nx.empty_graph()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "C"), ("C", "D")])
+        mapping = {"A": "aardvark", "B": "bear", "C": "cat", "D": "dog"}
+        H = nx.relabel_nodes(G, mapping)
+        assert nodes_equal(H.nodes(), ["aardvark", "bear", "cat", "dog"])
+
+    def test_relabel_nodes_function(self):
+        G = nx.empty_graph()
+        G.add_edges_from([("A", "B"), ("A", "C"), ("B", "C"), ("C", "D")])
+        # function mapping no longer encouraged but works
+
+        def mapping(n):
+            return ord(n)
+
+        H = nx.relabel_nodes(G, mapping)
+        assert nodes_equal(H.nodes(), [65, 66, 67, 68])
+
+    def test_relabel_nodes_callable_type(self):
+        G = nx.path_graph(4)
+        H = nx.relabel_nodes(G, str)
+        assert nodes_equal(H.nodes, ["0", "1", "2", "3"])
+
+    @pytest.mark.parametrize("non_mc", ("0123", ["0", "1", "2", "3"]))
+    def test_relabel_nodes_non_mapping_or_callable(self, non_mc):
+        """If `mapping` is neither a Callable or a Mapping, an exception
+        should be raised."""
+        G = nx.path_graph(4)
+        with pytest.raises(AttributeError):
+            nx.relabel_nodes(G, non_mc)
+
+    def test_relabel_nodes_graph(self):
+        G = nx.Graph([("A", "B"), ("A", "C"), ("B", "C"), ("C", "D")])
+        mapping = {"A": "aardvark", "B": "bear", "C": "cat", "D": "dog"}
+        H = nx.relabel_nodes(G, mapping)
+        assert nodes_equal(H.nodes(), ["aardvark", "bear", "cat", "dog"])
+
+    def test_relabel_nodes_orderedgraph(self):
+        G = nx.Graph()
+        G.add_nodes_from([1, 2, 3])
+        G.add_edges_from([(1, 3), (2, 3)])
+        mapping = {1: "a", 2: "b", 3: "c"}
+        H = nx.relabel_nodes(G, mapping)
+        assert list(H.nodes) == ["a", "b", "c"]
+
+    def test_relabel_nodes_digraph(self):
+        G = nx.DiGraph([("A", "B"), ("A", "C"), ("B", "C"), ("C", "D")])
+        mapping = {"A": "aardvark", "B": "bear", "C": "cat", "D": "dog"}
+        H = nx.relabel_nodes(G, mapping, copy=False)
+        assert nodes_equal(H.nodes(), ["aardvark", "bear", "cat", "dog"])
+
+    def test_relabel_nodes_multigraph(self):
+        G = nx.MultiGraph([("a", "b"), ("a", "b")])
+        mapping = {"a": "aardvark", "b": "bear"}
+        G = nx.relabel_nodes(G, mapping, copy=False)
+        assert nodes_equal(G.nodes(), ["aardvark", "bear"])
+        assert edges_equal(G.edges(), [("aardvark", "bear"), ("aardvark", "bear")])
+
+    def test_relabel_nodes_multidigraph(self):
+        G = nx.MultiDiGraph([("a", "b"), ("a", "b")])
+        mapping = {"a": "aardvark", "b": "bear"}
+        G = nx.relabel_nodes(G, mapping, copy=False)
+        assert nodes_equal(G.nodes(), ["aardvark", "bear"])
+        assert edges_equal(G.edges(), [("aardvark", "bear"), ("aardvark", "bear")])
+
+    def test_relabel_isolated_nodes_to_same(self):
+        G = nx.Graph()
+        G.add_nodes_from(range(4))
+        mapping = {1: 1}
+        H = nx.relabel_nodes(G, mapping, copy=False)
+        assert nodes_equal(H.nodes(), list(range(4)))
+
+    def test_relabel_nodes_missing(self):
+        G = nx.Graph([("A", "B"), ("A", "C"), ("B", "C"), ("C", "D")])
+        mapping = {0: "aardvark"}
+        # copy=True
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        assert nodes_equal(H.nodes, G.nodes)
+        # copy=False
+        GG = G.copy()
+        nx.relabel_nodes(G, mapping, copy=False)
+        assert nodes_equal(G.nodes, GG.nodes)
+
+    def test_relabel_copy_name(self):
+        G = nx.Graph()
+        H = nx.relabel_nodes(G, {}, copy=True)
+        assert H.graph == G.graph
+        H = nx.relabel_nodes(G, {}, copy=False)
+        assert H.graph == G.graph
+        G.name = "first"
+        H = nx.relabel_nodes(G, {}, copy=True)
+        assert H.graph == G.graph
+        H = nx.relabel_nodes(G, {}, copy=False)
+        assert H.graph == G.graph
+
+    def test_relabel_toposort(self):
+        K5 = nx.complete_graph(4)
+        G = nx.complete_graph(4)
+        G = nx.relabel_nodes(G, {i: i + 1 for i in range(4)}, copy=False)
+        assert nx.is_isomorphic(K5, G)
+        G = nx.complete_graph(4)
+        G = nx.relabel_nodes(G, {i: i - 1 for i in range(4)}, copy=False)
+        assert nx.is_isomorphic(K5, G)
+
+    def test_relabel_selfloop(self):
+        G = nx.DiGraph([(1, 1), (1, 2), (2, 3)])
+        G = nx.relabel_nodes(G, {1: "One", 2: "Two", 3: "Three"}, copy=False)
+        assert nodes_equal(G.nodes(), ["One", "Three", "Two"])
+        G = nx.MultiDiGraph([(1, 1), (1, 2), (2, 3)])
+        G = nx.relabel_nodes(G, {1: "One", 2: "Two", 3: "Three"}, copy=False)
+        assert nodes_equal(G.nodes(), ["One", "Three", "Two"])
+        G = nx.MultiDiGraph([(1, 1)])
+        G = nx.relabel_nodes(G, {1: 0}, copy=False)
+        assert nodes_equal(G.nodes(), [0])
+
+    def test_relabel_multidigraph_inout_merge_nodes(self):
+        for MG in (nx.MultiGraph, nx.MultiDiGraph):
+            for cc in (True, False):
+                G = MG([(0, 4), (1, 4), (4, 2), (4, 3)])
+                G[0][4][0]["value"] = "a"
+                G[1][4][0]["value"] = "b"
+                G[4][2][0]["value"] = "c"
+                G[4][3][0]["value"] = "d"
+                G.add_edge(0, 4, key="x", value="e")
+                G.add_edge(4, 3, key="x", value="f")
+                mapping = {0: 9, 1: 9, 2: 9, 3: 9}
+                H = nx.relabel_nodes(G, mapping, copy=cc)
+                # No ordering on keys enforced
+                assert {"value": "a"} in H[9][4].values()
+                assert {"value": "b"} in H[9][4].values()
+                assert {"value": "c"} in H[4][9].values()
+                assert len(H[4][9]) == 3 if G.is_directed() else 6
+                assert {"value": "d"} in H[4][9].values()
+                assert {"value": "e"} in H[9][4].values()
+                assert {"value": "f"} in H[4][9].values()
+                assert len(H[9][4]) == 3 if G.is_directed() else 6
+
+    def test_relabel_multigraph_merge_inplace(self):
+        G = nx.MultiGraph([(0, 1), (0, 2), (0, 3), (0, 1), (0, 2), (0, 3)])
+        G[0][1][0]["value"] = "a"
+        G[0][2][0]["value"] = "b"
+        G[0][3][0]["value"] = "c"
+        mapping = {1: 4, 2: 4, 3: 4}
+        nx.relabel_nodes(G, mapping, copy=False)
+        # No ordering on keys enforced
+        assert {"value": "a"} in G[0][4].values()
+        assert {"value": "b"} in G[0][4].values()
+        assert {"value": "c"} in G[0][4].values()
+
+    def test_relabel_multidigraph_merge_inplace(self):
+        G = nx.MultiDiGraph([(0, 1), (0, 2), (0, 3)])
+        G[0][1][0]["value"] = "a"
+        G[0][2][0]["value"] = "b"
+        G[0][3][0]["value"] = "c"
+        mapping = {1: 4, 2: 4, 3: 4}
+        nx.relabel_nodes(G, mapping, copy=False)
+        # No ordering on keys enforced
+        assert {"value": "a"} in G[0][4].values()
+        assert {"value": "b"} in G[0][4].values()
+        assert {"value": "c"} in G[0][4].values()
+
+    def test_relabel_multidigraph_inout_copy(self):
+        G = nx.MultiDiGraph([(0, 4), (1, 4), (4, 2), (4, 3)])
+        G[0][4][0]["value"] = "a"
+        G[1][4][0]["value"] = "b"
+        G[4][2][0]["value"] = "c"
+        G[4][3][0]["value"] = "d"
+        G.add_edge(0, 4, key="x", value="e")
+        G.add_edge(4, 3, key="x", value="f")
+        mapping = {0: 9, 1: 9, 2: 9, 3: 9}
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        # No ordering on keys enforced
+        assert {"value": "a"} in H[9][4].values()
+        assert {"value": "b"} in H[9][4].values()
+        assert {"value": "c"} in H[4][9].values()
+        assert len(H[4][9]) == 3
+        assert {"value": "d"} in H[4][9].values()
+        assert {"value": "e"} in H[9][4].values()
+        assert {"value": "f"} in H[4][9].values()
+        assert len(H[9][4]) == 3
+
+    def test_relabel_multigraph_merge_copy(self):
+        G = nx.MultiGraph([(0, 1), (0, 2), (0, 3)])
+        G[0][1][0]["value"] = "a"
+        G[0][2][0]["value"] = "b"
+        G[0][3][0]["value"] = "c"
+        mapping = {1: 4, 2: 4, 3: 4}
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        assert {"value": "a"} in H[0][4].values()
+        assert {"value": "b"} in H[0][4].values()
+        assert {"value": "c"} in H[0][4].values()
+
+    def test_relabel_multidigraph_merge_copy(self):
+        G = nx.MultiDiGraph([(0, 1), (0, 2), (0, 3)])
+        G[0][1][0]["value"] = "a"
+        G[0][2][0]["value"] = "b"
+        G[0][3][0]["value"] = "c"
+        mapping = {1: 4, 2: 4, 3: 4}
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        assert {"value": "a"} in H[0][4].values()
+        assert {"value": "b"} in H[0][4].values()
+        assert {"value": "c"} in H[0][4].values()
+
+    def test_relabel_multigraph_nonnumeric_key(self):
+        for MG in (nx.MultiGraph, nx.MultiDiGraph):
+            for cc in (True, False):
+                G = nx.MultiGraph()
+                G.add_edge(0, 1, key="I", value="a")
+                G.add_edge(0, 2, key="II", value="b")
+                G.add_edge(0, 3, key="II", value="c")
+                mapping = {1: 4, 2: 4, 3: 4}
+                nx.relabel_nodes(G, mapping, copy=False)
+                assert {"value": "a"} in G[0][4].values()
+                assert {"value": "b"} in G[0][4].values()
+                assert {"value": "c"} in G[0][4].values()
+                assert 0 in G[0][4]
+                assert "I" in G[0][4]
+                assert "II" in G[0][4]
+
+    def test_relabel_circular(self):
+        G = nx.path_graph(3)
+        mapping = {0: 1, 1: 0}
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        with pytest.raises(nx.NetworkXUnfeasible):
+            H = nx.relabel_nodes(G, mapping, copy=False)
+
+    def test_relabel_preserve_node_order_full_mapping_with_copy_true(self):
+        G = nx.path_graph(3)
+        original_order = list(G.nodes())
+        mapping = {2: "a", 1: "b", 0: "c"}  # dictionary keys out of order on purpose
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        new_order = list(H.nodes())
+        assert [mapping.get(i, i) for i in original_order] == new_order
+
+    def test_relabel_preserve_node_order_full_mapping_with_copy_false(self):
+        G = nx.path_graph(3)
+        original_order = list(G)
+        mapping = {2: "a", 1: "b", 0: "c"}  # dictionary keys out of order on purpose
+        H = nx.relabel_nodes(G, mapping, copy=False)
+        new_order = list(H)
+        assert [mapping.get(i, i) for i in original_order] == new_order
+
+    def test_relabel_preserve_node_order_partial_mapping_with_copy_true(self):
+        G = nx.path_graph(3)
+        original_order = list(G)
+        mapping = {1: "a", 0: "b"}  # partial mapping and keys out of order on purpose
+        H = nx.relabel_nodes(G, mapping, copy=True)
+        new_order = list(H)
+        assert [mapping.get(i, i) for i in original_order] == new_order
+
+    def test_relabel_preserve_node_order_partial_mapping_with_copy_false(self):
+        G = nx.path_graph(3)
+        original_order = list(G)
+        mapping = {1: "a", 0: "b"}  # partial mapping and keys out of order on purpose
+        H = nx.relabel_nodes(G, mapping, copy=False)
+        new_order = list(H)
+        assert [mapping.get(i, i) for i in original_order] != new_order
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/__init__.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6abb178e4fd014bcec4c781fe98b8917d3ec876
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/__init__.py
@@ -0,0 +1,8 @@
+from networkx.utils.misc import *
+from networkx.utils.decorators import *
+from networkx.utils.random_sequence import *
+from networkx.utils.union_find import *
+from networkx.utils.rcm import *
+from networkx.utils.heaps import *
+from networkx.utils.configs import *
+from networkx.utils.backends import *
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/backends.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/backends.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a9250c43f0d7466c6f54f07783deae8c853e5a0
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/backends.py
@@ -0,0 +1,2143 @@
+# Notes about NetworkX namespace objects set up here:
+#
+# nx.utils.backends.backends:
+#   dict keyed by backend name to the backend entry point object.
+#   Filled using ``_get_backends("networkx.backends")`` during import of this module.
+#
+# nx.utils.backends.backend_info:
+#   dict keyed by backend name to the metadata returned by the function indicated
+#   by the "networkx.backend_info" entry point.
+#   Created as an empty dict while importing this module, but later filled using
+#   ``_set_configs_from_environment()`` at end of importing ``networkx/__init__.py``.
+#
+# nx.config:
+#   Config object for NetworkX config setting. Created using
+#   ``_set_configs_from_environment()`` at end of importing ``networkx/__init__.py``.
+#
+# private dicts:
+#   nx.utils.backends._loaded_backends:
+#       dict used to memoize loaded backends. Keyed by backend name to loaded backends.
+#
+#   nx.utils.backends._registered_algorithms:
+#       dict of all the dispatchable functions in networkx, keyed by _dispatchable
+#       function name to the wrapped function object.
+
+import inspect
+import itertools
+import logging
+import os
+import typing
+import warnings
+from functools import partial
+from importlib.metadata import entry_points
+
+import networkx as nx
+
+from .configs import BackendPriorities, Config, NetworkXConfig
+from .decorators import argmap
+
+__all__ = ["_dispatchable"]
+
+_logger = logging.getLogger(__name__)
+FAILED_TO_CONVERT = "FAILED_TO_CONVERT"
+
+
+def _get_backends(group, *, load_and_call=False):
+    """
+    Retrieve NetworkX ``backends`` and ``backend_info`` from the entry points.
+
+    Parameters
+    -----------
+    group : str
+        The entry_point to be retrieved.
+    load_and_call : bool, optional
+        If True, load and call the backend. Defaults to False.
+
+    Returns
+    --------
+    dict
+        A dictionary mapping backend names to their respective backend objects.
+
+    Notes
+    ------
+    If a backend is defined more than once, a warning is issued.
+    The "nx_loopback" backend is removed if it exists, as it is only available during testing.
+    A warning is displayed if an error occurs while loading a backend.
+    """
+    items = entry_points(group=group)
+    rv = {}
+    for ep in items:
+        if ep.name in rv:
+            warnings.warn(
+                f"networkx backend defined more than once: {ep.name}",
+                RuntimeWarning,
+                stacklevel=2,
+            )
+        elif load_and_call:
+            try:
+                rv[ep.name] = ep.load()()
+            except Exception as exc:
+                warnings.warn(
+                    f"Error encountered when loading info for backend {ep.name}: {exc}",
+                    RuntimeWarning,
+                    stacklevel=2,
+                )
+        else:
+            rv[ep.name] = ep
+    rv.pop("nx_loopback", None)
+    return rv
+
+
+# Note: "networkx" is in `backend_info` but ignored in `backends` and `config.backends`.
+# It is valid to use "networkx" as a backend argument and in `config.backend_priority`.
+# If we make "networkx" a "proper" backend, put it in `backends` and `config.backends`.
+backends = _get_backends("networkx.backends")
+
+# Use _set_configs_from_environment() below to fill backend_info dict as
+# the last step in importing networkx
+backend_info = {}
+
+# Load and cache backends on-demand
+_loaded_backends = {}  # type: ignore[var-annotated]
+_registered_algorithms = {}
+
+
+# Get default configuration from environment variables at import time
+def _comma_sep_to_list(string):
+    return [x_strip for x in string.strip().split(",") if (x_strip := x.strip())]
+
+
+def _set_configs_from_environment():
+    """Initialize ``config.backend_priority``, load backend_info and config.
+
+    This gets default values from environment variables (see ``nx.config`` for details).
+    This function is run at the very end of importing networkx. It is run at this time
+    to avoid loading backend_info before the rest of networkx is imported in case a
+    backend uses networkx for its backend_info (e.g. subclassing the Config class.)
+    """
+    # backend_info is defined above as empty dict. Fill it after import finishes.
+    backend_info.update(_get_backends("networkx.backend_info", load_and_call=True))
+    backend_info.update(
+        (backend, {}) for backend in backends.keys() - backend_info.keys()
+    )
+
+    # set up config based on backend_info and environment
+    backend_config = {}
+    for backend, info in backend_info.items():
+        if "default_config" not in info:
+            cfg = Config()
+        else:
+            cfg = info["default_config"]
+            if not isinstance(cfg, Config):
+                cfg = Config(**cfg)
+        backend_config[backend] = cfg
+    backend_config = Config(**backend_config)
+    # Setting doc of backends_config type is not setting doc of Config
+    # Config has __new__ method that returns instance with a unique type!
+    type(backend_config).__doc__ = "All installed NetworkX backends and their configs."
+
+    backend_priority = BackendPriorities(algos=[], generators=[])
+
+    config = NetworkXConfig(
+        backend_priority=backend_priority,
+        backends=backend_config,
+        cache_converted_graphs=bool(
+            os.environ.get("NETWORKX_CACHE_CONVERTED_GRAPHS", True)
+        ),
+        fallback_to_nx=bool(os.environ.get("NETWORKX_FALLBACK_TO_NX", False)),
+        warnings_to_ignore=set(
+            _comma_sep_to_list(os.environ.get("NETWORKX_WARNINGS_TO_IGNORE", ""))
+        ),
+    )
+
+    # Add "networkx" item to backend_info now b/c backend_config is set up
+    backend_info["networkx"] = {}
+
+    # NETWORKX_BACKEND_PRIORITY is the same as NETWORKX_BACKEND_PRIORITY_ALGOS
+    priorities = {
+        key[26:].lower(): val
+        for key, val in os.environ.items()
+        if key.startswith("NETWORKX_BACKEND_PRIORITY_")
+    }
+    backend_priority = config.backend_priority
+    backend_priority.algos = (
+        _comma_sep_to_list(priorities.pop("algos"))
+        if "algos" in priorities
+        else _comma_sep_to_list(
+            os.environ.get(
+                "NETWORKX_BACKEND_PRIORITY",
+                os.environ.get("NETWORKX_AUTOMATIC_BACKENDS", ""),
+            )
+        )
+    )
+    backend_priority.generators = _comma_sep_to_list(priorities.pop("generators", ""))
+    for key in sorted(priorities):
+        backend_priority[key] = _comma_sep_to_list(priorities[key])
+
+    return config
+
+
+def _do_nothing():
+    """This does nothing at all, yet it helps turn ``_dispatchable`` into functions.
+
+    Use this with the ``argmap`` decorator to turn ``self`` into a function. It results
+    in some small additional overhead compared to calling ``_dispatchable`` directly,
+    but ``argmap`` has the property that it can stack with other ``argmap``
+    decorators "for free". Being a function is better for REPRs and type-checkers.
+    """
+
+
+def _always_run(name, args, kwargs):
+    return True
+
+
+def _load_backend(backend_name):
+    if backend_name in _loaded_backends:
+        return _loaded_backends[backend_name]
+    if backend_name not in backends:
+        raise ImportError(f"'{backend_name}' backend is not installed")
+    rv = _loaded_backends[backend_name] = backends[backend_name].load()
+    if not hasattr(rv, "can_run"):
+        rv.can_run = _always_run
+    if not hasattr(rv, "should_run"):
+        rv.should_run = _always_run
+    return rv
+
+
+class _dispatchable:
+    _is_testing = False
+
+    def __new__(
+        cls,
+        func=None,
+        *,
+        name=None,
+        graphs="G",
+        edge_attrs=None,
+        node_attrs=None,
+        preserve_edge_attrs=False,
+        preserve_node_attrs=False,
+        preserve_graph_attrs=False,
+        preserve_all_attrs=False,
+        mutates_input=False,
+        returns_graph=False,
+        implemented_by_nx=True,
+    ):
+        """A decorator function that is used to redirect the execution of ``func``
+        function to its backend implementation.
+
+        This decorator allows the function to dispatch to different backend
+        implementations based on the input graph types, and also manages the
+        extra keywords ``backend`` and ``**backend_kwargs``.
+        Usage can be any of the following decorator forms:
+
+        - ``@_dispatchable``
+        - ``@_dispatchable()``
+        - ``@_dispatchable(name="override_name")``
+        - ``@_dispatchable(graphs="graph_var_name")``
+        - ``@_dispatchable(edge_attrs="weight")``
+        - ``@_dispatchable(graphs={"G": 0, "H": 1}, edge_attrs={"weight": "default"})``
+            with 0 and 1 giving the position in the signature function for graph
+            objects. When ``edge_attrs`` is a dict, keys are keyword names and values
+            are defaults.
+
+        Parameters
+        ----------
+        func : callable, optional (default: None)
+            The function to be decorated. If None, ``_dispatchable`` returns a
+            partial object that can be used to decorate a function later. If ``func``
+            is a callable, returns a new callable object that dispatches to a backend
+            function based on input graph types.
+
+        name : str, optional (default: name of `func`)
+            The name for the function as used for dispatching. If not provided,
+            the name of ``func`` will be used. ``name`` is useful to avoid name
+            conflicts, as all dispatched functions live in a single namespace.
+            For example, ``nx.tournament.is_strongly_connected`` had a name
+            conflict with the standard ``nx.is_strongly_connected``, so we used
+            ``@_dispatchable(name="tournament_is_strongly_connected")``.
+
+        graphs : str or dict or None, optional (default: "G")
+            If a string, the parameter name of the graph, which must be the first
+            argument of the wrapped function. If more than one graph is required
+            for the function (or if the graph is not the first argument), provide
+            a dict keyed by graph parameter name to the value parameter position.
+            A question mark in the name indicates an optional argument.
+            For example, ``@_dispatchable(graphs={"G": 0, "auxiliary?": 4})``
+            indicates the 0th parameter ``G`` of the function is a required graph,
+            and the 4th parameter ``auxiliary?`` is an optional graph.
+            To indicate that an argument is a list of graphs, do ``"[graphs]"``.
+            Use ``graphs=None``, if *no* arguments are NetworkX graphs such as for
+            graph generators, readers, and conversion functions.
+
+        edge_attrs : str or dict, optional (default: None)
+            ``edge_attrs`` holds information about edge attribute arguments
+            and default values for those edge attributes.
+            If a string, ``edge_attrs`` holds the function argument name that
+            indicates a single edge attribute to include in the converted graph.
+            The default value for this attribute is 1. To indicate that an argument
+            is a list of attributes (all with default value 1), use e.g. ``"[attrs]"``.
+            If a dict, ``edge_attrs`` holds a dict keyed by argument names, with
+            values that are either the default value or, if a string, the argument
+            name that indicates the default value.
+            If None, function does not use edge attributes.
+
+        node_attrs : str or dict, optional
+            Like ``edge_attrs``, but for node attributes.
+
+        preserve_edge_attrs : bool or str or dict, optional (default: False)
+            If bool, whether to preserve all edge attributes.
+            If a string, the parameter name that may indicate (with ``True`` or a
+            callable argument) whether all edge attributes should be preserved
+            when converting graphs to a backend graph type.
+            If a dict of form ``{graph_name: {attr: default}}``, indicate
+            pre-determined edge attributes (and defaults) to preserve for the
+            indicated input graph.
+
+        preserve_node_attrs : bool or str or dict, optional (default: False)
+            Like ``preserve_edge_attrs``, but for node attributes.
+
+        preserve_graph_attrs : bool or set, optional (default: False)
+            If bool, whether to preserve all graph attributes.
+            If set, which input graph arguments to preserve graph attributes.
+
+        preserve_all_attrs : bool, optional (default: False)
+            Whether to preserve all edge, node and graph attributes.
+            If True, this overrides all the other preserve_*_attrs.
+
+        mutates_input : bool or dict, optional (default: False)
+            If bool, whether the function mutates an input graph argument.
+            If dict of ``{arg_name: arg_pos}``, name and position of bool arguments
+            that indicate whether an input graph will be mutated, and ``arg_name``
+            may begin with ``"not "`` to negate the logic (for example, ``"not copy"``
+            means we mutate the input graph when the ``copy`` argument is False).
+            By default, dispatching doesn't convert input graphs to a different
+            backend for functions that mutate input graphs.
+
+        returns_graph : bool, optional (default: False)
+            Whether the function can return or yield a graph object. By default,
+            dispatching doesn't convert input graphs to a different backend for
+            functions that return graphs.
+
+        implemented_by_nx : bool, optional (default: True)
+            Whether the function is implemented by NetworkX. If it is not, then the
+            function is included in NetworkX only as an API to dispatch to backends.
+            Default is True.
+        """
+        if func is None:
+            return partial(
+                _dispatchable,
+                name=name,
+                graphs=graphs,
+                edge_attrs=edge_attrs,
+                node_attrs=node_attrs,
+                preserve_edge_attrs=preserve_edge_attrs,
+                preserve_node_attrs=preserve_node_attrs,
+                preserve_graph_attrs=preserve_graph_attrs,
+                preserve_all_attrs=preserve_all_attrs,
+                mutates_input=mutates_input,
+                returns_graph=returns_graph,
+                implemented_by_nx=implemented_by_nx,
+            )
+        if isinstance(func, str):
+            raise TypeError("'name' and 'graphs' must be passed by keyword") from None
+        # If name not provided, use the name of the function
+        if name is None:
+            name = func.__name__
+
+        self = object.__new__(cls)
+
+        # standard function-wrapping stuff
+        # __annotations__ not used
+        self.__name__ = func.__name__
+        # self.__doc__ = func.__doc__  # __doc__ handled as cached property
+        self.__defaults__ = func.__defaults__
+        # Add `backend=` keyword argument to allow backend choice at call-time
+        if func.__kwdefaults__:
+            self.__kwdefaults__ = {**func.__kwdefaults__, "backend": None}
+        else:
+            self.__kwdefaults__ = {"backend": None}
+        self.__module__ = func.__module__
+        self.__qualname__ = func.__qualname__
+        self.__dict__.update(func.__dict__)
+        self.__wrapped__ = func
+
+        # Supplement docstring with backend info; compute and cache when needed
+        self._orig_doc = func.__doc__
+        self._cached_doc = None
+
+        self.orig_func = func
+        self.name = name
+        self.edge_attrs = edge_attrs
+        self.node_attrs = node_attrs
+        self.preserve_edge_attrs = preserve_edge_attrs or preserve_all_attrs
+        self.preserve_node_attrs = preserve_node_attrs or preserve_all_attrs
+        self.preserve_graph_attrs = preserve_graph_attrs or preserve_all_attrs
+        self.mutates_input = mutates_input
+        # Keep `returns_graph` private for now, b/c we may extend info on return types
+        self._returns_graph = returns_graph
+
+        if edge_attrs is not None and not isinstance(edge_attrs, str | dict):
+            raise TypeError(
+                f"Bad type for edge_attrs: {type(edge_attrs)}. Expected str or dict."
+            ) from None
+        if node_attrs is not None and not isinstance(node_attrs, str | dict):
+            raise TypeError(
+                f"Bad type for node_attrs: {type(node_attrs)}. Expected str or dict."
+            ) from None
+        if not isinstance(self.preserve_edge_attrs, bool | str | dict):
+            raise TypeError(
+                f"Bad type for preserve_edge_attrs: {type(self.preserve_edge_attrs)}."
+                " Expected bool, str, or dict."
+            ) from None
+        if not isinstance(self.preserve_node_attrs, bool | str | dict):
+            raise TypeError(
+                f"Bad type for preserve_node_attrs: {type(self.preserve_node_attrs)}."
+                " Expected bool, str, or dict."
+            ) from None
+        if not isinstance(self.preserve_graph_attrs, bool | set):
+            raise TypeError(
+                f"Bad type for preserve_graph_attrs: {type(self.preserve_graph_attrs)}."
+                " Expected bool or set."
+            ) from None
+        if not isinstance(self.mutates_input, bool | dict):
+            raise TypeError(
+                f"Bad type for mutates_input: {type(self.mutates_input)}."
+                " Expected bool or dict."
+            ) from None
+        if not isinstance(self._returns_graph, bool):
+            raise TypeError(
+                f"Bad type for returns_graph: {type(self._returns_graph)}."
+                " Expected bool."
+            ) from None
+
+        if isinstance(graphs, str):
+            graphs = {graphs: 0}
+        elif graphs is None:
+            pass
+        elif not isinstance(graphs, dict):
+            raise TypeError(
+                f"Bad type for graphs: {type(graphs)}. Expected str or dict."
+            ) from None
+        elif len(graphs) == 0:
+            raise KeyError("'graphs' must contain at least one variable name") from None
+
+        # This dict comprehension is complicated for better performance; equivalent shown below.
+        self.optional_graphs = set()
+        self.list_graphs = set()
+        if graphs is None:
+            self.graphs = {}
+        else:
+            self.graphs = {
+                self.optional_graphs.add(val := k[:-1]) or val
+                if (last := k[-1]) == "?"
+                else self.list_graphs.add(val := k[1:-1]) or val
+                if last == "]"
+                else k: v
+                for k, v in graphs.items()
+            }
+        # The above is equivalent to:
+        # self.optional_graphs = {k[:-1] for k in graphs if k[-1] == "?"}
+        # self.list_graphs = {k[1:-1] for k in graphs if k[-1] == "]"}
+        # self.graphs = {k[:-1] if k[-1] == "?" else k: v for k, v in graphs.items()}
+
+        # Compute and cache the signature on-demand
+        self._sig = None
+
+        # Which backends implement this function?
+        self.backends = {
+            backend
+            for backend, info in backend_info.items()
+            if "functions" in info and name in info["functions"]
+        }
+        if implemented_by_nx:
+            self.backends.add("networkx")
+
+        if name in _registered_algorithms:
+            raise KeyError(
+                f"Algorithm already exists in dispatch registry: {name}"
+            ) from None
+        # Use the `argmap` decorator to turn `self` into a function. This does result
+        # in small additional overhead compared to calling `_dispatchable` directly,
+        # but `argmap` has the property that it can stack with other `argmap`
+        # decorators "for free". Being a function is better for REPRs and type-checkers.
+        self = argmap(_do_nothing)(self)
+        _registered_algorithms[name] = self
+        return self
+
+    @property
+    def __doc__(self):
+        """If the cached documentation exists, it is returned.
+        Otherwise, the documentation is generated using _make_doc() method,
+        cached, and then returned."""
+
+        rv = self._cached_doc
+        if rv is None:
+            rv = self._cached_doc = self._make_doc()
+        return rv
+
+    @__doc__.setter
+    def __doc__(self, val):
+        """Sets the original documentation to the given value and resets the
+        cached documentation."""
+
+        self._orig_doc = val
+        self._cached_doc = None
+
+    @property
+    def __signature__(self):
+        """Return the signature of the original function, with the addition of
+        the `backend` and `backend_kwargs` parameters."""
+
+        if self._sig is None:
+            sig = inspect.signature(self.orig_func)
+            # `backend` is now a reserved argument used by dispatching.
+            # assert "backend" not in sig.parameters
+            if not any(
+                p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()
+            ):
+                sig = sig.replace(
+                    parameters=[
+                        *sig.parameters.values(),
+                        inspect.Parameter(
+                            "backend", inspect.Parameter.KEYWORD_ONLY, default=None
+                        ),
+                        inspect.Parameter(
+                            "backend_kwargs", inspect.Parameter.VAR_KEYWORD
+                        ),
+                    ]
+                )
+            else:
+                *parameters, var_keyword = sig.parameters.values()
+                sig = sig.replace(
+                    parameters=[
+                        *parameters,
+                        inspect.Parameter(
+                            "backend", inspect.Parameter.KEYWORD_ONLY, default=None
+                        ),
+                        var_keyword,
+                    ]
+                )
+            self._sig = sig
+        return self._sig
+
+    # Fast, simple path if no backends are installed
+    def _call_if_no_backends_installed(self, /, *args, backend=None, **kwargs):
+        """Returns the result of the original function (no backends installed)."""
+        if backend is not None and backend != "networkx":
+            raise ImportError(f"'{backend}' backend is not installed")
+        if "networkx" not in self.backends:
+            raise NotImplementedError(
+                f"'{self.name}' is not implemented by 'networkx' backend. "
+                "This function is included in NetworkX as an API to dispatch to "
+                "other backends."
+            )
+        return self.orig_func(*args, **kwargs)
+
+    # Dispatch to backends based on inputs, `backend=` arg, or configuration
+    def _call_if_any_backends_installed(self, /, *args, backend=None, **kwargs):
+        """Returns the result of the original function, or the backend function if
+        the backend is specified and that backend implements `func`."""
+        # Use `backend_name` in this function instead of `backend`.
+        # This is purely for aesthetics and to make it easier to search for this
+        # variable since "backend" is used in many comments and log/error messages.
+        backend_name = backend
+        if backend_name is not None and backend_name not in backend_info:
+            raise ImportError(f"'{backend_name}' backend is not installed")
+
+        graphs_resolved = {}
+        for gname, pos in self.graphs.items():
+            if pos < len(args):
+                if gname in kwargs:
+                    raise TypeError(f"{self.name}() got multiple values for {gname!r}")
+                graph = args[pos]
+            elif gname in kwargs:
+                graph = kwargs[gname]
+            elif gname not in self.optional_graphs:
+                raise TypeError(
+                    f"{self.name}() missing required graph argument: {gname}"
+                )
+            else:
+                continue
+            if graph is None:
+                if gname not in self.optional_graphs:
+                    raise TypeError(
+                        f"{self.name}() required graph argument {gname!r} is None; must be a graph"
+                    )
+            else:
+                graphs_resolved[gname] = graph
+
+        # Alternative to the above that does not check duplicated args or missing required graphs.
+        # graphs_resolved = {
+        #     gname: graph
+        #     for gname, pos in self.graphs.items()
+        #     if (graph := args[pos] if pos < len(args) else kwargs.get(gname)) is not None
+        # }
+
+        # Check if any graph comes from a backend
+        if self.list_graphs:
+            # Make sure we don't lose values by consuming an iterator
+            args = list(args)
+            for gname in self.list_graphs & graphs_resolved.keys():
+                list_of_graphs = list(graphs_resolved[gname])
+                graphs_resolved[gname] = list_of_graphs
+                if gname in kwargs:
+                    kwargs[gname] = list_of_graphs
+                else:
+                    args[self.graphs[gname]] = list_of_graphs
+
+            graph_backend_names = {
+                getattr(g, "__networkx_backend__", None)
+                for gname, g in graphs_resolved.items()
+                if gname not in self.list_graphs
+            }
+            for gname in self.list_graphs & graphs_resolved.keys():
+                graph_backend_names.update(
+                    getattr(g, "__networkx_backend__", None)
+                    for g in graphs_resolved[gname]
+                )
+        else:
+            graph_backend_names = {
+                getattr(g, "__networkx_backend__", None)
+                for g in graphs_resolved.values()
+            }
+
+        backend_priority = nx.config.backend_priority.get(
+            self.name,
+            nx.config.backend_priority.generators
+            if self._returns_graph
+            else nx.config.backend_priority.algos,
+        )
+        fallback_to_nx = nx.config.fallback_to_nx and "networkx" in self.backends
+        if self._is_testing and backend_priority and backend_name is None:
+            # Special path if we are running networkx tests with a backend.
+            # This even runs for (and handles) functions that mutate input graphs.
+            return self._convert_and_call_for_tests(
+                backend_priority[0],
+                args,
+                kwargs,
+                fallback_to_nx=fallback_to_nx,
+            )
+
+        graph_backend_names.discard(None)
+        if backend_name is not None:
+            # Must run with the given backend.
+            # `can_run` only used for better log and error messages.
+            # Check `mutates_input` for logging, not behavior.
+            backend_kwarg_msg = (
+                "No other backends will be attempted, because the backend was "
+                f"specified with the `backend='{backend_name}'` keyword argument."
+            )
+            extra_message = (
+                f"'{backend_name}' backend raised NotImplementedError when calling "
+                f"'{self.name}'. {backend_kwarg_msg}"
+            )
+            if not graph_backend_names or graph_backend_names == {backend_name}:
+                # All graphs are backend graphs--no need to convert!
+                if self._can_backend_run(backend_name, args, kwargs):
+                    return self._call_with_backend(
+                        backend_name, args, kwargs, extra_message=extra_message
+                    )
+                if self._does_backend_have(backend_name):
+                    extra = " for the given arguments"
+                else:
+                    extra = ""
+                raise NotImplementedError(
+                    f"'{self.name}' is not implemented by '{backend_name}' backend"
+                    f"{extra}. {backend_kwarg_msg}"
+                )
+            if self._can_convert(backend_name, graph_backend_names):
+                if self._can_backend_run(backend_name, args, kwargs):
+                    if self._will_call_mutate_input(args, kwargs):
+                        _logger.debug(
+                            "'%s' will mutate an input graph. This prevents automatic conversion "
+                            "to, and use of, backends listed in `nx.config.backend_priority`. "
+                            "Using backend specified by the "
+                            "`backend='%s'` keyword argument. This may change behavior by not "
+                            "mutating inputs.",
+                            self.name,
+                            backend_name,
+                        )
+                        mutations = []
+                    else:
+                        mutations = None
+                    rv = self._convert_and_call(
+                        backend_name,
+                        graph_backend_names,
+                        args,
+                        kwargs,
+                        extra_message=extra_message,
+                        mutations=mutations,
+                    )
+                    if mutations:
+                        for cache, key in mutations:
+                            # If the call mutates inputs, then remove all inputs gotten
+                            # from cache. We do this after all conversions (and call) so
+                            # that a graph can be gotten from a cache multiple times.
+                            cache.pop(key, None)
+                    return rv
+                if self._does_backend_have(backend_name):
+                    extra = " for the given arguments"
+                else:
+                    extra = ""
+                raise NotImplementedError(
+                    f"'{self.name}' is not implemented by '{backend_name}' backend"
+                    f"{extra}. {backend_kwarg_msg}"
+                )
+            if len(graph_backend_names) == 1:
+                maybe_s = ""
+                graph_backend_names = f"'{next(iter(graph_backend_names))}'"
+            else:
+                maybe_s = "s"
+            raise TypeError(
+                f"'{self.name}' is unable to convert graph from backend{maybe_s} "
+                f"{graph_backend_names} to '{backend_name}' backend, which was "
+                f"specified with the `backend='{backend_name}'` keyword argument. "
+                f"{backend_kwarg_msg}"
+            )
+
+        if self._will_call_mutate_input(args, kwargs):
+            # The current behavior for functions that mutate input graphs:
+            #
+            # 1. If backend is specified by `backend=` keyword, use it (done above).
+            # 2. If inputs are from one backend, try to use it.
+            # 3. If all input graphs are instances of `nx.Graph`, then run with the
+            #    default "networkx" implementation.
+            #
+            # Do not automatically convert if a call will mutate inputs, because doing
+            # so would change behavior. Hence, we should fail if there are multiple input
+            # backends or if the input backend does not implement the function. However,
+            # we offer a way for backends to circumvent this if they do not implement
+            # this function: we will fall back to the default "networkx" implementation
+            # without using conversions if all input graphs are subclasses of `nx.Graph`.
+            mutate_msg = (
+                "conversions between backends (if configured) will not be attempted "
+                "because the original input graph would not be mutated. Using the "
+                "backend keyword e.g. `backend='some_backend'` will force conversions "
+                "and not mutate the original input graph."
+            )
+            fallback_msg = (
+                "This call will mutate inputs, so fall back to 'networkx' "
+                "backend (without converting) since all input graphs are "
+                "instances of nx.Graph and are hopefully compatible."
+            )
+            if len(graph_backend_names) == 1:
+                [backend_name] = graph_backend_names
+                msg_template = (
+                    f"Backend '{backend_name}' does not implement '{self.name}'%s. "
+                    f"This call will mutate an input, so automatic {mutate_msg}"
+                )
+                # `can_run` is only used for better log and error messages
+                try:
+                    if self._can_backend_run(backend_name, args, kwargs):
+                        return self._call_with_backend(
+                            backend_name,
+                            args,
+                            kwargs,
+                            extra_message=msg_template % " with these arguments",
+                        )
+                except NotImplementedError as exc:
+                    if all(isinstance(g, nx.Graph) for g in graphs_resolved.values()):
+                        _logger.debug(
+                            "Backend '%s' raised when calling '%s': %s. %s",
+                            backend_name,
+                            self.name,
+                            exc,
+                            fallback_msg,
+                        )
+                    else:
+                        raise
+                else:
+                    if fallback_to_nx and all(
+                        # Consider dropping the `isinstance` check here to allow
+                        # duck-type graphs, but let's wait for a backend to ask us.
+                        isinstance(g, nx.Graph)
+                        for g in graphs_resolved.values()
+                    ):
+                        # Log that we are falling back to networkx
+                        _logger.debug(
+                            "Backend '%s' can't run '%s'. %s",
+                            backend_name,
+                            self.name,
+                            fallback_msg,
+                        )
+                    else:
+                        if self._does_backend_have(backend_name):
+                            extra = " with these arguments"
+                        else:
+                            extra = ""
+                        raise NotImplementedError(msg_template % extra)
+            elif fallback_to_nx and all(
+                # Consider dropping the `isinstance` check here to allow
+                # duck-type graphs, but let's wait for a backend to ask us.
+                isinstance(g, nx.Graph)
+                for g in graphs_resolved.values()
+            ):
+                # Log that we are falling back to networkx
+                _logger.debug(
+                    "'%s' was called with inputs from multiple backends: %s. %s",
+                    self.name,
+                    graph_backend_names,
+                    fallback_msg,
+                )
+            else:
+                raise RuntimeError(
+                    f"'{self.name}' will mutate an input, but it was called with "
+                    f"inputs from multiple backends: {graph_backend_names}. "
+                    f"Automatic {mutate_msg}"
+                )
+            # At this point, no backends are available to handle the call with
+            # the input graph types, but if the input graphs are compatible
+            # nx.Graph instances, fall back to networkx without converting.
+            return self.orig_func(*args, **kwargs)
+
+        # We may generalize fallback configuration as e.g. `nx.config.backend_fallback`
+        if fallback_to_nx or not graph_backend_names:
+            # Use "networkx" by default if there are no inputs from backends.
+            # For example, graph generators should probably return NetworkX graphs
+            # instead of raising NotImplementedError.
+            backend_fallback = ["networkx"]
+        else:
+            backend_fallback = []
+
+        # ##########################
+        # # How this behaves today #
+        # ##########################
+        #
+        # The prose below describes the implementation and a *possible* way to
+        # generalize "networkx" as "just another backend". The code is structured
+        # to perhaps someday support backend-to-backend conversions (including
+        # simply passing objects from one backend directly to another backend;
+        # the dispatch machinery does not necessarily need to perform conversions),
+        # but since backend-to-backend matching is not yet supported, the following
+        # code is merely a convenient way to implement dispatch behaviors that have
+        # been carefully developed since NetworkX 3.0 and to include falling back
+        # to the default NetworkX implementation.
+        #
+        # The current behavior for functions that don't mutate input graphs:
+        #
+        # 1. If backend is specified by `backend=` keyword, use it (done above).
+        # 2. If input is from a backend other than "networkx", try to use it.
+        #    - Note: if present, "networkx" graphs will be converted to the backend.
+        # 3. If input is from "networkx" (or no backend), try to use backends from
+        #    `backend_priority` before running with the default "networkx" implementation.
+        # 4. If configured, "fall back" and run with the default "networkx" implementation.
+        #
+        # ################################################
+        # # How this is implemented and may work someday #
+        # ################################################
+        #
+        # Let's determine the order of backends we should try according
+        # to `backend_priority`, `backend_fallback`, and input backends.
+        # There are two† dimensions of priorities to consider:
+        #   backend_priority > unspecified > backend_fallback
+        # and
+        #   backend of an input > not a backend of an input
+        # These are combined to form five groups of priorities as such:
+        #
+        #                    input   ~input
+        #                  +-------+-------+
+        # backend_priority |   1   |   2   |
+        #      unspecified |   3   |  N/A  | (if only 1)
+        # backend_fallback |   4   |   5   |
+        #                  +-------+-------+
+        #
+        # This matches the behaviors we developed in versions 3.0 to 3.2, it
+        # ought to cover virtually all use cases we expect, and I (@eriknw) don't
+        # think it can be done any simpler (although it can be generalized further
+        # and made to be more complicated to capture 100% of *possible* use cases).
+        # Some observations:
+        #
+        #   1. If an input is in `backend_priority`, it will be used before trying a
+        #      backend that is higher priority in `backend_priority` and not an input.
+        #   2. To prioritize converting from one backend to another even if both implement
+        #      a function, list one in `backend_priority` and one in `backend_fallback`.
+        #   3. To disable conversions, set `backend_priority` and `backend_fallback` to [].
+        #
+        # †: There is actually a third dimension of priorities:
+        #        should_run == True > should_run == False
+        #    Backends with `can_run == True` and `should_run == False` are tried last.
+        #
+        seen = set()
+        group1 = []  # In backend_priority, and an input
+        group2 = []  # In backend_priority, but not an input
+        for name in backend_priority:
+            if name in seen:
+                continue
+            seen.add(name)
+            if name in graph_backend_names:
+                group1.append(name)
+            else:
+                group2.append(name)
+        group4 = []  # In backend_fallback, and an input
+        group5 = []  # In backend_fallback, but not an input
+        for name in backend_fallback:
+            if name in seen:
+                continue
+            seen.add(name)
+            if name in graph_backend_names:
+                group4.append(name)
+            else:
+                group5.append(name)
+        # An input, but not in backend_priority or backend_fallback.
+        group3 = graph_backend_names - seen
+        if len(group3) > 1:
+            # `group3` backends are not configured for automatic conversion or fallback.
+            # There are at least two issues if this group contains multiple backends:
+            #
+            #   1. How should we prioritize them? We have no good way to break ties.
+            #      Although we could arbitrarily choose alphabetical or left-most,
+            #      let's follow the Zen of Python and refuse the temptation to guess.
+            #   2. We probably shouldn't automatically convert to these backends,
+            #      because we are not configured to do so.
+            #
+            # (2) is important to allow disabling all conversions by setting both
+            # `nx.config.backend_priority` and `nx.config.backend_fallback` to [].
+            #
+            # If there is a single backend in `group3`, then giving it priority over
+            # the fallback backends is what is generally expected. For example, this
+            # allows input graphs of `backend_fallback` backends (such as "networkx")
+            # to be converted to, and run with, the unspecified backend.
+            _logger.debug(
+                "Call to '%s' has inputs from multiple backends, %s, that "
+                "have no priority set in `nx.config.backend_priority`, "
+                "so automatic conversions to "
+                "these backends will not be attempted.",
+                self.name,
+                group3,
+            )
+            group3 = ()
+
+        try_order = list(itertools.chain(group1, group2, group3, group4, group5))
+        if len(try_order) > 1:
+            # Should we consider adding an option for more verbose logging?
+            # For example, we could explain the order of `try_order` in detail.
+            _logger.debug(
+                "Call to '%s' has inputs from %s backends, and will try to use "
+                "backends in the following order: %s",
+                self.name,
+                graph_backend_names or "no",
+                try_order,
+            )
+        backends_to_try_again = []
+        for is_not_first, backend_name in enumerate(try_order):
+            if is_not_first:
+                _logger.debug("Trying next backend: '%s'", backend_name)
+            try:
+                if not graph_backend_names or graph_backend_names == {backend_name}:
+                    if self._can_backend_run(backend_name, args, kwargs):
+                        return self._call_with_backend(backend_name, args, kwargs)
+                elif self._can_convert(
+                    backend_name, graph_backend_names
+                ) and self._can_backend_run(backend_name, args, kwargs):
+                    if self._should_backend_run(backend_name, args, kwargs):
+                        rv = self._convert_and_call(
+                            backend_name, graph_backend_names, args, kwargs
+                        )
+                        if (
+                            self._returns_graph
+                            and graph_backend_names
+                            and backend_name not in graph_backend_names
+                        ):
+                            # If the function has graph inputs and graph output, we try
+                            # to make it so the backend of the return type will match the
+                            # backend of the input types. In case this is not possible,
+                            # let's tell the user that the backend of the return graph
+                            # has changed. Perhaps we could try to convert back, but
+                            # "fallback" backends for graph generators should typically
+                            # be compatible with NetworkX graphs.
+                            _logger.debug(
+                                "Call to '%s' is returning a graph from a different "
+                                "backend! It has inputs from %s backends, but ran with "
+                                "'%s' backend and is returning graph from '%s' backend",
+                                self.name,
+                                graph_backend_names,
+                                backend_name,
+                                backend_name,
+                            )
+                        return rv
+                    # `should_run` is False, but `can_run` is True, so try again later
+                    backends_to_try_again.append(backend_name)
+            except NotImplementedError as exc:
+                _logger.debug(
+                    "Backend '%s' raised when calling '%s': %s",
+                    backend_name,
+                    self.name,
+                    exc,
+                )
+
+        # We are about to fail. Let's try backends with can_run=True and should_run=False.
+        # This is unlikely to help today since we try to run with "networkx" before this.
+        for backend_name in backends_to_try_again:
+            _logger.debug(
+                "Trying backend: '%s' (ignoring `should_run=False`)", backend_name
+            )
+            try:
+                rv = self._convert_and_call(
+                    backend_name, graph_backend_names, args, kwargs
+                )
+                if (
+                    self._returns_graph
+                    and graph_backend_names
+                    and backend_name not in graph_backend_names
+                ):
+                    _logger.debug(
+                        "Call to '%s' is returning a graph from a different "
+                        "backend! It has inputs from %s backends, but ran with "
+                        "'%s' backend and is returning graph from '%s' backend",
+                        self.name,
+                        graph_backend_names,
+                        backend_name,
+                        backend_name,
+                    )
+                return rv
+            except NotImplementedError as exc:
+                _logger.debug(
+                    "Backend '%s' raised when calling '%s': %s",
+                    backend_name,
+                    self.name,
+                    exc,
+                )
+        # As a final effort, we could try to convert and run with `group3` backends
+        # that we discarded when `len(group3) > 1`, but let's not consider doing
+        # so until there is a reasonable request for it.
+
+        if len(unspecified_backends := graph_backend_names - seen) > 1:
+            raise TypeError(
+                f"Unable to convert inputs from {graph_backend_names} backends and "
+                f"run '{self.name}'. NetworkX is configured to automatically convert "
+                f"to {try_order} backends. To remedy this, you may enable automatic "
+                f"conversion to {unspecified_backends} backends by adding them to "
+                "`nx.config.backend_priority`, or you "
+                "may specify a backend to use with the `backend=` keyword argument."
+            )
+        if "networkx" not in self.backends:
+            extra = (
+                " This function is included in NetworkX as an API to dispatch to "
+                "other backends."
+            )
+        else:
+            extra = ""
+        raise NotImplementedError(
+            f"'{self.name}' is not implemented by {try_order} backends. To remedy "
+            "this, you may enable automatic conversion to more backends (including "
+            "'networkx') by adding them to `nx.config.backend_priority`, "
+            "or you may specify a backend to use with "
+            f"the `backend=` keyword argument.{extra}"
+        )
+
+    # Dispatch only if there exist any installed backend(s)
+    __call__: typing.Callable = (
+        _call_if_any_backends_installed if backends else _call_if_no_backends_installed
+    )
+
+    def _will_call_mutate_input(self, args, kwargs):
+        # Fairly few nx functions mutate the input graph. Most that do, always do.
+        # So a boolean input indicates "always" or "never".
+        if isinstance((mutates_input := self.mutates_input), bool):
+            return mutates_input
+
+        # The ~10 other nx functions either use "copy=True" to control mutation or
+        # an arg naming an edge/node attribute to mutate (None means no mutation).
+        # Now `mutates_input` is a dict keyed by arg_name to its func-sig position.
+        # The `copy=` args are keyed as "not copy" to mean "negate the copy argument".
+        # Keys w/o "not " mean the call mutates only when the arg value `is not None`.
+        #
+        # This section might need different code if new functions mutate in new ways.
+        #
+        # NetworkX doesn't have any `mutates_input` dicts with more than 1 item.
+        # But we treat it like it might have more than 1 item for generality.
+        n = len(args)
+        return any(
+            (args[arg_pos] if n > arg_pos else kwargs.get(arg_name)) is not None
+            if not arg_name.startswith("not ")
+            # This assumes that e.g. `copy=True` is the default
+            else not (args[arg_pos] if n > arg_pos else kwargs.get(arg_name[4:], True))
+            for arg_name, arg_pos in mutates_input.items()
+        )
+
+    def _can_convert(self, backend_name, graph_backend_names):
+        # Backend-to-backend conversion not supported yet.
+        # We can only convert to and from networkx.
+        rv = backend_name == "networkx" or graph_backend_names.issubset(
+            {"networkx", backend_name}
+        )
+        if not rv:
+            _logger.debug(
+                "Unable to convert from %s backends to '%s' backend",
+                graph_backend_names,
+                backend_name,
+            )
+        return rv
+
+    def _does_backend_have(self, backend_name):
+        """Does the specified backend have this algorithm?"""
+        if backend_name == "networkx":
+            return "networkx" in self.backends
+        # Inspect the backend; don't trust metadata used to create `self.backends`
+        backend = _load_backend(backend_name)
+        return hasattr(backend, self.name)
+
+    def _can_backend_run(self, backend_name, args, kwargs):
+        """Can the specified backend run this algorithm with these arguments?"""
+        if backend_name == "networkx":
+            return "networkx" in self.backends
+        backend = _load_backend(backend_name)
+        # `backend.can_run` and `backend.should_run` may return strings that describe
+        # why they can't or shouldn't be run.
+        if not hasattr(backend, self.name):
+            _logger.debug(
+                "Backend '%s' does not implement '%s'", backend_name, self.name
+            )
+            return False
+        can_run = backend.can_run(self.name, args, kwargs)
+        if isinstance(can_run, str) or not can_run:
+            reason = f", because: {can_run}" if isinstance(can_run, str) else ""
+            _logger.debug(
+                "Backend '%s' can't run `%s` with arguments: %s%s",
+                backend_name,
+                self.name,
+                _LazyArgsRepr(self, args, kwargs),
+                reason,
+            )
+            return False
+        return True
+
+    def _should_backend_run(self, backend_name, args, kwargs):
+        """Should the specified backend run this algorithm with these arguments?
+
+        Note that this does not check ``backend.can_run``.
+        """
+        # `backend.can_run` and `backend.should_run` may return strings that describe
+        # why they can't or shouldn't be run.
+        # `_should_backend_run` may assume that `_can_backend_run` returned True.
+        if backend_name == "networkx":
+            return True
+        backend = _load_backend(backend_name)
+        should_run = backend.should_run(self.name, args, kwargs)
+        if isinstance(should_run, str) or not should_run:
+            reason = f", because: {should_run}" if isinstance(should_run, str) else ""
+            _logger.debug(
+                "Backend '%s' shouldn't run `%s` with arguments: %s%s",
+                backend_name,
+                self.name,
+                _LazyArgsRepr(self, args, kwargs),
+                reason,
+            )
+            return False
+        return True
+
+    def _convert_arguments(self, backend_name, args, kwargs, *, use_cache, mutations):
+        """Convert graph arguments to the specified backend.
+
+        Returns
+        -------
+        args tuple and kwargs dict
+        """
+        bound = self.__signature__.bind(*args, **kwargs)
+        bound.apply_defaults()
+        if not self.graphs:
+            bound_kwargs = bound.kwargs
+            del bound_kwargs["backend"]
+            return bound.args, bound_kwargs
+        if backend_name == "networkx":
+            # `backend_interface.convert_from_nx` preserves everything
+            preserve_edge_attrs = preserve_node_attrs = preserve_graph_attrs = True
+        else:
+            preserve_edge_attrs = self.preserve_edge_attrs
+            preserve_node_attrs = self.preserve_node_attrs
+            preserve_graph_attrs = self.preserve_graph_attrs
+            edge_attrs = self.edge_attrs
+            node_attrs = self.node_attrs
+        # Convert graphs into backend graph-like object
+        # Include the edge and/or node labels if provided to the algorithm
+        if preserve_edge_attrs is False:
+            # e.g. `preserve_edge_attrs=False`
+            pass
+        elif preserve_edge_attrs is True:
+            # e.g. `preserve_edge_attrs=True`
+            edge_attrs = None
+        elif isinstance(preserve_edge_attrs, str):
+            if bound.arguments[preserve_edge_attrs] is True or callable(
+                bound.arguments[preserve_edge_attrs]
+            ):
+                # e.g. `preserve_edge_attrs="attr"` and `func(attr=True)`
+                # e.g. `preserve_edge_attrs="attr"` and `func(attr=myfunc)`
+                preserve_edge_attrs = True
+                edge_attrs = None
+            elif bound.arguments[preserve_edge_attrs] is False and (
+                isinstance(edge_attrs, str)
+                and edge_attrs == preserve_edge_attrs
+                or isinstance(edge_attrs, dict)
+                and preserve_edge_attrs in edge_attrs
+            ):
+                # e.g. `preserve_edge_attrs="attr"` and `func(attr=False)`
+                # Treat `False` argument as meaning "preserve_edge_data=False"
+                # and not `False` as the edge attribute to use.
+                preserve_edge_attrs = False
+                edge_attrs = None
+            else:
+                # e.g. `preserve_edge_attrs="attr"` and `func(attr="weight")`
+                preserve_edge_attrs = False
+        # Else: e.g. `preserve_edge_attrs={"G": {"weight": 1}}`
+
+        if edge_attrs is None:
+            # May have been set to None above b/c all attributes are preserved
+            pass
+        elif isinstance(edge_attrs, str):
+            if edge_attrs[0] == "[":
+                # e.g. `edge_attrs="[edge_attributes]"` (argument of list of attributes)
+                # e.g. `func(edge_attributes=["foo", "bar"])`
+                edge_attrs = dict.fromkeys(bound.arguments[edge_attrs[1:-1]], 1)
+            elif callable(bound.arguments[edge_attrs]):
+                # e.g. `edge_attrs="weight"` and `func(weight=myfunc)`
+                preserve_edge_attrs = True
+                edge_attrs = None
+            elif bound.arguments[edge_attrs] is not None:
+                # e.g. `edge_attrs="weight"` and `func(weight="foo")` (default of 1)
+                edge_attrs = {bound.arguments[edge_attrs]: 1}
+            elif self.name == "to_numpy_array" and hasattr(
+                bound.arguments["dtype"], "names"
+            ):
+                # Custom handling: attributes may be obtained from `dtype`
+                edge_attrs = dict.fromkeys(bound.arguments["dtype"].names, 1)
+            else:
+                # e.g. `edge_attrs="weight"` and `func(weight=None)`
+                edge_attrs = None
+        else:
+            # e.g. `edge_attrs={"attr": "default"}` and `func(attr="foo", default=7)`
+            # e.g. `edge_attrs={"attr": 0}` and `func(attr="foo")`
+            edge_attrs = {
+                edge_attr: bound.arguments.get(val, 1) if isinstance(val, str) else val
+                for key, val in edge_attrs.items()
+                if (edge_attr := bound.arguments[key]) is not None
+            }
+
+        if preserve_node_attrs is False:
+            # e.g. `preserve_node_attrs=False`
+            pass
+        elif preserve_node_attrs is True:
+            # e.g. `preserve_node_attrs=True`
+            node_attrs = None
+        elif isinstance(preserve_node_attrs, str):
+            if bound.arguments[preserve_node_attrs] is True or callable(
+                bound.arguments[preserve_node_attrs]
+            ):
+                # e.g. `preserve_node_attrs="attr"` and `func(attr=True)`
+                # e.g. `preserve_node_attrs="attr"` and `func(attr=myfunc)`
+                preserve_node_attrs = True
+                node_attrs = None
+            elif bound.arguments[preserve_node_attrs] is False and (
+                isinstance(node_attrs, str)
+                and node_attrs == preserve_node_attrs
+                or isinstance(node_attrs, dict)
+                and preserve_node_attrs in node_attrs
+            ):
+                # e.g. `preserve_node_attrs="attr"` and `func(attr=False)`
+                # Treat `False` argument as meaning "preserve_node_data=False"
+                # and not `False` as the node attribute to use. Is this used?
+                preserve_node_attrs = False
+                node_attrs = None
+            else:
+                # e.g. `preserve_node_attrs="attr"` and `func(attr="weight")`
+                preserve_node_attrs = False
+        # Else: e.g. `preserve_node_attrs={"G": {"pos": None}}`
+
+        if node_attrs is None:
+            # May have been set to None above b/c all attributes are preserved
+            pass
+        elif isinstance(node_attrs, str):
+            if node_attrs[0] == "[":
+                # e.g. `node_attrs="[node_attributes]"` (argument of list of attributes)
+                # e.g. `func(node_attributes=["foo", "bar"])`
+                node_attrs = dict.fromkeys(bound.arguments[node_attrs[1:-1]])
+            elif callable(bound.arguments[node_attrs]):
+                # e.g. `node_attrs="weight"` and `func(weight=myfunc)`
+                preserve_node_attrs = True
+                node_attrs = None
+            elif bound.arguments[node_attrs] is not None:
+                # e.g. `node_attrs="weight"` and `func(weight="foo")`
+                node_attrs = {bound.arguments[node_attrs]: None}
+            else:
+                # e.g. `node_attrs="weight"` and `func(weight=None)`
+                node_attrs = None
+        else:
+            # e.g. `node_attrs={"attr": "default"}` and `func(attr="foo", default=7)`
+            # e.g. `node_attrs={"attr": 0}` and `func(attr="foo")`
+            node_attrs = {
+                node_attr: bound.arguments.get(val) if isinstance(val, str) else val
+                for key, val in node_attrs.items()
+                if (node_attr := bound.arguments[key]) is not None
+            }
+
+        # It should be safe to assume that we either have networkx graphs or backend graphs.
+        # Future work: allow conversions between backends.
+        for gname in self.graphs:
+            if gname in self.list_graphs:
+                bound.arguments[gname] = [
+                    self._convert_graph(
+                        backend_name,
+                        g,
+                        edge_attrs=edge_attrs,
+                        node_attrs=node_attrs,
+                        preserve_edge_attrs=preserve_edge_attrs,
+                        preserve_node_attrs=preserve_node_attrs,
+                        preserve_graph_attrs=preserve_graph_attrs,
+                        graph_name=gname,
+                        use_cache=use_cache,
+                        mutations=mutations,
+                    )
+                    if getattr(g, "__networkx_backend__", "networkx") != backend_name
+                    else g
+                    for g in bound.arguments[gname]
+                ]
+            else:
+                graph = bound.arguments[gname]
+                if graph is None:
+                    if gname in self.optional_graphs:
+                        continue
+                    raise TypeError(
+                        f"Missing required graph argument `{gname}` in {self.name} function"
+                    )
+                if isinstance(preserve_edge_attrs, dict):
+                    preserve_edges = False
+                    edges = preserve_edge_attrs.get(gname, edge_attrs)
+                else:
+                    preserve_edges = preserve_edge_attrs
+                    edges = edge_attrs
+                if isinstance(preserve_node_attrs, dict):
+                    preserve_nodes = False
+                    nodes = preserve_node_attrs.get(gname, node_attrs)
+                else:
+                    preserve_nodes = preserve_node_attrs
+                    nodes = node_attrs
+                if isinstance(preserve_graph_attrs, set):
+                    preserve_graph = gname in preserve_graph_attrs
+                else:
+                    preserve_graph = preserve_graph_attrs
+                if getattr(graph, "__networkx_backend__", "networkx") != backend_name:
+                    bound.arguments[gname] = self._convert_graph(
+                        backend_name,
+                        graph,
+                        edge_attrs=edges,
+                        node_attrs=nodes,
+                        preserve_edge_attrs=preserve_edges,
+                        preserve_node_attrs=preserve_nodes,
+                        preserve_graph_attrs=preserve_graph,
+                        graph_name=gname,
+                        use_cache=use_cache,
+                        mutations=mutations,
+                    )
+        bound_kwargs = bound.kwargs
+        del bound_kwargs["backend"]
+        return bound.args, bound_kwargs
+
+    def _convert_graph(
+        self,
+        backend_name,
+        graph,
+        *,
+        edge_attrs,
+        node_attrs,
+        preserve_edge_attrs,
+        preserve_node_attrs,
+        preserve_graph_attrs,
+        graph_name,
+        use_cache,
+        mutations,
+    ):
+        nx_cache = getattr(graph, "__networkx_cache__", None) if use_cache else None
+        if nx_cache is not None:
+            cache = nx_cache.setdefault("backends", {}).setdefault(backend_name, {})
+            key = _get_cache_key(
+                edge_attrs=edge_attrs,
+                node_attrs=node_attrs,
+                preserve_edge_attrs=preserve_edge_attrs,
+                preserve_node_attrs=preserve_node_attrs,
+                preserve_graph_attrs=preserve_graph_attrs,
+            )
+            compat_key, rv = _get_from_cache(cache, key, mutations=mutations)
+            if rv is not None:
+                if "cache" not in nx.config.warnings_to_ignore:
+                    warnings.warn(
+                        "Note: conversions to backend graphs are saved to cache "
+                        "(`G.__networkx_cache__` on the original graph) by default."
+                        "\n\nThis warning means the cached graph is being used "
+                        f"for the {backend_name!r} backend in the "
+                        f"call to {self.name}.\n\nFor the cache to be consistent "
+                        "(i.e., correct), the input graph must not have been "
+                        "manually mutated since the cached graph was created. "
+                        "Examples of manually mutating the graph data structures "
+                        "resulting in an inconsistent cache include:\n\n"
+                        "    >>> G[u][v][key] = val\n\n"
+                        "and\n\n"
+                        "    >>> for u, v, d in G.edges(data=True):\n"
+                        "    ...     d[key] = val\n\n"
+                        "Using methods such as `G.add_edge(u, v, weight=val)` "
+                        "will correctly clear the cache to keep it consistent. "
+                        "You may also use `G.__networkx_cache__.clear()` to "
+                        "manually clear the cache, or set `G.__networkx_cache__` "
+                        "to None to disable caching for G. Enable or disable caching "
+                        "globally via `nx.config.cache_converted_graphs` config.\n\n"
+                        "To disable this warning:\n\n"
+                        '    >>> nx.config.warnings_to_ignore.add("cache")\n'
+                    )
+                if rv == FAILED_TO_CONVERT:
+                    # NotImplementedError is reasonable to use since the backend doesn't
+                    # implement this conversion. However, this will be different than
+                    # the original exception that the backend raised when it failed.
+                    # Using NotImplementedError allows the next backend to be attempted.
+                    raise NotImplementedError(
+                        "Graph conversion aborted: unable to convert graph to "
+                        f"'{backend_name}' backend in call to `{self.name}', "
+                        "because this conversion has previously failed."
+                    )
+                _logger.debug(
+                    "Using cached converted graph (from '%s' to '%s' backend) "
+                    "in call to '%s' for '%s' argument",
+                    getattr(graph, "__networkx_backend__", None),
+                    backend_name,
+                    self.name,
+                    graph_name,
+                )
+                return rv
+
+        if backend_name == "networkx":
+            # Perhaps we should check that "__networkx_backend__" attribute exists
+            # and return the original object if not.
+            if not hasattr(graph, "__networkx_backend__"):
+                _logger.debug(
+                    "Unable to convert input to 'networkx' backend in call to '%s' for "
+                    "'%s argument, because it is not from a backend (i.e., it does not "
+                    "have `G.__networkx_backend__` attribute). Using the original "
+                    "object: %s",
+                    self.name,
+                    graph_name,
+                    graph,
+                )
+                # This may fail, but let it fail in the networkx function
+                return graph
+            backend = _load_backend(graph.__networkx_backend__)
+            try:
+                rv = backend.convert_to_nx(graph)
+            except Exception:
+                if nx_cache is not None:
+                    _set_to_cache(cache, key, FAILED_TO_CONVERT)
+                raise
+        else:
+            backend = _load_backend(backend_name)
+            try:
+                rv = backend.convert_from_nx(
+                    graph,
+                    edge_attrs=edge_attrs,
+                    node_attrs=node_attrs,
+                    preserve_edge_attrs=preserve_edge_attrs,
+                    preserve_node_attrs=preserve_node_attrs,
+                    # Always preserve graph attrs when we are caching b/c this should be
+                    # cheap and may help prevent extra (unnecessary) conversions. Because
+                    # we do this, we don't need `preserve_graph_attrs` in the cache key.
+                    preserve_graph_attrs=preserve_graph_attrs or nx_cache is not None,
+                    name=self.name,
+                    graph_name=graph_name,
+                )
+            except Exception:
+                if nx_cache is not None:
+                    _set_to_cache(cache, key, FAILED_TO_CONVERT)
+                raise
+        if nx_cache is not None:
+            _set_to_cache(cache, key, rv)
+            _logger.debug(
+                "Caching converted graph (from '%s' to '%s' backend) "
+                "in call to '%s' for '%s' argument",
+                getattr(graph, "__networkx_backend__", None),
+                backend_name,
+                self.name,
+                graph_name,
+            )
+
+        return rv
+
+    def _call_with_backend(self, backend_name, args, kwargs, *, extra_message=None):
+        """Call this dispatchable function with a backend without converting inputs."""
+        if backend_name == "networkx":
+            return self.orig_func(*args, **kwargs)
+        backend = _load_backend(backend_name)
+        _logger.debug(
+            "Using backend '%s' for call to '%s' with arguments: %s",
+            backend_name,
+            self.name,
+            _LazyArgsRepr(self, args, kwargs),
+        )
+        try:
+            return getattr(backend, self.name)(*args, **kwargs)
+        except NotImplementedError as exc:
+            if extra_message is not None:
+                _logger.debug(
+                    "Backend '%s' raised when calling '%s': %s",
+                    backend_name,
+                    self.name,
+                    exc,
+                )
+                raise NotImplementedError(extra_message) from exc
+            raise
+
+    def _convert_and_call(
+        self,
+        backend_name,
+        input_backend_names,
+        args,
+        kwargs,
+        *,
+        extra_message=None,
+        mutations=None,
+    ):
+        """Call this dispatchable function with a backend after converting inputs.
+
+        Parameters
+        ----------
+        backend_name : str
+        input_backend_names : set[str]
+        args : arguments tuple
+        kwargs : keywords dict
+        extra_message : str, optional
+            Additional message to log if NotImplementedError is raised by backend.
+        mutations : list, optional
+            Used to clear objects gotten from cache if inputs will be mutated.
+        """
+        if backend_name == "networkx":
+            func = self.orig_func
+        else:
+            backend = _load_backend(backend_name)
+            func = getattr(backend, self.name)
+        other_backend_names = input_backend_names - {backend_name}
+        _logger.debug(
+            "Converting input graphs from %s backend%s to '%s' backend for call to '%s'",
+            other_backend_names
+            if len(other_backend_names) > 1
+            else f"'{next(iter(other_backend_names))}'",
+            "s" if len(other_backend_names) > 1 else "",
+            backend_name,
+            self.name,
+        )
+        try:
+            converted_args, converted_kwargs = self._convert_arguments(
+                backend_name,
+                args,
+                kwargs,
+                use_cache=nx.config.cache_converted_graphs,
+                mutations=mutations,
+            )
+        except NotImplementedError as exc:
+            # Only log the exception if we are adding an extra message
+            # because we don't want to lose any information.
+            _logger.debug(
+                "Failed to convert graphs from %s to '%s' backend for call to '%s'"
+                + ("" if extra_message is None else ": %s"),
+                input_backend_names,
+                backend_name,
+                self.name,
+                *(() if extra_message is None else (exc,)),
+            )
+            if extra_message is not None:
+                raise NotImplementedError(extra_message) from exc
+            raise
+        if backend_name != "networkx":
+            _logger.debug(
+                "Using backend '%s' for call to '%s' with arguments: %s",
+                backend_name,
+                self.name,
+                _LazyArgsRepr(self, converted_args, converted_kwargs),
+            )
+        try:
+            return func(*converted_args, **converted_kwargs)
+        except NotImplementedError as exc:
+            if extra_message is not None:
+                _logger.debug(
+                    "Backend '%s' raised when calling '%s': %s",
+                    backend_name,
+                    self.name,
+                    exc,
+                )
+                raise NotImplementedError(extra_message) from exc
+            raise
+
+    def _convert_and_call_for_tests(
+        self, backend_name, args, kwargs, *, fallback_to_nx=False
+    ):
+        """Call this dispatchable function with a backend; for use with testing."""
+        backend = _load_backend(backend_name)
+        if not self._can_backend_run(backend_name, args, kwargs):
+            if fallback_to_nx or not self.graphs:
+                if fallback_to_nx:
+                    _logger.debug(
+                        "Falling back to use 'networkx' instead of '%s' backend "
+                        "for call to '%s' with arguments: %s",
+                        backend_name,
+                        self.name,
+                        _LazyArgsRepr(self, args, kwargs),
+                    )
+                return self.orig_func(*args, **kwargs)
+
+            import pytest
+
+            msg = f"'{self.name}' not implemented by {backend_name}"
+            if hasattr(backend, self.name):
+                msg += " with the given arguments"
+            pytest.xfail(msg)
+
+        from collections.abc import Iterable, Iterator, Mapping
+        from copy import copy, deepcopy
+        from io import BufferedReader, BytesIO, StringIO, TextIOWrapper
+        from itertools import tee
+        from random import Random
+
+        import numpy as np
+        from numpy.random import Generator, RandomState
+        from scipy.sparse import sparray
+
+        # We sometimes compare the backend result (or input graphs) to the
+        # original result (or input graphs), so we need two sets of arguments.
+        compare_result_to_nx = (
+            self._returns_graph
+            and "networkx" in self.backends
+            and self.name
+            not in {
+                # Has graphs as node values (unable to compare)
+                "quotient_graph",
+                # We don't handle tempfile.NamedTemporaryFile arguments
+                "read_gml",
+                "read_graph6",
+                "read_sparse6",
+                # We don't handle io.BufferedReader or io.TextIOWrapper arguments
+                "bipartite_read_edgelist",
+                "read_adjlist",
+                "read_edgelist",
+                "read_graphml",
+                "read_multiline_adjlist",
+                "read_pajek",
+                "from_pydot",
+                "pydot_read_dot",
+                "agraph_read_dot",
+                # graph comparison fails b/c of nan values
+                "read_gexf",
+            }
+        )
+        compare_inputs_to_nx = (
+            "networkx" in self.backends and self._will_call_mutate_input(args, kwargs)
+        )
+
+        # Tee iterators and copy random state so that they may be used twice.
+        if not args or not compare_result_to_nx and not compare_inputs_to_nx:
+            args_to_convert = args_nx = args
+        else:
+            args_to_convert, args_nx = zip(
+                *(
+                    (arg, deepcopy(arg))
+                    if isinstance(arg, RandomState)
+                    else (arg, copy(arg))
+                    if isinstance(arg, BytesIO | StringIO | Random | Generator)
+                    else tee(arg)
+                    if isinstance(arg, Iterator)
+                    and not isinstance(arg, BufferedReader | TextIOWrapper)
+                    else (arg, arg)
+                    for arg in args
+                )
+            )
+        if not kwargs or not compare_result_to_nx and not compare_inputs_to_nx:
+            kwargs_to_convert = kwargs_nx = kwargs
+        else:
+            kwargs_to_convert, kwargs_nx = zip(
+                *(
+                    ((k, v), (k, deepcopy(v)))
+                    if isinstance(v, RandomState)
+                    else ((k, v), (k, copy(v)))
+                    if isinstance(v, BytesIO | StringIO | Random | Generator)
+                    else ((k, (teed := tee(v))[0]), (k, teed[1]))
+                    if isinstance(v, Iterator)
+                    and not isinstance(v, BufferedReader | TextIOWrapper)
+                    else ((k, v), (k, v))
+                    for k, v in kwargs.items()
+                )
+            )
+            kwargs_to_convert = dict(kwargs_to_convert)
+            kwargs_nx = dict(kwargs_nx)
+
+        try:
+            converted_args, converted_kwargs = self._convert_arguments(
+                backend_name,
+                args_to_convert,
+                kwargs_to_convert,
+                use_cache=False,
+                mutations=None,
+            )
+        except NotImplementedError as exc:
+            if fallback_to_nx:
+                _logger.debug(
+                    "Graph conversion failed; falling back to use 'networkx' instead "
+                    "of '%s' backend for call to '%s'",
+                    backend_name,
+                    self.name,
+                )
+                return self.orig_func(*args_nx, **kwargs_nx)
+            import pytest
+
+            pytest.xfail(
+                exc.args[0] if exc.args else f"{self.name} raised {type(exc).__name__}"
+            )
+
+        if compare_inputs_to_nx:
+            # Ensure input graphs are different if the function mutates an input graph.
+            bound_backend = self.__signature__.bind(*converted_args, **converted_kwargs)
+            bound_backend.apply_defaults()
+            bound_nx = self.__signature__.bind(*args_nx, **kwargs_nx)
+            bound_nx.apply_defaults()
+            for gname in self.graphs:
+                graph_nx = bound_nx.arguments[gname]
+                if bound_backend.arguments[gname] is graph_nx is not None:
+                    bound_nx.arguments[gname] = graph_nx.copy()
+            args_nx = bound_nx.args
+            kwargs_nx = bound_nx.kwargs
+            kwargs_nx.pop("backend", None)
+
+        _logger.debug(
+            "Using backend '%s' for call to '%s' with arguments: %s",
+            backend_name,
+            self.name,
+            _LazyArgsRepr(self, converted_args, converted_kwargs),
+        )
+        try:
+            result = getattr(backend, self.name)(*converted_args, **converted_kwargs)
+        except NotImplementedError as exc:
+            if fallback_to_nx:
+                _logger.debug(
+                    "Backend '%s' raised when calling '%s': %s; "
+                    "falling back to use 'networkx' instead.",
+                    backend_name,
+                    self.name,
+                    exc,
+                )
+                return self.orig_func(*args_nx, **kwargs_nx)
+            import pytest
+
+            pytest.xfail(
+                exc.args[0] if exc.args else f"{self.name} raised {type(exc).__name__}"
+            )
+
+        # Verify that `self._returns_graph` is correct. This compares the return type
+        # to the type expected from `self._returns_graph`. This handles tuple and list
+        # return types, but *does not* catch functions that yield graphs.
+        if (
+            self._returns_graph
+            != (
+                isinstance(result, nx.Graph)
+                or hasattr(result, "__networkx_backend__")
+                or isinstance(result, tuple | list)
+                and any(
+                    isinstance(x, nx.Graph) or hasattr(x, "__networkx_backend__")
+                    for x in result
+                )
+            )
+            and not (
+                # May return Graph or None
+                self.name in {"check_planarity", "check_planarity_recursive"}
+                and any(x is None for x in result)
+            )
+            and not (
+                # May return Graph or dict
+                self.name in {"held_karp_ascent"}
+                and any(isinstance(x, dict) for x in result)
+            )
+            and self.name
+            not in {
+                # yields graphs
+                "all_triads",
+                "general_k_edge_subgraphs",
+                # yields graphs or arrays
+                "nonisomorphic_trees",
+            }
+        ):
+            raise RuntimeError(f"`returns_graph` is incorrect for {self.name}")
+
+        def check_result(val, depth=0):
+            if isinstance(val, np.number):
+                raise RuntimeError(
+                    f"{self.name} returned a numpy scalar {val} ({type(val)}, depth={depth})"
+                )
+            if isinstance(val, np.ndarray | sparray):
+                return
+            if isinstance(val, nx.Graph):
+                check_result(val._node, depth=depth + 1)
+                check_result(val._adj, depth=depth + 1)
+                return
+            if isinstance(val, Iterator):
+                raise NotImplementedError
+            if isinstance(val, Iterable) and not isinstance(val, str):
+                for x in val:
+                    check_result(x, depth=depth + 1)
+            if isinstance(val, Mapping):
+                for x in val.values():
+                    check_result(x, depth=depth + 1)
+
+        def check_iterator(it):
+            for val in it:
+                try:
+                    check_result(val)
+                except RuntimeError as exc:
+                    raise RuntimeError(
+                        f"{self.name} returned a numpy scalar {val} ({type(val)})"
+                    ) from exc
+                yield val
+
+        if self.name in {"from_edgelist"}:
+            # numpy scalars are explicitly given as values in some tests
+            pass
+        elif isinstance(result, Iterator):
+            result = check_iterator(result)
+        else:
+            try:
+                check_result(result)
+            except RuntimeError as exc:
+                raise RuntimeError(
+                    f"{self.name} returned a numpy scalar {result} ({type(result)})"
+                ) from exc
+            check_result(result)
+
+        def assert_graphs_equal(G1, G2, strict=True):
+            assert G1.number_of_nodes() == G2.number_of_nodes()
+            assert G1.number_of_edges() == G2.number_of_edges()
+            assert G1.is_directed() is G2.is_directed()
+            assert G1.is_multigraph() is G2.is_multigraph()
+            if strict:
+                assert G1.graph == G2.graph
+                assert G1._node == G2._node
+                assert G1._adj == G2._adj
+            else:
+                assert set(G1) == set(G2)
+                assert set(G1.edges) == set(G2.edges)
+
+        if compare_inputs_to_nx:
+            # Special-case algorithms that mutate input graphs
+            result_nx = self.orig_func(*args_nx, **kwargs_nx)
+            for gname in self.graphs:
+                G0 = bound_backend.arguments[gname]
+                G1 = bound_nx.arguments[gname]
+                if G0 is not None or G1 is not None:
+                    G1 = backend.convert_to_nx(G1)
+                    assert_graphs_equal(G0, G1, strict=False)
+
+        converted_result = backend.convert_to_nx(result)
+        if compare_result_to_nx and isinstance(converted_result, nx.Graph):
+            # For graph return types (e.g. generators), we compare that results are
+            # the same between the backend and networkx, then return the original
+            # networkx result so the iteration order will be consistent in tests.
+            if compare_inputs_to_nx:
+                G = result_nx
+            else:
+                G = self.orig_func(*args_nx, **kwargs_nx)
+            assert_graphs_equal(G, converted_result)
+            return G
+
+        return converted_result
+
+    def _make_doc(self):
+        """Generate the backends section at the end for functions having an alternate
+        backend implementation(s) using the `backend_info` entry-point."""
+
+        if self.backends == {"networkx"}:
+            return self._orig_doc
+        # Add "Backends" section to the bottom of the docstring (if there are backends)
+        lines = [
+            "Backends",
+            "--------",
+        ]
+        for backend in sorted(self.backends - {"networkx"}):
+            info = backend_info[backend]
+            if "short_summary" in info:
+                lines.append(f"{backend} : {info['short_summary']}")
+            else:
+                lines.append(backend)
+            if "functions" not in info or self.name not in info["functions"]:
+                lines.append("")
+                continue
+
+            func_info = info["functions"][self.name]
+
+            # Renaming extra_docstring to additional_docs
+            if func_docs := (
+                func_info.get("additional_docs") or func_info.get("extra_docstring")
+            ):
+                lines.extend(
+                    f"  {line}" if line else line for line in func_docs.split("\n")
+                )
+                add_gap = True
+            else:
+                add_gap = False
+
+            # Renaming extra_parameters to additional_parameters
+            if extra_parameters := (
+                func_info.get("extra_parameters")
+                or func_info.get("additional_parameters")
+            ):
+                if add_gap:
+                    lines.append("")
+                lines.append("  Additional parameters:")
+                for param in sorted(extra_parameters):
+                    lines.append(f"    {param}")
+                    if desc := extra_parameters[param]:
+                        lines.append(f"      {desc}")
+                    lines.append("")
+            else:
+                lines.append("")
+
+            if func_url := func_info.get("url"):
+                lines.append(f"[`Source <{func_url}>`_]")
+                lines.append("")
+
+        # We assume the docstrings are indented by four spaces (true for now)
+        new_doc = self._orig_doc or ""
+        if not new_doc.rstrip():
+            new_doc = f"The original docstring for {self.name} was empty."
+        if self.backends:
+            lines.pop()  # Remove last empty line
+            to_add = "\n    ".join(lines)
+            new_doc = f"{new_doc.rstrip()}\n\n    {to_add}"
+
+        # For backend-only funcs, add "Attention" admonishment after the one line summary
+        if "networkx" not in self.backends:
+            lines = new_doc.split("\n")
+            index = 0
+            while not lines[index].strip():
+                index += 1
+            while index < len(lines) and lines[index].strip():
+                index += 1
+            backends = sorted(self.backends)
+            if len(backends) == 0:
+                example = ""
+            elif len(backends) == 1:
+                example = f' such as "{backends[0]}"'
+            elif len(backends) == 2:
+                example = f' such as "{backends[0]} or "{backends[1]}"'
+            else:
+                example = (
+                    " such as "
+                    + ", ".join(f'"{x}"' for x in backends[:-1])
+                    + f', or "{backends[-1]}"'  # Oxford comma
+                )
+            to_add = (
+                "\n    .. attention:: This function does not have a default NetworkX implementation.\n"
+                "        It may only be run with an installable :doc:`backend </backends>` that\n"
+                f"        supports it{example}.\n\n"
+                "        Hint: use ``backend=...`` keyword argument to specify a backend or add\n"
+                "        backends to ``nx.config.backend_priority``."
+            )
+            lines.insert(index, to_add)
+            new_doc = "\n".join(lines)
+        return new_doc
+
+    def __reduce__(self):
+        """Allow this object to be serialized with pickle.
+
+        This uses the global registry `_registered_algorithms` to deserialize.
+        """
+        return _restore_dispatchable, (self.name,)
+
+
+def _restore_dispatchable(name):
+    return _registered_algorithms[name].__wrapped__
+
+
+def _get_cache_key(
+    *,
+    edge_attrs,
+    node_attrs,
+    preserve_edge_attrs,
+    preserve_node_attrs,
+    preserve_graph_attrs,
+):
+    """Return key used by networkx caching given arguments for ``convert_from_nx``."""
+    # edge_attrs: dict | None
+    # node_attrs: dict | None
+    # preserve_edge_attrs: bool (False if edge_attrs is not None)
+    # preserve_node_attrs: bool (False if node_attrs is not None)
+    return (
+        frozenset(edge_attrs.items())
+        if edge_attrs is not None
+        else preserve_edge_attrs,
+        frozenset(node_attrs.items())
+        if node_attrs is not None
+        else preserve_node_attrs,
+    )
+
+
+def _get_from_cache(cache, key, *, backend_name=None, mutations=None):
+    """Search the networkx cache for a graph that is compatible with ``key``.
+
+    Parameters
+    ----------
+    cache : dict
+        If ``backend_name`` is given, then this is treated as ``G.__networkx_cache__``,
+        but if ``backend_name`` is None, then this is treated as the resolved inner
+        cache such as ``G.__networkx_cache__["backends"][backend_name]``.
+    key : tuple
+        Cache key from ``_get_cache_key``.
+    backend_name : str, optional
+        Name of the backend to control how ``cache`` is interpreted.
+    mutations : list, optional
+        Used internally to clear objects gotten from cache if inputs will be mutated.
+
+    Returns
+    -------
+    tuple or None
+        The key of the compatible graph found in the cache.
+    graph or "FAILED_TO_CONVERT" or None
+        A compatible graph if possible. "FAILED_TO_CONVERT" indicates that a previous
+        conversion attempt failed for this cache key.
+    """
+    if backend_name is not None:
+        cache = cache.get("backends", {}).get(backend_name, {})
+    if not cache:
+        return None, None
+
+    # Do a simple search for a cached graph with compatible data.
+    # For example, if we need a single attribute, then it's okay
+    # to use a cached graph that preserved all attributes.
+    # This looks for an exact match first.
+    edge_key, node_key = key
+    for compat_key in itertools.product(
+        (edge_key, True) if edge_key is not True else (True,),
+        (node_key, True) if node_key is not True else (True,),
+    ):
+        if (rv := cache.get(compat_key)) is not None and (
+            rv != FAILED_TO_CONVERT or key == compat_key
+        ):
+            if mutations is not None:
+                # Remove this item from the cache (after all conversions) if
+                # the call to this dispatchable function will mutate an input.
+                mutations.append((cache, compat_key))
+            return compat_key, rv
+
+    # Iterate over the items in `cache` to see if any are compatible.
+    # For example, if no edge attributes are needed, then a graph
+    # with any edge attribute will suffice. We use the same logic
+    # below (but switched) to clear unnecessary items from the cache.
+    # Use `list(cache.items())` to be thread-safe.
+    for (ekey, nkey), graph in list(cache.items()):
+        if graph == FAILED_TO_CONVERT:
+            # Return FAILED_TO_CONVERT if any cache key that requires a subset
+            # of the edge/node attributes of the given cache key has previously
+            # failed to convert. This logic is similar to `_set_to_cache`.
+            if ekey is False or edge_key is True:
+                pass
+            elif ekey is True or edge_key is False or not ekey.issubset(edge_key):
+                continue
+            if nkey is False or node_key is True:  # or nkey == node_key:
+                pass
+            elif nkey is True or node_key is False or not nkey.issubset(node_key):
+                continue
+            # Save to cache for faster subsequent lookups
+            cache[key] = FAILED_TO_CONVERT
+        elif edge_key is False or ekey is True:
+            pass  # Cache works for edge data!
+        elif edge_key is True or ekey is False or not edge_key.issubset(ekey):
+            continue  # Cache missing required edge data; does not work
+        if node_key is False or nkey is True:
+            pass  # Cache works for node data!
+        elif node_key is True or nkey is False or not node_key.issubset(nkey):
+            continue  # Cache missing required node data; does not work
+        if mutations is not None:
+            # Remove this item from the cache (after all conversions) if
+            # the call to this dispatchable function will mutate an input.
+            mutations.append((cache, (ekey, nkey)))
+        return (ekey, nkey), graph
+
+    return None, None
+
+
+def _set_to_cache(cache, key, graph, *, backend_name=None):
+    """Set a backend graph to the cache, and remove unnecessary cached items.
+
+    Parameters
+    ----------
+    cache : dict
+        If ``backend_name`` is given, then this is treated as ``G.__networkx_cache__``,
+        but if ``backend_name`` is None, then this is treated as the resolved inner
+        cache such as ``G.__networkx_cache__["backends"][backend_name]``.
+    key : tuple
+        Cache key from ``_get_cache_key``.
+    graph : graph or "FAILED_TO_CONVERT"
+        Setting value to "FAILED_TO_CONVERT" prevents this conversion from being
+        attempted in future calls.
+    backend_name : str, optional
+        Name of the backend to control how ``cache`` is interpreted.
+
+    Returns
+    -------
+    dict
+        The items that were removed from the cache.
+    """
+    if backend_name is not None:
+        cache = cache.setdefault("backends", {}).setdefault(backend_name, {})
+    # Remove old cached items that are no longer necessary since they
+    # are dominated/subsumed/outdated by what was just calculated.
+    # This uses the same logic as above, but with keys switched.
+    # Also, don't update the cache here if the call will mutate an input.
+    removed = {}
+    edge_key, node_key = key
+    cache[key] = graph  # Set at beginning to be thread-safe
+    if graph == FAILED_TO_CONVERT:
+        return removed
+    for cur_key in list(cache):
+        if cur_key == key:
+            continue
+        ekey, nkey = cur_key
+        if ekey is False or edge_key is True:
+            pass
+        elif ekey is True or edge_key is False or not ekey.issubset(edge_key):
+            continue
+        if nkey is False or node_key is True:
+            pass
+        elif nkey is True or node_key is False or not nkey.issubset(node_key):
+            continue
+        # Use pop instead of del to try to be thread-safe
+        if (graph := cache.pop(cur_key, None)) is not None:
+            removed[cur_key] = graph
+    return removed
+
+
+class _LazyArgsRepr:
+    """Simple wrapper to display arguments of dispatchable functions in logging calls."""
+
+    def __init__(self, func, args, kwargs):
+        self.func = func
+        self.args = args
+        self.kwargs = kwargs
+        self.value = None
+
+    def __repr__(self):
+        if self.value is None:
+            bound = self.func.__signature__.bind_partial(*self.args, **self.kwargs)
+            inner = ", ".join(f"{key}={val!r}" for key, val in bound.arguments.items())
+            self.value = f"({inner})"
+        return self.value
+
+
+if os.environ.get("_NETWORKX_BUILDING_DOCS_"):
+    # When building docs with Sphinx, use the original function with the
+    # dispatched __doc__, b/c Sphinx renders normal Python functions better.
+    # This doesn't show e.g. `*, backend=None, **backend_kwargs` in the
+    # signatures, which is probably okay. It does allow the docstring to be
+    # updated based on the installed backends.
+    _orig_dispatchable = _dispatchable
+
+    def _dispatchable(func=None, **kwargs):  # type: ignore[no-redef]
+        if func is None:
+            return partial(_dispatchable, **kwargs)
+        dispatched_func = _orig_dispatchable(func, **kwargs)
+        func.__doc__ = dispatched_func.__doc__
+        return func
+
+    _dispatchable.__doc__ = _orig_dispatchable.__new__.__doc__  # type: ignore[method-assign,assignment]
+    _sig = inspect.signature(_orig_dispatchable.__new__)
+    _dispatchable.__signature__ = _sig.replace(  # type: ignore[method-assign,assignment]
+        parameters=[v for k, v in _sig.parameters.items() if k != "cls"]
+    )
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/configs.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/configs.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbd9792b863bd7e1ecddee791837e727da54d37e
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/configs.py
@@ -0,0 +1,391 @@
+import collections
+import typing
+from dataclasses import dataclass
+
+__all__ = ["Config"]
+
+
+@dataclass(init=False, eq=False, slots=True, kw_only=True, match_args=False)
+class Config:
+    """The base class for NetworkX configuration.
+
+    There are two ways to use this to create configurations. The recommended way
+    is to subclass ``Config`` with docs and annotations.
+
+    >>> class MyConfig(Config):
+    ...     '''Breakfast!'''
+    ...
+    ...     eggs: int
+    ...     spam: int
+    ...
+    ...     def _on_setattr(self, key, value):
+    ...         assert isinstance(value, int) and value >= 0
+    ...         return value
+    >>> cfg = MyConfig(eggs=1, spam=5)
+
+    Another way is to simply pass the initial configuration as keyword arguments to
+    the ``Config`` instance:
+
+    >>> cfg1 = Config(eggs=1, spam=5)
+    >>> cfg1
+    Config(eggs=1, spam=5)
+
+    Once defined, config items may be modified, but can't be added or deleted by default.
+    ``Config`` is a ``Mapping``, and can get and set configs via attributes or brackets:
+
+    >>> cfg.eggs = 2
+    >>> cfg.eggs
+    2
+    >>> cfg["spam"] = 42
+    >>> cfg["spam"]
+    42
+
+    For convenience, it can also set configs within a context with the "with" statement:
+
+    >>> with cfg(spam=3):
+    ...     print("spam (in context):", cfg.spam)
+    spam (in context): 3
+    >>> print("spam (after context):", cfg.spam)
+    spam (after context): 42
+
+    Subclasses may also define ``_on_setattr`` (as done in the example above)
+    to ensure the value being assigned is valid:
+
+    >>> cfg.spam = -1
+    Traceback (most recent call last):
+        ...
+    AssertionError
+
+    If a more flexible configuration object is needed that allows adding and deleting
+    configurations, then pass ``strict=False`` when defining the subclass:
+
+    >>> class FlexibleConfig(Config, strict=False):
+    ...     default_greeting: str = "Hello"
+    >>> flexcfg = FlexibleConfig()
+    >>> flexcfg.name = "Mr. Anderson"
+    >>> flexcfg
+    FlexibleConfig(default_greeting='Hello', name='Mr. Anderson')
+    """
+
+    def __init_subclass__(cls, strict=True):
+        cls._strict = strict
+
+    def __new__(cls, **kwargs):
+        orig_class = cls
+        if cls is Config:
+            # Enable the "simple" case of accepting config definition as keywords
+            cls = type(
+                cls.__name__,
+                (cls,),
+                {"__annotations__": dict.fromkeys(kwargs, typing.Any)},
+            )
+        cls = dataclass(
+            eq=False,
+            repr=cls._strict,
+            slots=cls._strict,
+            kw_only=True,
+            match_args=False,
+        )(cls)
+        if not cls._strict:
+            cls.__repr__ = _flexible_repr
+        cls._orig_class = orig_class  # Save original class so we can pickle
+        cls._prev = None  # Stage previous configs to enable use as context manager
+        cls._context_stack = []  # Stack of previous configs when used as context
+        instance = object.__new__(cls)
+        instance.__init__(**kwargs)
+        return instance
+
+    def _on_setattr(self, key, value):
+        """Process config value and check whether it is valid. Useful for subclasses."""
+        return value
+
+    def _on_delattr(self, key):
+        """Callback for when a config item is being deleted. Useful for subclasses."""
+
+    # Control behavior of attributes
+    def __dir__(self):
+        return self.__dataclass_fields__.keys()
+
+    def __setattr__(self, key, value):
+        if self._strict and key not in self.__dataclass_fields__:
+            raise AttributeError(f"Invalid config name: {key!r}")
+        value = self._on_setattr(key, value)
+        object.__setattr__(self, key, value)
+        self.__class__._prev = None
+
+    def __delattr__(self, key):
+        if self._strict:
+            raise TypeError(
+                f"Configuration items can't be deleted (can't delete {key!r})."
+            )
+        self._on_delattr(key)
+        object.__delattr__(self, key)
+        self.__class__._prev = None
+
+    # Be a `collection.abc.Collection`
+    def __contains__(self, key):
+        return (
+            key in self.__dataclass_fields__ if self._strict else key in self.__dict__
+        )
+
+    def __iter__(self):
+        return iter(self.__dataclass_fields__ if self._strict else self.__dict__)
+
+    def __len__(self):
+        return len(self.__dataclass_fields__ if self._strict else self.__dict__)
+
+    def __reversed__(self):
+        return reversed(self.__dataclass_fields__ if self._strict else self.__dict__)
+
+    # Add dunder methods for `collections.abc.Mapping`
+    def __getitem__(self, key):
+        try:
+            return getattr(self, key)
+        except AttributeError as err:
+            raise KeyError(*err.args) from None
+
+    def __setitem__(self, key, value):
+        try:
+            self.__setattr__(key, value)
+        except AttributeError as err:
+            raise KeyError(*err.args) from None
+
+    def __delitem__(self, key):
+        try:
+            self.__delattr__(key)
+        except AttributeError as err:
+            raise KeyError(*err.args) from None
+
+    _ipython_key_completions_ = __dir__  # config["<TAB>
+
+    # Go ahead and make it a `collections.abc.Mapping`
+    def get(self, key, default=None):
+        return getattr(self, key, default)
+
+    def items(self):
+        return collections.abc.ItemsView(self)
+
+    def keys(self):
+        return collections.abc.KeysView(self)
+
+    def values(self):
+        return collections.abc.ValuesView(self)
+
+    # dataclass can define __eq__ for us, but do it here so it works after pickling
+    def __eq__(self, other):
+        if not isinstance(other, Config):
+            return NotImplemented
+        return self._orig_class == other._orig_class and self.items() == other.items()
+
+    # Make pickle work
+    def __reduce__(self):
+        return self._deserialize, (self._orig_class, dict(self))
+
+    @staticmethod
+    def _deserialize(cls, kwargs):
+        return cls(**kwargs)
+
+    # Allow to be used as context manager
+    def __call__(self, **kwargs):
+        kwargs = {key: self._on_setattr(key, val) for key, val in kwargs.items()}
+        prev = dict(self)
+        for key, val in kwargs.items():
+            setattr(self, key, val)
+        self.__class__._prev = prev
+        return self
+
+    def __enter__(self):
+        if self.__class__._prev is None:
+            raise RuntimeError(
+                "Config being used as a context manager without config items being set. "
+                "Set config items via keyword arguments when calling the config object. "
+                "For example, using config as a context manager should be like:\n\n"
+                '    >>> with cfg(breakfast="spam"):\n'
+                "    ...     ...  # Do stuff\n"
+            )
+        self.__class__._context_stack.append(self.__class__._prev)
+        self.__class__._prev = None
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        prev = self.__class__._context_stack.pop()
+        for key, val in prev.items():
+            setattr(self, key, val)
+
+
+def _flexible_repr(self):
+    return (
+        f"{self.__class__.__qualname__}("
+        + ", ".join(f"{key}={val!r}" for key, val in self.__dict__.items())
+        + ")"
+    )
+
+
+# Register, b/c `Mapping.__subclasshook__` returns `NotImplemented`
+collections.abc.Mapping.register(Config)
+
+
+class BackendPriorities(Config, strict=False):
+    """Configuration to control automatic conversion to and calling of backends.
+
+    Priority is given to backends listed earlier.
+
+    Parameters
+    ----------
+    algos : list of backend names
+        This controls "algorithms" such as ``nx.pagerank`` that don't return a graph.
+    generators : list of backend names
+        This controls "generators" such as ``nx.from_pandas_edgelist`` that return a graph.
+    kwargs : variadic keyword arguments of function name to list of backend names
+        This allows each function to be configured separately and will override the config
+        in ``algos`` or ``generators`` if present. The dispatchable function name may be
+        gotten from the ``.name`` attribute such as ``nx.pagerank.name`` (it's typically
+        the same as the name of the function).
+    """
+
+    algos: list[str]
+    generators: list[str]
+
+    def _on_setattr(self, key, value):
+        from .backends import _registered_algorithms, backend_info
+
+        if key in {"algos", "generators"}:
+            pass
+        elif key not in _registered_algorithms:
+            raise AttributeError(
+                f"Invalid config name: {key!r}. Expected 'algos', 'generators', or a name "
+                "of a dispatchable function (e.g. `.name` attribute of the function)."
+            )
+        if not (isinstance(value, list) and all(isinstance(x, str) for x in value)):
+            raise TypeError(
+                f"{key!r} config must be a list of backend names; got {value!r}"
+            )
+        if missing := {x for x in value if x not in backend_info}:
+            missing = ", ".join(map(repr, sorted(missing)))
+            raise ValueError(f"Unknown backend when setting {key!r}: {missing}")
+        return value
+
+    def _on_delattr(self, key):
+        if key in {"algos", "generators"}:
+            raise TypeError(f"{key!r} configuration item can't be deleted.")
+
+
+class NetworkXConfig(Config):
+    """Configuration for NetworkX that controls behaviors such as how to use backends.
+
+    Attribute and bracket notation are supported for getting and setting configurations::
+
+        >>> nx.config.backend_priority == nx.config["backend_priority"]
+        True
+
+    Parameters
+    ----------
+    backend_priority : list of backend names or dict or BackendPriorities
+        Enable automatic conversion of graphs to backend graphs for functions
+        implemented by the backend. Priority is given to backends listed earlier.
+        This is a nested configuration with keys ``algos``, ``generators``, and,
+        optionally, function names. Setting this value to a list of backend names
+        will set ``nx.config.backend_priority.algos``. For more information, see
+        ``help(nx.config.backend_priority)``. Default is empty list.
+
+    backends : Config mapping of backend names to backend Config
+        The keys of the Config mapping are names of all installed NetworkX backends,
+        and the values are their configurations as Config mappings.
+
+    cache_converted_graphs : bool
+        If True, then save converted graphs to the cache of the input graph. Graph
+        conversion may occur when automatically using a backend from `backend_priority`
+        or when using the `backend=` keyword argument to a function call. Caching can
+        improve performance by avoiding repeated conversions, but it uses more memory.
+        Care should be taken to not manually mutate a graph that has cached graphs; for
+        example, ``G[u][v][k] = val`` changes the graph, but does not clear the cache.
+        Using methods such as ``G.add_edge(u, v, weight=val)`` will clear the cache to
+        keep it consistent. ``G.__networkx_cache__.clear()`` manually clears the cache.
+        Default is True.
+
+    fallback_to_nx : bool
+        If True, then "fall back" and run with the default "networkx" implementation
+        for dispatchable functions not implemented by backends of input graphs. When a
+        backend graph is passed to a dispatchable function, the default behavior is to
+        use the implementation from that backend if possible and raise if not. Enabling
+        ``fallback_to_nx`` makes the networkx implementation the fallback to use instead
+        of raising, and will convert the backend graph to a networkx-compatible graph.
+        Default is False.
+
+    warnings_to_ignore : set of strings
+        Control which warnings from NetworkX are not emitted. Valid elements:
+
+        - `"cache"`: when a cached value is used from ``G.__networkx_cache__``.
+
+    Notes
+    -----
+    Environment variables may be used to control some default configurations:
+
+    - ``NETWORKX_BACKEND_PRIORITY``: set ``backend_priority.algos`` from comma-separated names.
+    - ``NETWORKX_CACHE_CONVERTED_GRAPHS``: set ``cache_converted_graphs`` to True if nonempty.
+    - ``NETWORKX_FALLBACK_TO_NX``: set ``fallback_to_nx`` to True if nonempty.
+    - ``NETWORKX_WARNINGS_TO_IGNORE``: set `warnings_to_ignore` from comma-separated names.
+
+    and can be used for finer control of ``backend_priority`` such as:
+
+    - ``NETWORKX_BACKEND_PRIORITY_ALGOS``: same as ``NETWORKX_BACKEND_PRIORITY``
+      to set ``backend_priority.algos``.
+
+    This is a global configuration. Use with caution when using from multiple threads.
+    """
+
+    backend_priority: BackendPriorities
+    backends: Config
+    cache_converted_graphs: bool
+    fallback_to_nx: bool
+    warnings_to_ignore: set[str]
+
+    def _on_setattr(self, key, value):
+        from .backends import backend_info
+
+        if key == "backend_priority":
+            if isinstance(value, list):
+                # `config.backend_priority = [backend]` sets `backend_priority.algos`
+                value = BackendPriorities(
+                    **dict(
+                        self.backend_priority,
+                        algos=self.backend_priority._on_setattr("algos", value),
+                    )
+                )
+            elif isinstance(value, dict):
+                kwargs = value
+                value = BackendPriorities(algos=[], generators=[])
+                for key, val in kwargs.items():
+                    setattr(value, key, val)
+            elif not isinstance(value, BackendPriorities):
+                raise TypeError(
+                    f"{key!r} config must be a dict of lists of backend names; got {value!r}"
+                )
+        elif key == "backends":
+            if not (
+                isinstance(value, Config)
+                and all(isinstance(key, str) for key in value)
+                and all(isinstance(val, Config) for val in value.values())
+            ):
+                raise TypeError(
+                    f"{key!r} config must be a Config of backend configs; got {value!r}"
+                )
+            if missing := {x for x in value if x not in backend_info}:
+                missing = ", ".join(map(repr, sorted(missing)))
+                raise ValueError(f"Unknown backend when setting {key!r}: {missing}")
+        elif key in {"cache_converted_graphs", "fallback_to_nx"}:
+            if not isinstance(value, bool):
+                raise TypeError(f"{key!r} config must be True or False; got {value!r}")
+        elif key == "warnings_to_ignore":
+            if not (isinstance(value, set) and all(isinstance(x, str) for x in value)):
+                raise TypeError(
+                    f"{key!r} config must be a set of warning names; got {value!r}"
+                )
+            known_warnings = {"cache"}
+            if missing := {x for x in value if x not in known_warnings}:
+                missing = ", ".join(map(repr, sorted(missing)))
+                raise ValueError(
+                    f"Unknown warning when setting {key!r}: {missing}. Valid entries: "
+                    + ", ".join(sorted(known_warnings))
+                )
+        return value
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/decorators.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/decorators.py
new file mode 100644
index 0000000000000000000000000000000000000000..f222744fb939454dc504bf31078fc58b3cfe0da8
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/decorators.py
@@ -0,0 +1,1233 @@
+import bz2
+import collections
+import gzip
+import inspect
+import itertools
+import re
+from collections import defaultdict
+from os.path import splitext
+from pathlib import Path
+
+import networkx as nx
+from networkx.utils import create_py_random_state, create_random_state
+
+__all__ = [
+    "not_implemented_for",
+    "open_file",
+    "nodes_or_number",
+    "np_random_state",
+    "py_random_state",
+    "argmap",
+]
+
+
+def not_implemented_for(*graph_types):
+    """Decorator to mark algorithms as not implemented
+
+    Parameters
+    ----------
+    graph_types : container of strings
+        Entries must be one of "directed", "undirected", "multigraph", or "graph".
+
+    Returns
+    -------
+    _require : function
+        The decorated function.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+    If any of the packages cannot be imported
+
+    Notes
+    -----
+    Multiple types are joined logically with "and".
+    For "or" use multiple @not_implemented_for() lines.
+
+    Examples
+    --------
+    Decorate functions like this::
+
+       @not_implemented_for("directed")
+       def sp_function(G):
+           pass
+
+
+       # rule out MultiDiGraph
+       @not_implemented_for("directed", "multigraph")
+       def sp_np_function(G):
+           pass
+
+
+       # rule out all except DiGraph
+       @not_implemented_for("undirected")
+       @not_implemented_for("multigraph")
+       def sp_np_function(G):
+           pass
+    """
+    if ("directed" in graph_types) and ("undirected" in graph_types):
+        raise ValueError("Function not implemented on directed AND undirected graphs?")
+    if ("multigraph" in graph_types) and ("graph" in graph_types):
+        raise ValueError("Function not implemented on graph AND multigraphs?")
+    if not set(graph_types) < {"directed", "undirected", "multigraph", "graph"}:
+        raise KeyError(
+            "use one or more of directed, undirected, multigraph, graph.  "
+            f"You used {graph_types}"
+        )
+
+    # 3-way logic: True if "directed" input, False if "undirected" input, else None
+    dval = ("directed" in graph_types) or "undirected" not in graph_types and None
+    mval = ("multigraph" in graph_types) or "graph" not in graph_types and None
+    errmsg = f"not implemented for {' '.join(graph_types)} type"
+
+    def _not_implemented_for(g):
+        if (mval is None or mval == g.is_multigraph()) and (
+            dval is None or dval == g.is_directed()
+        ):
+            raise nx.NetworkXNotImplemented(errmsg)
+
+        return g
+
+    return argmap(_not_implemented_for, 0)
+
+
+# To handle new extensions, define a function accepting a `path` and `mode`.
+# Then add the extension to _dispatch_dict.
+fopeners = {
+    ".gz": gzip.open,
+    ".gzip": gzip.open,
+    ".bz2": bz2.BZ2File,
+}
+_dispatch_dict = defaultdict(lambda: open, **fopeners)
+
+
+def open_file(path_arg, mode="r"):
+    """Decorator to ensure clean opening and closing of files.
+
+    Parameters
+    ----------
+    path_arg : string or int
+        Name or index of the argument that is a path.
+
+    mode : str
+        String for opening mode.
+
+    Returns
+    -------
+    _open_file : function
+        Function which cleanly executes the io.
+
+    Examples
+    --------
+    Decorate functions like this::
+
+       @open_file(0, "r")
+       def read_function(pathname):
+           pass
+
+
+       @open_file(1, "w")
+       def write_function(G, pathname):
+           pass
+
+
+       @open_file(1, "w")
+       def write_function(G, pathname="graph.dot"):
+           pass
+
+
+       @open_file("pathname", "w")
+       def write_function(G, pathname="graph.dot"):
+           pass
+
+
+       @open_file("path", "w+")
+       def another_function(arg, **kwargs):
+           path = kwargs["path"]
+           pass
+
+    Notes
+    -----
+    Note that this decorator solves the problem when a path argument is
+    specified as a string, but it does not handle the situation when the
+    function wants to accept a default of None (and then handle it).
+
+    Here is an example of how to handle this case::
+
+      @open_file("path")
+      def some_function(arg1, arg2, path=None):
+          if path is None:
+              fobj = tempfile.NamedTemporaryFile(delete=False)
+          else:
+              # `path` could have been a string or file object or something
+              # similar. In any event, the decorator has given us a file object
+              # and it will close it for us, if it should.
+              fobj = path
+
+          try:
+              fobj.write("blah")
+          finally:
+              if path is None:
+                  fobj.close()
+
+    Normally, we'd want to use "with" to ensure that fobj gets closed.
+    However, the decorator will make `path` a file object for us,
+    and using "with" would undesirably close that file object.
+    Instead, we use a try block, as shown above.
+    When we exit the function, fobj will be closed, if it should be, by the decorator.
+    """
+
+    def _open_file(path):
+        # Now we have the path_arg. There are two types of input to consider:
+        #   1) string representing a path that should be opened
+        #   2) an already opened file object
+        if isinstance(path, str):
+            ext = splitext(path)[1]
+        elif isinstance(path, Path):
+            # path is a pathlib reference to a filename
+            ext = path.suffix
+            path = str(path)
+        else:
+            # could be None, or a file handle, in which case the algorithm will deal with it
+            return path, lambda: None
+
+        fobj = _dispatch_dict[ext](path, mode=mode)
+        return fobj, lambda: fobj.close()
+
+    return argmap(_open_file, path_arg, try_finally=True)
+
+
+def nodes_or_number(which_args):
+    """Decorator to allow number of nodes or container of nodes.
+
+    With this decorator, the specified argument can be either a number or a container
+    of nodes. If it is a number, the nodes used are `range(n)`.
+    This allows `nx.complete_graph(50)` in place of `nx.complete_graph(list(range(50)))`.
+    And it also allows `nx.complete_graph(any_list_of_nodes)`.
+
+    Parameters
+    ----------
+    which_args : string or int or sequence of strings or ints
+        If string, the name of the argument to be treated.
+        If int, the index of the argument to be treated.
+        If more than one node argument is allowed, can be a list of locations.
+
+    Returns
+    -------
+    _nodes_or_numbers : function
+        Function which replaces int args with ranges.
+
+    Examples
+    --------
+    Decorate functions like this::
+
+       @nodes_or_number("nodes")
+       def empty_graph(nodes):
+           # nodes is converted to a list of nodes
+
+       @nodes_or_number(0)
+       def empty_graph(nodes):
+           # nodes is converted to a list of nodes
+
+       @nodes_or_number(["m1", "m2"])
+       def grid_2d_graph(m1, m2, periodic=False):
+           # m1 and m2 are each converted to a list of nodes
+
+       @nodes_or_number([0, 1])
+       def grid_2d_graph(m1, m2, periodic=False):
+           # m1 and m2 are each converted to a list of nodes
+
+       @nodes_or_number(1)
+       def full_rary_tree(r, n)
+           # presumably r is a number. It is not handled by this decorator.
+           # n is converted to a list of nodes
+    """
+
+    def _nodes_or_number(n):
+        try:
+            nodes = list(range(n))
+        except TypeError:
+            nodes = tuple(n)
+        else:
+            if n < 0:
+                raise nx.NetworkXError(f"Negative number of nodes not valid: {n}")
+        return (n, nodes)
+
+    try:
+        iter_wa = iter(which_args)
+    except TypeError:
+        iter_wa = (which_args,)
+
+    return argmap(_nodes_or_number, *iter_wa)
+
+
+def np_random_state(random_state_argument):
+    """Decorator to generate a numpy RandomState or Generator instance.
+
+    The decorator processes the argument indicated by `random_state_argument`
+    using :func:`nx.utils.create_random_state`.
+    The argument value can be a seed (integer), or a `numpy.random.RandomState`
+    or `numpy.random.RandomState` instance or (`None` or `numpy.random`).
+    The latter two options use the global random number generator for `numpy.random`.
+
+    The returned instance is a `numpy.random.RandomState` or `numpy.random.Generator`.
+
+    Parameters
+    ----------
+    random_state_argument : string or int
+        The name or index of the argument to be converted
+        to a `numpy.random.RandomState` instance.
+
+    Returns
+    -------
+    _random_state : function
+        Function whose random_state keyword argument is a RandomState instance.
+
+    Examples
+    --------
+    Decorate functions like this::
+
+       @np_random_state("seed")
+       def random_float(seed=None):
+           return seed.rand()
+
+
+       @np_random_state(0)
+       def random_float(rng=None):
+           return rng.rand()
+
+
+       @np_random_state(1)
+       def random_array(dims, random_state=1):
+           return random_state.rand(*dims)
+
+    See Also
+    --------
+    py_random_state
+    """
+    return argmap(create_random_state, random_state_argument)
+
+
+def py_random_state(random_state_argument):
+    """Decorator to generate a random.Random instance (or equiv).
+
+    This decorator processes `random_state_argument` using
+    :func:`nx.utils.create_py_random_state`.
+    The input value can be a seed (integer), or a random number generator::
+
+        If int, return a random.Random instance set with seed=int.
+        If random.Random instance, return it.
+        If None or the `random` package, return the global random number
+        generator used by `random`.
+        If np.random package, or the default numpy RandomState instance,
+        return the default numpy random number generator wrapped in a
+        `PythonRandomViaNumpyBits`  class.
+        If np.random.Generator instance, return it wrapped in a
+        `PythonRandomViaNumpyBits`  class.
+
+        # Legacy options
+        If np.random.RandomState instance, return it wrapped in a
+        `PythonRandomInterface` class.
+        If a `PythonRandomInterface` instance, return it
+
+    Parameters
+    ----------
+    random_state_argument : string or int
+        The name of the argument or the index of the argument in args that is
+        to be converted to the random.Random instance or numpy.random.RandomState
+        instance that mimics basic methods of random.Random.
+
+    Returns
+    -------
+    _random_state : function
+        Function whose random_state_argument is converted to a Random instance.
+
+    Examples
+    --------
+    Decorate functions like this::
+
+       @py_random_state("random_state")
+       def random_float(random_state=None):
+           return random_state.rand()
+
+
+       @py_random_state(0)
+       def random_float(rng=None):
+           return rng.rand()
+
+
+       @py_random_state(1)
+       def random_array(dims, seed=12345):
+           return seed.rand(*dims)
+
+    See Also
+    --------
+    np_random_state
+    """
+
+    return argmap(create_py_random_state, random_state_argument)
+
+
+class argmap:
+    """A decorator to apply a map to arguments before calling the function
+
+    This class provides a decorator that maps (transforms) arguments of the function
+    before the function is called. Thus for example, we have similar code
+    in many functions to determine whether an argument is the number of nodes
+    to be created, or a list of nodes to be handled. The decorator provides
+    the code to accept either -- transforming the indicated argument into a
+    list of nodes before the actual function is called.
+
+    This decorator class allows us to process single or multiple arguments.
+    The arguments to be processed can be specified by string, naming the argument,
+    or by index, specifying the item in the args list.
+
+    Parameters
+    ----------
+    func : callable
+        The function to apply to arguments
+
+    *args : iterable of (int, str or tuple)
+        A list of parameters, specified either as strings (their names), ints
+        (numerical indices) or tuples, which may contain ints, strings, and
+        (recursively) tuples. Each indicates which parameters the decorator
+        should map. Tuples indicate that the map function takes (and returns)
+        multiple parameters in the same order and nested structure as indicated
+        here.
+
+    try_finally : bool (default: False)
+        When True, wrap the function call in a try-finally block with code
+        for the finally block created by `func`. This is used when the map
+        function constructs an object (like a file handle) that requires
+        post-processing (like closing).
+
+        Note: try_finally decorators cannot be used to decorate generator
+        functions.
+
+    Examples
+    --------
+    Most of these examples use `@argmap(...)` to apply the decorator to
+    the function defined on the next line.
+    In the NetworkX codebase however, `argmap` is used within a function to
+    construct a decorator. That is, the decorator defines a mapping function
+    and then uses `argmap` to build and return a decorated function.
+    A simple example is a decorator that specifies which currency to report money.
+    The decorator (named `convert_to`) would be used like::
+
+        @convert_to("US_Dollars", "income")
+        def show_me_the_money(name, income):
+            print(f"{name} : {income}")
+
+    And the code to create the decorator might be::
+
+        def convert_to(currency, which_arg):
+            def _convert(amount):
+                if amount.currency != currency:
+                    amount = amount.to_currency(currency)
+                return amount
+
+            return argmap(_convert, which_arg)
+
+    Despite this common idiom for argmap, most of the following examples
+    use the `@argmap(...)` idiom to save space.
+
+    Here's an example use of argmap to sum the elements of two of the functions
+    arguments. The decorated function::
+
+        @argmap(sum, "xlist", "zlist")
+        def foo(xlist, y, zlist):
+            return xlist - y + zlist
+
+    is syntactic sugar for::
+
+        def foo(xlist, y, zlist):
+            x = sum(xlist)
+            z = sum(zlist)
+            return x - y + z
+
+    and is equivalent to (using argument indexes)::
+
+        @argmap(sum, "xlist", 2)
+        def foo(xlist, y, zlist):
+            return xlist - y + zlist
+
+    or::
+
+        @argmap(sum, "zlist", 0)
+        def foo(xlist, y, zlist):
+            return xlist - y + zlist
+
+    Transforming functions can be applied to multiple arguments, such as::
+
+        def swap(x, y):
+            return y, x
+
+        # the 2-tuple tells argmap that the map `swap` has 2 inputs/outputs.
+        @argmap(swap, ("a", "b")):
+        def foo(a, b, c):
+            return a / b * c
+
+    is equivalent to::
+
+        def foo(a, b, c):
+            a, b = swap(a, b)
+            return a / b * c
+
+    More generally, the applied arguments can be nested tuples of strings or ints.
+    The syntax `@argmap(some_func, ("a", ("b", "c")))` would expect `some_func` to
+    accept 2 inputs with the second expected to be a 2-tuple. It should then return
+    2 outputs with the second a 2-tuple. The returns values would replace input "a"
+    "b" and "c" respectively. Similarly for `@argmap(some_func, (0, ("b", 2)))`.
+
+    Also, note that an index larger than the number of named parameters is allowed
+    for variadic functions. For example::
+
+        def double(a):
+            return 2 * a
+
+
+        @argmap(double, 3)
+        def overflow(a, *args):
+            return a, args
+
+
+        print(overflow(1, 2, 3, 4, 5, 6))  # output is 1, (2, 3, 8, 5, 6)
+
+    **Try Finally**
+
+    Additionally, this `argmap` class can be used to create a decorator that
+    initiates a try...finally block. The decorator must be written to return
+    both the transformed argument and a closing function.
+    This feature was included to enable the `open_file` decorator which might
+    need to close the file or not depending on whether it had to open that file.
+    This feature uses the keyword-only `try_finally` argument to `@argmap`.
+
+    For example this map opens a file and then makes sure it is closed::
+
+        def open_file(fn):
+            f = open(fn)
+            return f, lambda: f.close()
+
+    The decorator applies that to the function `foo`::
+
+        @argmap(open_file, "file", try_finally=True)
+        def foo(file):
+            print(file.read())
+
+    is syntactic sugar for::
+
+        def foo(file):
+            file, close_file = open_file(file)
+            try:
+                print(file.read())
+            finally:
+                close_file()
+
+    and is equivalent to (using indexes)::
+
+        @argmap(open_file, 0, try_finally=True)
+        def foo(file):
+            print(file.read())
+
+    Here's an example of the try_finally feature used to create a decorator::
+
+        def my_closing_decorator(which_arg):
+            def _opener(path):
+                if path is None:
+                    path = open(path)
+                    fclose = path.close
+                else:
+                    # assume `path` handles the closing
+                    fclose = lambda: None
+                return path, fclose
+
+            return argmap(_opener, which_arg, try_finally=True)
+
+    which can then be used as::
+
+        @my_closing_decorator("file")
+        def fancy_reader(file=None):
+            # this code doesn't need to worry about closing the file
+            print(file.read())
+
+    Decorators with try_finally = True cannot be used with generator functions,
+    because the `finally` block is evaluated before the generator is exhausted::
+
+        @argmap(open_file, "file", try_finally=True)
+        def file_to_lines(file):
+            for line in file.readlines():
+                yield line
+
+    is equivalent to::
+
+        def file_to_lines_wrapped(file):
+            for line in file.readlines():
+                yield line
+
+
+        def file_to_lines_wrapper(file):
+            try:
+                file = open_file(file)
+                return file_to_lines_wrapped(file)
+            finally:
+                file.close()
+
+    which behaves similarly to::
+
+        def file_to_lines_whoops(file):
+            file = open_file(file)
+            file.close()
+            for line in file.readlines():
+                yield line
+
+    because the `finally` block of `file_to_lines_wrapper` is executed before
+    the caller has a chance to exhaust the iterator.
+
+    Notes
+    -----
+    An object of this class is callable and intended to be used when
+    defining a decorator. Generally, a decorator takes a function as input
+    and constructs a function as output. Specifically, an `argmap` object
+    returns the input function decorated/wrapped so that specified arguments
+    are mapped (transformed) to new values before the decorated function is called.
+
+    As an overview, the argmap object returns a new function with all the
+    dunder values of the original function (like `__doc__`, `__name__`, etc).
+    Code for this decorated function is built based on the original function's
+    signature. It starts by mapping the input arguments to potentially new
+    values. Then it calls the decorated function with these new values in place
+    of the indicated arguments that have been mapped. The return value of the
+    original function is then returned. This new function is the function that
+    is actually called by the user.
+
+    Three additional features are provided.
+        1) The code is lazily compiled. That is, the new function is returned
+        as an object without the code compiled, but with all information
+        needed so it can be compiled upon it's first invocation. This saves
+        time on import at the cost of additional time on the first call of
+        the function. Subsequent calls are then just as fast as normal.
+
+        2) If the "try_finally" keyword-only argument is True, a try block
+        follows each mapped argument, matched on the other side of the wrapped
+        call, by a finally block closing that mapping.  We expect func to return
+        a 2-tuple: the mapped value and a function to be called in the finally
+        clause.  This feature was included so the `open_file` decorator could
+        provide a file handle to the decorated function and close the file handle
+        after the function call. It even keeps track of whether to close the file
+        handle or not based on whether it had to open the file or the input was
+        already open. So, the decorated function does not need to include any
+        code to open or close files.
+
+        3) The maps applied can process multiple arguments. For example,
+        you could swap two arguments using a mapping, or transform
+        them to their sum and their difference. This was included to allow
+        a decorator in the `quality.py` module that checks that an input
+        `partition` is a valid partition of the nodes of the input graph `G`.
+        In this example, the map has inputs `(G, partition)`. After checking
+        for a valid partition, the map either raises an exception or leaves
+        the inputs unchanged. Thus many functions that make this check can
+        use the decorator rather than copy the checking code into each function.
+        More complicated nested argument structures are described below.
+
+    The remaining notes describe the code structure and methods for this
+    class in broad terms to aid in understanding how to use it.
+
+    Instantiating an `argmap` object simply stores the mapping function and
+    the input identifiers of which arguments to map. The resulting decorator
+    is ready to use this map to decorate any function. Calling that object
+    (`argmap.__call__`, but usually done via `@my_decorator`) a lazily
+    compiled thin wrapper of the decorated function is constructed,
+    wrapped with the necessary function dunder attributes like `__doc__`
+    and `__name__`. That thinly wrapped function is returned as the
+    decorated function. When that decorated function is called, the thin
+    wrapper of code calls `argmap._lazy_compile` which compiles the decorated
+    function (using `argmap.compile`) and replaces the code of the thin
+    wrapper with the newly compiled code. This saves the compilation step
+    every import of networkx, at the cost of compiling upon the first call
+    to the decorated function.
+
+    When the decorated function is compiled, the code is recursively assembled
+    using the `argmap.assemble` method. The recursive nature is needed in
+    case of nested decorators. The result of the assembly is a number of
+    useful objects.
+
+      sig : the function signature of the original decorated function as
+          constructed by :func:`argmap.signature`. This is constructed
+          using `inspect.signature` but enhanced with attribute
+          strings `sig_def` and `sig_call`, and other information
+          specific to mapping arguments of this function.
+          This information is used to construct a string of code defining
+          the new decorated function.
+
+      wrapped_name : a unique internally used name constructed by argmap
+          for the decorated function.
+
+      functions : a dict of the functions used inside the code of this
+          decorated function, to be used as `globals` in `exec`.
+          This dict is recursively updated to allow for nested decorating.
+
+      mapblock : code (as a list of strings) to map the incoming argument
+          values to their mapped values.
+
+      finallys : code (as a list of strings) to provide the possibly nested
+          set of finally clauses if needed.
+
+      mutable_args : a bool indicating whether the `sig.args` tuple should be
+          converted to a list so mutation can occur.
+
+    After this recursive assembly process, the `argmap.compile` method
+    constructs code (as strings) to convert the tuple `sig.args` to a list
+    if needed. It joins the defining code with appropriate indents and
+    compiles the result.  Finally, this code is evaluated and the original
+    wrapper's implementation is replaced with the compiled version (see
+    `argmap._lazy_compile` for more details).
+
+    Other `argmap` methods include `_name` and `_count` which allow internally
+    generated names to be unique within a python session.
+    The methods `_flatten` and `_indent` process the nested lists of strings
+    into properly indented python code ready to be compiled.
+
+    More complicated nested tuples of arguments also allowed though
+    usually not used. For the simple 2 argument case, the argmap
+    input ("a", "b") implies the mapping function will take 2 arguments
+    and return a 2-tuple of mapped values. A more complicated example
+    with argmap input `("a", ("b", "c"))` requires the mapping function
+    take 2 inputs, with the second being a 2-tuple. It then must output
+    the 3 mapped values in the same nested structure `(newa, (newb, newc))`.
+    This level of generality is not often needed, but was convenient
+    to implement when handling the multiple arguments.
+
+    See Also
+    --------
+    not_implemented_for
+    open_file
+    nodes_or_number
+    py_random_state
+    networkx.algorithms.community.quality.require_partition
+
+    """
+
+    def __init__(self, func, *args, try_finally=False):
+        self._func = func
+        self._args = args
+        self._finally = try_finally
+
+    @staticmethod
+    def _lazy_compile(func):
+        """Compile the source of a wrapped function
+
+        Assemble and compile the decorated function, and intrusively replace its
+        code with the compiled version's.  The thinly wrapped function becomes
+        the decorated function.
+
+        Parameters
+        ----------
+        func : callable
+            A function returned by argmap.__call__ which is in the process
+            of being called for the first time.
+
+        Returns
+        -------
+        func : callable
+            The same function, with a new __code__ object.
+
+        Notes
+        -----
+        It was observed in NetworkX issue #4732 [1] that the import time of
+        NetworkX was significantly bloated by the use of decorators: over half
+        of the import time was being spent decorating functions.  This was
+        somewhat improved by a change made to the `decorator` library, at the
+        cost of a relatively heavy-weight call to `inspect.Signature.bind`
+        for each call to the decorated function.
+
+        The workaround we arrived at is to do minimal work at the time of
+        decoration.  When the decorated function is called for the first time,
+        we compile a function with the same function signature as the wrapped
+        function.  The resulting decorated function is faster than one made by
+        the `decorator` library, so that the overhead of the first call is
+        'paid off' after a small number of calls.
+
+        References
+        ----------
+
+        [1] https://github.com/networkx/networkx/issues/4732
+
+        """
+        real_func = func.__argmap__.compile(func.__wrapped__)
+        func.__code__ = real_func.__code__
+        func.__globals__.update(real_func.__globals__)
+        func.__dict__.update(real_func.__dict__)
+        return func
+
+    def __call__(self, f):
+        """Construct a lazily decorated wrapper of f.
+
+        The decorated function will be compiled when it is called for the first time,
+        and it will replace its own __code__ object so subsequent calls are fast.
+
+        Parameters
+        ----------
+        f : callable
+            A function to be decorated.
+
+        Returns
+        -------
+        func : callable
+            The decorated function.
+
+        See Also
+        --------
+        argmap._lazy_compile
+        """
+
+        def func(*args, __wrapper=None, **kwargs):
+            return argmap._lazy_compile(__wrapper)(*args, **kwargs)
+
+        # standard function-wrapping stuff
+        func.__name__ = f.__name__
+        func.__doc__ = f.__doc__
+        func.__defaults__ = f.__defaults__
+        func.__kwdefaults__.update(f.__kwdefaults__ or {})
+        func.__module__ = f.__module__
+        func.__qualname__ = f.__qualname__
+        func.__dict__.update(f.__dict__)
+        func.__wrapped__ = f
+
+        # now that we've wrapped f, we may have picked up some __dict__ or
+        # __kwdefaults__ items that were set by a previous argmap.  Thus, we set
+        # these values after those update() calls.
+
+        # If we attempt to access func from within itself, that happens through
+        # a closure -- which trips an error when we replace func.__code__.  The
+        # standard workaround for functions which can't see themselves is to use
+        # a Y-combinator, as we do here.
+        func.__kwdefaults__["_argmap__wrapper"] = func
+
+        # this self-reference is here because functools.wraps preserves
+        # everything in __dict__, and we don't want to mistake a non-argmap
+        # wrapper for an argmap wrapper
+        func.__self__ = func
+
+        # this is used to variously call self.assemble and self.compile
+        func.__argmap__ = self
+
+        if hasattr(f, "__argmap__"):
+            func.__is_generator = f.__is_generator
+        else:
+            func.__is_generator = inspect.isgeneratorfunction(f)
+
+        if self._finally and func.__is_generator:
+            raise nx.NetworkXError("argmap cannot decorate generators with try_finally")
+
+        return func
+
+    __count = 0
+
+    @classmethod
+    def _count(cls):
+        """Maintain a globally-unique identifier for function names and "file" names
+
+        Note that this counter is a class method reporting a class variable
+        so the count is unique within a Python session. It could differ from
+        session to session for a specific decorator depending on the order
+        that the decorators are created. But that doesn't disrupt `argmap`.
+
+        This is used in two places: to construct unique variable names
+        in the `_name` method and to construct unique fictitious filenames
+        in the `_compile` method.
+
+        Returns
+        -------
+        count : int
+            An integer unique to this Python session (simply counts from zero)
+        """
+        cls.__count += 1
+        return cls.__count
+
+    _bad_chars = re.compile("[^a-zA-Z0-9_]")
+
+    @classmethod
+    def _name(cls, f):
+        """Mangle the name of a function to be unique but somewhat human-readable
+
+        The names are unique within a Python session and set using `_count`.
+
+        Parameters
+        ----------
+        f : str or object
+
+        Returns
+        -------
+        name : str
+            The mangled version of `f.__name__` (if `f.__name__` exists) or `f`
+
+        """
+        f = f.__name__ if hasattr(f, "__name__") else f
+        fname = re.sub(cls._bad_chars, "_", f)
+        return f"argmap_{fname}_{cls._count()}"
+
+    def compile(self, f):
+        """Compile the decorated function.
+
+        Called once for a given decorated function -- collects the code from all
+        argmap decorators in the stack, and compiles the decorated function.
+
+        Much of the work done here uses the `assemble` method to allow recursive
+        treatment of multiple argmap decorators on a single decorated function.
+        That flattens the argmap decorators, collects the source code to construct
+        a single decorated function, then compiles/executes/returns that function.
+
+        The source code for the decorated function is stored as an attribute
+        `_code` on the function object itself.
+
+        Note that Python's `compile` function requires a filename, but this
+        code is constructed without a file, so a fictitious filename is used
+        to describe where the function comes from. The name is something like:
+        "argmap compilation 4".
+
+        Parameters
+        ----------
+        f : callable
+            The function to be decorated
+
+        Returns
+        -------
+        func : callable
+            The decorated file
+
+        """
+        sig, wrapped_name, functions, mapblock, finallys, mutable_args = self.assemble(
+            f
+        )
+
+        call = f"{sig.call_sig.format(wrapped_name)}#"
+        mut_args = f"{sig.args} = list({sig.args})" if mutable_args else ""
+        body = argmap._indent(sig.def_sig, mut_args, mapblock, call, finallys)
+        code = "\n".join(body)
+
+        locl = {}
+        globl = dict(functions.values())
+        filename = f"{self.__class__} compilation {self._count()}"
+        compiled = compile(code, filename, "exec")
+        exec(compiled, globl, locl)
+        func = locl[sig.name]
+        func._code = code
+        return func
+
+    def assemble(self, f):
+        """Collects components of the source for the decorated function wrapping f.
+
+        If `f` has multiple argmap decorators, we recursively assemble the stack of
+        decorators into a single flattened function.
+
+        This method is part of the `compile` method's process yet separated
+        from that method to allow recursive processing. The outputs are
+        strings, dictionaries and lists that collect needed info to
+        flatten any nested argmap-decoration.
+
+        Parameters
+        ----------
+        f : callable
+            The function to be decorated.  If f is argmapped, we assemble it.
+
+        Returns
+        -------
+        sig : argmap.Signature
+            The function signature as an `argmap.Signature` object.
+        wrapped_name : str
+            The mangled name used to represent the wrapped function in the code
+            being assembled.
+        functions : dict
+            A dictionary mapping id(g) -> (mangled_name(g), g) for functions g
+            referred to in the code being assembled. These need to be present
+            in the ``globals`` scope of ``exec`` when defining the decorated
+            function.
+        mapblock : list of lists and/or strings
+            Code that implements mapping of parameters including any try blocks
+            if needed. This code will precede the decorated function call.
+        finallys : list of lists and/or strings
+            Code that implements the finally blocks to post-process the
+            arguments (usually close any files if needed) after the
+            decorated function is called.
+        mutable_args : bool
+            True if the decorator needs to modify positional arguments
+            via their indices. The compile method then turns the argument
+            tuple into a list so that the arguments can be modified.
+        """
+
+        # first, we check if f is already argmapped -- if that's the case,
+        # build up the function recursively.
+        # > mapblock is generally a list of function calls of the sort
+        #     arg = func(arg)
+        # in addition to some try-blocks if needed.
+        # > finallys is a recursive list of finally blocks of the sort
+        #         finally:
+        #             close_func_1()
+        #     finally:
+        #         close_func_2()
+        # > functions is a dict of functions used in the scope of our decorated
+        # function. It will be used to construct globals used in compilation.
+        # We make functions[id(f)] = name_of_f, f to ensure that a given
+        # function is stored and named exactly once even if called by
+        # nested decorators.
+        if hasattr(f, "__argmap__") and f.__self__ is f:
+            (
+                sig,
+                wrapped_name,
+                functions,
+                mapblock,
+                finallys,
+                mutable_args,
+            ) = f.__argmap__.assemble(f.__wrapped__)
+            functions = dict(functions)  # shallow-copy just in case
+        else:
+            sig = self.signature(f)
+            wrapped_name = self._name(f)
+            mapblock, finallys = [], []
+            functions = {id(f): (wrapped_name, f)}
+            mutable_args = False
+
+        if id(self._func) in functions:
+            fname, _ = functions[id(self._func)]
+        else:
+            fname, _ = functions[id(self._func)] = self._name(self._func), self._func
+
+        # this is a bit complicated -- we can call functions with a variety of
+        # nested arguments, so long as their input and output are tuples with
+        # the same nested structure. e.g. ("a", "b") maps arguments a and b.
+        # A more complicated nesting like (0, (3, 4)) maps arguments 0, 3, 4
+        # expecting the mapping to output new values in the same nested shape.
+        # The ability to argmap multiple arguments was necessary for
+        # the decorator `nx.algorithms.community.quality.require_partition`, and
+        # while we're not taking full advantage of the ability to handle
+        # multiply-nested tuples, it was convenient to implement this in
+        # generality because the recursive call to `get_name` is necessary in
+        # any case.
+        applied = set()
+
+        def get_name(arg, first=True):
+            nonlocal mutable_args
+            if isinstance(arg, tuple):
+                name = ", ".join(get_name(x, False) for x in arg)
+                return name if first else f"({name})"
+            if arg in applied:
+                raise nx.NetworkXError(f"argument {arg} is specified multiple times")
+            applied.add(arg)
+            if arg in sig.names:
+                return sig.names[arg]
+            elif isinstance(arg, str):
+                if sig.kwargs is None:
+                    raise nx.NetworkXError(
+                        f"name {arg} is not a named parameter and this function doesn't have kwargs"
+                    )
+                return f"{sig.kwargs}[{arg!r}]"
+            else:
+                if sig.args is None:
+                    raise nx.NetworkXError(
+                        f"index {arg} not a parameter index and this function doesn't have args"
+                    )
+                mutable_args = True
+                return f"{sig.args}[{arg - sig.n_positional}]"
+
+        if self._finally:
+            # here's where we handle try_finally decorators.  Such a decorator
+            # returns a mapped argument and a function to be called in a
+            # finally block.  This feature was required by the open_file
+            # decorator.  The below generates the code
+            #
+            # name, final = func(name)                   #<--append to mapblock
+            # try:                                       #<--append to mapblock
+            #     ... more argmapping and try blocks
+            #     return WRAPPED_FUNCTION(...)
+            #     ... more finally blocks
+            # finally:                                   #<--prepend to finallys
+            #     final()                                #<--prepend to finallys
+            #
+            for a in self._args:
+                name = get_name(a)
+                final = self._name(name)
+                mapblock.append(f"{name}, {final} = {fname}({name})")
+                mapblock.append("try:")
+                finallys = ["finally:", f"{final}()#", "#", finallys]
+        else:
+            mapblock.extend(
+                f"{name} = {fname}({name})" for name in map(get_name, self._args)
+            )
+
+        return sig, wrapped_name, functions, mapblock, finallys, mutable_args
+
+    @classmethod
+    def signature(cls, f):
+        r"""Construct a Signature object describing `f`
+
+        Compute a Signature so that we can write a function wrapping f with
+        the same signature and call-type.
+
+        Parameters
+        ----------
+        f : callable
+            A function to be decorated
+
+        Returns
+        -------
+        sig : argmap.Signature
+            The Signature of f
+
+        Notes
+        -----
+        The Signature is a namedtuple with names:
+
+            name : a unique version of the name of the decorated function
+            signature : the inspect.signature of the decorated function
+            def_sig : a string used as code to define the new function
+            call_sig : a string used as code to call the decorated function
+            names : a dict keyed by argument name and index to the argument's name
+            n_positional : the number of positional arguments in the signature
+            args : the name of the VAR_POSITIONAL argument if any, i.e. \*theseargs
+            kwargs : the name of the VAR_KEYWORDS argument if any, i.e. \*\*kwargs
+
+        These named attributes of the signature are used in `assemble` and `compile`
+        to construct a string of source code for the decorated function.
+
+        """
+        sig = inspect.signature(f, follow_wrapped=False)
+        def_sig = []
+        call_sig = []
+        names = {}
+
+        kind = None
+        args = None
+        kwargs = None
+        npos = 0
+        for i, param in enumerate(sig.parameters.values()):
+            # parameters can be position-only, keyword-or-position, keyword-only
+            # in any combination, but only in the order as above.  we do edge
+            # detection to add the appropriate punctuation
+            prev = kind
+            kind = param.kind
+            if prev == param.POSITIONAL_ONLY != kind:
+                # the last token was position-only, but this one isn't
+                def_sig.append("/")
+            if (
+                param.VAR_POSITIONAL
+                != prev
+                != param.KEYWORD_ONLY
+                == kind
+                != param.VAR_POSITIONAL
+            ):
+                # param is the first keyword-only arg and isn't starred
+                def_sig.append("*")
+
+            # star arguments as appropriate
+            if kind == param.VAR_POSITIONAL:
+                name = "*" + param.name
+                args = param.name
+                count = 0
+            elif kind == param.VAR_KEYWORD:
+                name = "**" + param.name
+                kwargs = param.name
+                count = 0
+            else:
+                names[i] = names[param.name] = param.name
+                name = param.name
+                count = 1
+
+            # assign to keyword-only args in the function call
+            if kind == param.KEYWORD_ONLY:
+                call_sig.append(f"{name} = {name}")
+            else:
+                npos += count
+                call_sig.append(name)
+
+            def_sig.append(name)
+
+        fname = cls._name(f)
+        def_sig = f"def {fname}({', '.join(def_sig)}):"
+
+        call_sig = f"return {{}}({', '.join(call_sig)})"
+
+        return cls.Signature(fname, sig, def_sig, call_sig, names, npos, args, kwargs)
+
+    Signature = collections.namedtuple(
+        "Signature",
+        [
+            "name",
+            "signature",
+            "def_sig",
+            "call_sig",
+            "names",
+            "n_positional",
+            "args",
+            "kwargs",
+        ],
+    )
+
+    @staticmethod
+    def _flatten(nestlist, visited):
+        """flattens a recursive list of lists that doesn't have cyclic references
+
+        Parameters
+        ----------
+        nestlist : iterable
+            A recursive list of objects to be flattened into a single iterable
+
+        visited : set
+            A set of object ids which have been walked -- initialize with an
+            empty set
+
+        Yields
+        ------
+        Non-list objects contained in nestlist
+
+        """
+        for thing in nestlist:
+            if isinstance(thing, list):
+                if id(thing) in visited:
+                    raise ValueError("A cycle was found in nestlist.  Be a tree.")
+                else:
+                    visited.add(id(thing))
+                yield from argmap._flatten(thing, visited)
+            else:
+                yield thing
+
+    _tabs = " " * 64
+
+    @staticmethod
+    def _indent(*lines):
+        """Indent list of code lines to make executable Python code
+
+        Indents a tree-recursive list of strings, following the rule that one
+        space is added to the tab after a line that ends in a colon, and one is
+        removed after a line that ends in an hashmark.
+
+        Parameters
+        ----------
+        *lines : lists and/or strings
+            A recursive list of strings to be assembled into properly indented
+            code.
+
+        Returns
+        -------
+        code : str
+
+        Examples
+        --------
+
+            argmap._indent(*["try:", "try:", "pass#", "finally:", "pass#", "#",
+                             "finally:", "pass#"])
+
+        renders to
+
+            '''try:
+             try:
+              pass#
+             finally:
+              pass#
+             #
+            finally:
+             pass#'''
+        """
+        depth = 0
+        for line in argmap._flatten(lines, set()):
+            yield f"{argmap._tabs[:depth]}{line}"
+            depth += (line[-1:] == ":") - (line[-1:] == "#")
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/heaps.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/heaps.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d67dfd3ff381d51e2ece51aae7ea27ee3091acb
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/heaps.py
@@ -0,0 +1,338 @@
+"""
+Min-heaps.
+"""
+
+from heapq import heappop, heappush
+from itertools import count
+
+import networkx as nx
+
+__all__ = ["MinHeap", "PairingHeap", "BinaryHeap"]
+
+
+class MinHeap:
+    """Base class for min-heaps.
+
+    A MinHeap stores a collection of key-value pairs ordered by their values.
+    It supports querying the minimum pair, inserting a new pair, decreasing the
+    value in an existing pair and deleting the minimum pair.
+    """
+
+    class _Item:
+        """Used by subclassess to represent a key-value pair."""
+
+        __slots__ = ("key", "value")
+
+        def __init__(self, key, value):
+            self.key = key
+            self.value = value
+
+        def __repr__(self):
+            return repr((self.key, self.value))
+
+    def __init__(self):
+        """Initialize a new min-heap."""
+        self._dict = {}
+
+    def min(self):
+        """Query the minimum key-value pair.
+
+        Returns
+        -------
+        key, value : tuple
+            The key-value pair with the minimum value in the heap.
+
+        Raises
+        ------
+        NetworkXError
+            If the heap is empty.
+        """
+        raise NotImplementedError
+
+    def pop(self):
+        """Delete the minimum pair in the heap.
+
+        Returns
+        -------
+        key, value : tuple
+            The key-value pair with the minimum value in the heap.
+
+        Raises
+        ------
+        NetworkXError
+            If the heap is empty.
+        """
+        raise NotImplementedError
+
+    def get(self, key, default=None):
+        """Returns the value associated with a key.
+
+        Parameters
+        ----------
+        key : hashable object
+            The key to be looked up.
+
+        default : object
+            Default value to return if the key is not present in the heap.
+            Default value: None.
+
+        Returns
+        -------
+        value : object.
+            The value associated with the key.
+        """
+        raise NotImplementedError
+
+    def insert(self, key, value, allow_increase=False):
+        """Insert a new key-value pair or modify the value in an existing
+        pair.
+
+        Parameters
+        ----------
+        key : hashable object
+            The key.
+
+        value : object comparable with existing values.
+            The value.
+
+        allow_increase : bool
+            Whether the value is allowed to increase. If False, attempts to
+            increase an existing value have no effect. Default value: False.
+
+        Returns
+        -------
+        decreased : bool
+            True if a pair is inserted or the existing value is decreased.
+        """
+        raise NotImplementedError
+
+    def __nonzero__(self):
+        """Returns whether the heap if empty."""
+        return bool(self._dict)
+
+    def __bool__(self):
+        """Returns whether the heap if empty."""
+        return bool(self._dict)
+
+    def __len__(self):
+        """Returns the number of key-value pairs in the heap."""
+        return len(self._dict)
+
+    def __contains__(self, key):
+        """Returns whether a key exists in the heap.
+
+        Parameters
+        ----------
+        key : any hashable object.
+            The key to be looked up.
+        """
+        return key in self._dict
+
+
+class PairingHeap(MinHeap):
+    """A pairing heap."""
+
+    class _Node(MinHeap._Item):
+        """A node in a pairing heap.
+
+        A tree in a pairing heap is stored using the left-child, right-sibling
+        representation.
+        """
+
+        __slots__ = ("left", "next", "prev", "parent")
+
+        def __init__(self, key, value):
+            super().__init__(key, value)
+            # The leftmost child.
+            self.left = None
+            # The next sibling.
+            self.next = None
+            # The previous sibling.
+            self.prev = None
+            # The parent.
+            self.parent = None
+
+    def __init__(self):
+        """Initialize a pairing heap."""
+        super().__init__()
+        self._root = None
+
+    def min(self):
+        if self._root is None:
+            raise nx.NetworkXError("heap is empty.")
+        return (self._root.key, self._root.value)
+
+    def pop(self):
+        if self._root is None:
+            raise nx.NetworkXError("heap is empty.")
+        min_node = self._root
+        self._root = self._merge_children(self._root)
+        del self._dict[min_node.key]
+        return (min_node.key, min_node.value)
+
+    def get(self, key, default=None):
+        node = self._dict.get(key)
+        return node.value if node is not None else default
+
+    def insert(self, key, value, allow_increase=False):
+        node = self._dict.get(key)
+        root = self._root
+        if node is not None:
+            if value < node.value:
+                node.value = value
+                if node is not root and value < node.parent.value:
+                    self._cut(node)
+                    self._root = self._link(root, node)
+                return True
+            elif allow_increase and value > node.value:
+                node.value = value
+                child = self._merge_children(node)
+                # Nonstandard step: Link the merged subtree with the root. See
+                # below for the standard step.
+                if child is not None:
+                    self._root = self._link(self._root, child)
+                # Standard step: Perform a decrease followed by a pop as if the
+                # value were the smallest in the heap. Then insert the new
+                # value into the heap.
+                # if node is not root:
+                #     self._cut(node)
+                #     if child is not None:
+                #         root = self._link(root, child)
+                #     self._root = self._link(root, node)
+                # else:
+                #     self._root = (self._link(node, child)
+                #                   if child is not None else node)
+            return False
+        else:
+            # Insert a new key.
+            node = self._Node(key, value)
+            self._dict[key] = node
+            self._root = self._link(root, node) if root is not None else node
+            return True
+
+    def _link(self, root, other):
+        """Link two nodes, making the one with the smaller value the parent of
+        the other.
+        """
+        if other.value < root.value:
+            root, other = other, root
+        next = root.left
+        other.next = next
+        if next is not None:
+            next.prev = other
+        other.prev = None
+        root.left = other
+        other.parent = root
+        return root
+
+    def _merge_children(self, root):
+        """Merge the subtrees of the root using the standard two-pass method.
+        The resulting subtree is detached from the root.
+        """
+        node = root.left
+        root.left = None
+        if node is not None:
+            link = self._link
+            # Pass 1: Merge pairs of consecutive subtrees from left to right.
+            # At the end of the pass, only the prev pointers of the resulting
+            # subtrees have meaningful values. The other pointers will be fixed
+            # in pass 2.
+            prev = None
+            while True:
+                next = node.next
+                if next is None:
+                    node.prev = prev
+                    break
+                next_next = next.next
+                node = link(node, next)
+                node.prev = prev
+                prev = node
+                if next_next is None:
+                    break
+                node = next_next
+            # Pass 2: Successively merge the subtrees produced by pass 1 from
+            # right to left with the rightmost one.
+            prev = node.prev
+            while prev is not None:
+                prev_prev = prev.prev
+                node = link(prev, node)
+                prev = prev_prev
+            # Now node can become the new root. Its has no parent nor siblings.
+            node.prev = None
+            node.next = None
+            node.parent = None
+        return node
+
+    def _cut(self, node):
+        """Cut a node from its parent."""
+        prev = node.prev
+        next = node.next
+        if prev is not None:
+            prev.next = next
+        else:
+            node.parent.left = next
+        node.prev = None
+        if next is not None:
+            next.prev = prev
+            node.next = None
+        node.parent = None
+
+
+class BinaryHeap(MinHeap):
+    """A binary heap."""
+
+    def __init__(self):
+        """Initialize a binary heap."""
+        super().__init__()
+        self._heap = []
+        self._count = count()
+
+    def min(self):
+        dict = self._dict
+        if not dict:
+            raise nx.NetworkXError("heap is empty")
+        heap = self._heap
+        # Repeatedly remove stale key-value pairs until a up-to-date one is
+        # met.
+        while True:
+            value, _, key = heap[0]
+            if key in dict and value == dict[key]:
+                break
+            heappop(heap)
+        return (key, value)
+
+    def pop(self):
+        dict = self._dict
+        if not dict:
+            raise nx.NetworkXError("heap is empty")
+        heap = self._heap
+        # Repeatedly remove stale key-value pairs until a up-to-date one is
+        # met.
+        while True:
+            value, _, key = heap[0]
+            heappop(heap)
+            if key in dict and value == dict[key]:
+                break
+        del dict[key]
+        return (key, value)
+
+    def get(self, key, default=None):
+        return self._dict.get(key, default)
+
+    def insert(self, key, value, allow_increase=False):
+        dict = self._dict
+        if key in dict:
+            old_value = dict[key]
+            if value < old_value or (allow_increase and value > old_value):
+                # Since there is no way to efficiently obtain the location of a
+                # key-value pair in the heap, insert a new pair even if ones
+                # with the same key may already be present. Deem the old ones
+                # as stale and skip them when the minimum pair is queried.
+                dict[key] = value
+                heappush(self._heap, (value, next(self._count), key))
+                return value < old_value
+            return False
+        else:
+            dict[key] = value
+            heappush(self._heap, (value, next(self._count), key))
+            return True
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/mapped_queue.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/mapped_queue.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dcea368a93873fd72195fc8d388891c129942e0
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/mapped_queue.py
@@ -0,0 +1,297 @@
+"""Priority queue class with updatable priorities."""
+
+import heapq
+
+__all__ = ["MappedQueue"]
+
+
+class _HeapElement:
+    """This proxy class separates the heap element from its priority.
+
+    The idea is that using a 2-tuple (priority, element) works
+    for sorting, but not for dict lookup because priorities are
+    often floating point values so round-off can mess up equality.
+
+    So, we need inequalities to look at the priority (for sorting)
+    and equality (and hash) to look at the element to enable
+    updates to the priority.
+
+    Unfortunately, this class can be tricky to work with if you forget that
+    `__lt__` compares the priority while `__eq__` compares the element.
+    In `greedy_modularity_communities()` the following code is
+    used to check that two _HeapElements differ in either element or priority:
+
+        if d_oldmax != row_max or d_oldmax.priority != row_max.priority:
+
+    If the priorities are the same, this implementation uses the element
+    as a tiebreaker. This provides compatibility with older systems that
+    use tuples to combine priority and elements.
+    """
+
+    __slots__ = ["priority", "element", "_hash"]
+
+    def __init__(self, priority, element):
+        self.priority = priority
+        self.element = element
+        self._hash = hash(element)
+
+    def __lt__(self, other):
+        try:
+            other_priority = other.priority
+        except AttributeError:
+            return self.priority < other
+        # assume comparing to another _HeapElement
+        if self.priority == other_priority:
+            try:
+                return self.element < other.element
+            except TypeError as err:
+                raise TypeError(
+                    "Consider using a tuple, with a priority value that can be compared."
+                )
+        return self.priority < other_priority
+
+    def __gt__(self, other):
+        try:
+            other_priority = other.priority
+        except AttributeError:
+            return self.priority > other
+        # assume comparing to another _HeapElement
+        if self.priority == other_priority:
+            try:
+                return self.element > other.element
+            except TypeError as err:
+                raise TypeError(
+                    "Consider using a tuple, with a priority value that can be compared."
+                )
+        return self.priority > other_priority
+
+    def __eq__(self, other):
+        try:
+            return self.element == other.element
+        except AttributeError:
+            return self.element == other
+
+    def __hash__(self):
+        return self._hash
+
+    def __getitem__(self, indx):
+        return self.priority if indx == 0 else self.element[indx - 1]
+
+    def __iter__(self):
+        yield self.priority
+        try:
+            yield from self.element
+        except TypeError:
+            yield self.element
+
+    def __repr__(self):
+        return f"_HeapElement({self.priority}, {self.element})"
+
+
+class MappedQueue:
+    """The MappedQueue class implements a min-heap with removal and update-priority.
+
+    The min heap uses heapq as well as custom written _siftup and _siftdown
+    methods to allow the heap positions to be tracked by an additional dict
+    keyed by element to position. The smallest element can be popped in O(1) time,
+    new elements can be pushed in O(log n) time, and any element can be removed
+    or updated in O(log n) time. The queue cannot contain duplicate elements
+    and an attempt to push an element already in the queue will have no effect.
+
+    MappedQueue complements the heapq package from the python standard
+    library. While MappedQueue is designed for maximum compatibility with
+    heapq, it adds element removal, lookup, and priority update.
+
+    Parameters
+    ----------
+    data : dict or iterable
+
+    Examples
+    --------
+
+    A `MappedQueue` can be created empty, or optionally, given a dictionary
+    of initial elements and priorities.  The methods `push`, `pop`,
+    `remove`, and `update` operate on the queue.
+
+    >>> colors_nm = {"red": 665, "blue": 470, "green": 550}
+    >>> q = MappedQueue(colors_nm)
+    >>> q.remove("red")
+    >>> q.update("green", "violet", 400)
+    >>> q.push("indigo", 425)
+    True
+    >>> [q.pop().element for i in range(len(q.heap))]
+    ['violet', 'indigo', 'blue']
+
+    A `MappedQueue` can also be initialized with a list or other iterable. The priority is assumed
+    to be the sort order of the items in the list.
+
+    >>> q = MappedQueue([916, 50, 4609, 493, 237])
+    >>> q.remove(493)
+    >>> q.update(237, 1117)
+    >>> [q.pop() for i in range(len(q.heap))]
+    [50, 916, 1117, 4609]
+
+    An exception is raised if the elements are not comparable.
+
+    >>> q = MappedQueue([100, "a"])
+    Traceback (most recent call last):
+    ...
+    TypeError: '<' not supported between instances of 'int' and 'str'
+
+    To avoid the exception, use a dictionary to assign priorities to the elements.
+
+    >>> q = MappedQueue({100: 0, "a": 1})
+
+    References
+    ----------
+    .. [1] Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001).
+       Introduction to algorithms second edition.
+    .. [2] Knuth, D. E. (1997). The art of computer programming (Vol. 3).
+       Pearson Education.
+    """
+
+    def __init__(self, data=None):
+        """Priority queue class with updatable priorities."""
+        if data is None:
+            self.heap = []
+        elif isinstance(data, dict):
+            self.heap = [_HeapElement(v, k) for k, v in data.items()]
+        else:
+            self.heap = list(data)
+        self.position = {}
+        self._heapify()
+
+    def _heapify(self):
+        """Restore heap invariant and recalculate map."""
+        heapq.heapify(self.heap)
+        self.position = {elt: pos for pos, elt in enumerate(self.heap)}
+        if len(self.heap) != len(self.position):
+            raise AssertionError("Heap contains duplicate elements")
+
+    def __len__(self):
+        return len(self.heap)
+
+    def push(self, elt, priority=None):
+        """Add an element to the queue."""
+        if priority is not None:
+            elt = _HeapElement(priority, elt)
+        # If element is already in queue, do nothing
+        if elt in self.position:
+            return False
+        # Add element to heap and dict
+        pos = len(self.heap)
+        self.heap.append(elt)
+        self.position[elt] = pos
+        # Restore invariant by sifting down
+        self._siftdown(0, pos)
+        return True
+
+    def pop(self):
+        """Remove and return the smallest element in the queue."""
+        # Remove smallest element
+        elt = self.heap[0]
+        del self.position[elt]
+        # If elt is last item, remove and return
+        if len(self.heap) == 1:
+            self.heap.pop()
+            return elt
+        # Replace root with last element
+        last = self.heap.pop()
+        self.heap[0] = last
+        self.position[last] = 0
+        # Restore invariant by sifting up
+        self._siftup(0)
+        # Return smallest element
+        return elt
+
+    def update(self, elt, new, priority=None):
+        """Replace an element in the queue with a new one."""
+        if priority is not None:
+            new = _HeapElement(priority, new)
+        # Replace
+        pos = self.position[elt]
+        self.heap[pos] = new
+        del self.position[elt]
+        self.position[new] = pos
+        # Restore invariant by sifting up
+        self._siftup(pos)
+
+    def remove(self, elt):
+        """Remove an element from the queue."""
+        # Find and remove element
+        try:
+            pos = self.position[elt]
+            del self.position[elt]
+        except KeyError:
+            # Not in queue
+            raise
+        # If elt is last item, remove and return
+        if pos == len(self.heap) - 1:
+            self.heap.pop()
+            return
+        # Replace elt with last element
+        last = self.heap.pop()
+        self.heap[pos] = last
+        self.position[last] = pos
+        # Restore invariant by sifting up
+        self._siftup(pos)
+
+    def _siftup(self, pos):
+        """Move smaller child up until hitting a leaf.
+
+        Built to mimic code for heapq._siftup
+        only updating position dict too.
+        """
+        heap, position = self.heap, self.position
+        end_pos = len(heap)
+        startpos = pos
+        newitem = heap[pos]
+        # Shift up the smaller child until hitting a leaf
+        child_pos = (pos << 1) + 1  # start with leftmost child position
+        while child_pos < end_pos:
+            # Set child_pos to index of smaller child.
+            child = heap[child_pos]
+            right_pos = child_pos + 1
+            if right_pos < end_pos:
+                right = heap[right_pos]
+                if not child < right:
+                    child = right
+                    child_pos = right_pos
+            # Move the smaller child up.
+            heap[pos] = child
+            position[child] = pos
+            pos = child_pos
+            child_pos = (pos << 1) + 1
+        # pos is a leaf position. Put newitem there, and bubble it up
+        # to its final resting place (by sifting its parents down).
+        while pos > 0:
+            parent_pos = (pos - 1) >> 1
+            parent = heap[parent_pos]
+            if not newitem < parent:
+                break
+            heap[pos] = parent
+            position[parent] = pos
+            pos = parent_pos
+        heap[pos] = newitem
+        position[newitem] = pos
+
+    def _siftdown(self, start_pos, pos):
+        """Restore invariant. keep swapping with parent until smaller.
+
+        Built to mimic code for heapq._siftdown
+        only updating position dict too.
+        """
+        heap, position = self.heap, self.position
+        newitem = heap[pos]
+        # Follow the path to the root, moving parents down until finding a place
+        # newitem fits.
+        while pos > start_pos:
+            parent_pos = (pos - 1) >> 1
+            parent = heap[parent_pos]
+            if not newitem < parent:
+                break
+            heap[pos] = parent
+            position[parent] = pos
+            pos = parent_pos
+        heap[pos] = newitem
+        position[newitem] = pos
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/misc.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..73b50656b38b8dc1c7680e3a8db743c3a3fc1aed
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/misc.py
@@ -0,0 +1,651 @@
+"""
+Miscellaneous Helpers for NetworkX.
+
+These are not imported into the base networkx namespace but
+can be accessed, for example, as
+
+>>> import networkx as nx
+>>> nx.utils.make_list_of_ints({1, 2, 3})
+[1, 2, 3]
+>>> nx.utils.arbitrary_element({5, 1, 7})  # doctest: +SKIP
+1
+"""
+
+import random
+import warnings
+from collections import defaultdict
+from collections.abc import Iterable, Iterator, Sized
+from itertools import chain, tee
+
+import networkx as nx
+
+__all__ = [
+    "flatten",
+    "make_list_of_ints",
+    "dict_to_numpy_array",
+    "arbitrary_element",
+    "pairwise",
+    "groups",
+    "create_random_state",
+    "create_py_random_state",
+    "PythonRandomInterface",
+    "PythonRandomViaNumpyBits",
+    "nodes_equal",
+    "edges_equal",
+    "graphs_equal",
+    "_clear_cache",
+]
+
+
+# some cookbook stuff
+# used in deciding whether something is a bunch of nodes, edges, etc.
+# see G.add_nodes and others in Graph Class in networkx/base.py
+
+
+def flatten(obj, result=None):
+    """Return flattened version of (possibly nested) iterable object."""
+    if not isinstance(obj, Iterable | Sized) or isinstance(obj, str):
+        return obj
+    if result is None:
+        result = []
+    for item in obj:
+        if not isinstance(item, Iterable | Sized) or isinstance(item, str):
+            result.append(item)
+        else:
+            flatten(item, result)
+    return tuple(result)
+
+
+def make_list_of_ints(sequence):
+    """Return list of ints from sequence of integral numbers.
+
+    All elements of the sequence must satisfy int(element) == element
+    or a ValueError is raised. Sequence is iterated through once.
+
+    If sequence is a list, the non-int values are replaced with ints.
+    So, no new list is created
+    """
+    if not isinstance(sequence, list):
+        result = []
+        for i in sequence:
+            errmsg = f"sequence is not all integers: {i}"
+            try:
+                ii = int(i)
+            except ValueError:
+                raise nx.NetworkXError(errmsg) from None
+            if ii != i:
+                raise nx.NetworkXError(errmsg)
+            result.append(ii)
+        return result
+    # original sequence is a list... in-place conversion to ints
+    for indx, i in enumerate(sequence):
+        errmsg = f"sequence is not all integers: {i}"
+        if isinstance(i, int):
+            continue
+        try:
+            ii = int(i)
+        except ValueError:
+            raise nx.NetworkXError(errmsg) from None
+        if ii != i:
+            raise nx.NetworkXError(errmsg)
+        sequence[indx] = ii
+    return sequence
+
+
+def dict_to_numpy_array(d, mapping=None):
+    """Convert a dictionary of dictionaries to a numpy array
+    with optional mapping."""
+    try:
+        return _dict_to_numpy_array2(d, mapping)
+    except (AttributeError, TypeError):
+        # AttributeError is when no mapping was provided and v.keys() fails.
+        # TypeError is when a mapping was provided and d[k1][k2] fails.
+        return _dict_to_numpy_array1(d, mapping)
+
+
+def _dict_to_numpy_array2(d, mapping=None):
+    """Convert a dictionary of dictionaries to a 2d numpy array
+    with optional mapping.
+
+    """
+    import numpy as np
+
+    if mapping is None:
+        s = set(d.keys())
+        for k, v in d.items():
+            s.update(v.keys())
+        mapping = dict(zip(s, range(len(s))))
+    n = len(mapping)
+    a = np.zeros((n, n))
+    for k1, i in mapping.items():
+        for k2, j in mapping.items():
+            try:
+                a[i, j] = d[k1][k2]
+            except KeyError:
+                pass
+    return a
+
+
+def _dict_to_numpy_array1(d, mapping=None):
+    """Convert a dictionary of numbers to a 1d numpy array with optional mapping."""
+    import numpy as np
+
+    if mapping is None:
+        s = set(d.keys())
+        mapping = dict(zip(s, range(len(s))))
+    n = len(mapping)
+    a = np.zeros(n)
+    for k1, i in mapping.items():
+        i = mapping[k1]
+        a[i] = d[k1]
+    return a
+
+
+def arbitrary_element(iterable):
+    """Returns an arbitrary element of `iterable` without removing it.
+
+    This is most useful for "peeking" at an arbitrary element of a set,
+    but can be used for any list, dictionary, etc., as well.
+
+    Parameters
+    ----------
+    iterable : `abc.collections.Iterable` instance
+        Any object that implements ``__iter__``, e.g. set, dict, list, tuple,
+        etc.
+
+    Returns
+    -------
+    The object that results from ``next(iter(iterable))``
+
+    Raises
+    ------
+    ValueError
+        If `iterable` is an iterator (because the current implementation of
+        this function would consume an element from the iterator).
+
+    Examples
+    --------
+    Arbitrary elements from common Iterable objects:
+
+    >>> nx.utils.arbitrary_element([1, 2, 3])  # list
+    1
+    >>> nx.utils.arbitrary_element((1, 2, 3))  # tuple
+    1
+    >>> nx.utils.arbitrary_element({1, 2, 3})  # set
+    1
+    >>> d = {k: v for k, v in zip([1, 2, 3], [3, 2, 1])}
+    >>> nx.utils.arbitrary_element(d)  # dict_keys
+    1
+    >>> nx.utils.arbitrary_element(d.values())  # dict values
+    3
+
+    `str` is also an Iterable:
+
+    >>> nx.utils.arbitrary_element("hello")
+    'h'
+
+    :exc:`ValueError` is raised if `iterable` is an iterator:
+
+    >>> iterator = iter([1, 2, 3])  # Iterator, *not* Iterable
+    >>> nx.utils.arbitrary_element(iterator)
+    Traceback (most recent call last):
+        ...
+    ValueError: cannot return an arbitrary item from an iterator
+
+    Notes
+    -----
+    This function does not return a *random* element. If `iterable` is
+    ordered, sequential calls will return the same value::
+
+        >>> l = [1, 2, 3]
+        >>> nx.utils.arbitrary_element(l)
+        1
+        >>> nx.utils.arbitrary_element(l)
+        1
+
+    """
+    if isinstance(iterable, Iterator):
+        raise ValueError("cannot return an arbitrary item from an iterator")
+    # Another possible implementation is ``for x in iterable: return x``.
+    return next(iter(iterable))
+
+
+# Recipe from the itertools documentation.
+def pairwise(iterable, cyclic=False):
+    "s -> (s0, s1), (s1, s2), (s2, s3), ..."
+    a, b = tee(iterable)
+    first = next(b, None)
+    if cyclic is True:
+        return zip(a, chain(b, (first,)))
+    return zip(a, b)
+
+
+def groups(many_to_one):
+    """Converts a many-to-one mapping into a one-to-many mapping.
+
+    `many_to_one` must be a dictionary whose keys and values are all
+    :term:`hashable`.
+
+    The return value is a dictionary mapping values from `many_to_one`
+    to sets of keys from `many_to_one` that have that value.
+
+    Examples
+    --------
+    >>> from networkx.utils import groups
+    >>> many_to_one = {"a": 1, "b": 1, "c": 2, "d": 3, "e": 3}
+    >>> groups(many_to_one)  # doctest: +SKIP
+    {1: {'a', 'b'}, 2: {'c'}, 3: {'e', 'd'}}
+    """
+    one_to_many = defaultdict(set)
+    for v, k in many_to_one.items():
+        one_to_many[k].add(v)
+    return dict(one_to_many)
+
+
+def create_random_state(random_state=None):
+    """Returns a numpy.random.RandomState or numpy.random.Generator instance
+    depending on input.
+
+    Parameters
+    ----------
+    random_state : int or NumPy RandomState or Generator instance, optional (default=None)
+        If int, return a numpy.random.RandomState instance set with seed=int.
+        if `numpy.random.RandomState` instance, return it.
+        if `numpy.random.Generator` instance, return it.
+        if None or numpy.random, return the global random number generator used
+        by numpy.random.
+    """
+    import numpy as np
+
+    if random_state is None or random_state is np.random:
+        return np.random.mtrand._rand
+    if isinstance(random_state, np.random.RandomState):
+        return random_state
+    if isinstance(random_state, int):
+        return np.random.RandomState(random_state)
+    if isinstance(random_state, np.random.Generator):
+        return random_state
+    msg = (
+        f"{random_state} cannot be used to create a numpy.random.RandomState or\n"
+        "numpy.random.Generator instance"
+    )
+    raise ValueError(msg)
+
+
+class PythonRandomViaNumpyBits(random.Random):
+    """Provide the random.random algorithms using a numpy.random bit generator
+
+    The intent is to allow people to contribute code that uses Python's random
+    library, but still allow users to provide a single easily controlled random
+    bit-stream for all work with NetworkX. This implementation is based on helpful
+    comments and code from Robert Kern on NumPy's GitHub Issue #24458.
+
+    This implementation supersedes that of `PythonRandomInterface` which rewrote
+    methods to account for subtle differences in API between `random` and
+    `numpy.random`. Instead this subclasses `random.Random` and overwrites
+    the methods `random`, `getrandbits`, `getstate`, `setstate` and `seed`.
+    It makes them use the rng values from an input numpy `RandomState` or `Generator`.
+    Those few methods allow the rest of the `random.Random` methods to provide
+    the API interface of `random.random` while using randomness generated by
+    a numpy generator.
+    """
+
+    def __init__(self, rng=None):
+        try:
+            import numpy as np
+        except ImportError:
+            msg = "numpy not found, only random.random available."
+            warnings.warn(msg, ImportWarning)
+
+        if rng is None:
+            self._rng = np.random.mtrand._rand
+        else:
+            self._rng = rng
+
+        # Not necessary, given our overriding of gauss() below, but it's
+        # in the superclass and nominally public, so initialize it here.
+        self.gauss_next = None
+
+    def random(self):
+        """Get the next random number in the range 0.0 <= X < 1.0."""
+        return self._rng.random()
+
+    def getrandbits(self, k):
+        """getrandbits(k) -> x.  Generates an int with k random bits."""
+        if k < 0:
+            raise ValueError("number of bits must be non-negative")
+        numbytes = (k + 7) // 8  # bits / 8 and rounded up
+        x = int.from_bytes(self._rng.bytes(numbytes), "big")
+        return x >> (numbytes * 8 - k)  # trim excess bits
+
+    def getstate(self):
+        return self._rng.__getstate__()
+
+    def setstate(self, state):
+        self._rng.__setstate__(state)
+
+    def seed(self, *args, **kwds):
+        "Do nothing override method."
+        raise NotImplementedError("seed() not implemented in PythonRandomViaNumpyBits")
+
+
+##################################################################
+class PythonRandomInterface:
+    """PythonRandomInterface is included for backward compatibility
+    New code should use PythonRandomViaNumpyBits instead.
+    """
+
+    def __init__(self, rng=None):
+        try:
+            import numpy as np
+        except ImportError:
+            msg = "numpy not found, only random.random available."
+            warnings.warn(msg, ImportWarning)
+
+        if rng is None:
+            self._rng = np.random.mtrand._rand
+        else:
+            self._rng = rng
+
+    def random(self):
+        return self._rng.random()
+
+    def uniform(self, a, b):
+        return a + (b - a) * self._rng.random()
+
+    def randrange(self, a, b=None):
+        import numpy as np
+
+        if b is None:
+            a, b = 0, a
+        if b > 9223372036854775807:  # from np.iinfo(np.int64).max
+            tmp_rng = PythonRandomViaNumpyBits(self._rng)
+            return tmp_rng.randrange(a, b)
+
+        if isinstance(self._rng, np.random.Generator):
+            return self._rng.integers(a, b)
+        return self._rng.randint(a, b)
+
+    # NOTE: the numpy implementations of `choice` don't support strings, so
+    # this cannot be replaced with self._rng.choice
+    def choice(self, seq):
+        import numpy as np
+
+        if isinstance(self._rng, np.random.Generator):
+            idx = self._rng.integers(0, len(seq))
+        else:
+            idx = self._rng.randint(0, len(seq))
+        return seq[idx]
+
+    def gauss(self, mu, sigma):
+        return self._rng.normal(mu, sigma)
+
+    def shuffle(self, seq):
+        return self._rng.shuffle(seq)
+
+    #    Some methods don't match API for numpy RandomState.
+    #    Commented out versions are not used by NetworkX
+
+    def sample(self, seq, k):
+        return self._rng.choice(list(seq), size=(k,), replace=False)
+
+    def randint(self, a, b):
+        import numpy as np
+
+        if b > 9223372036854775807:  # from np.iinfo(np.int64).max
+            tmp_rng = PythonRandomViaNumpyBits(self._rng)
+            return tmp_rng.randint(a, b)
+
+        if isinstance(self._rng, np.random.Generator):
+            return self._rng.integers(a, b + 1)
+        return self._rng.randint(a, b + 1)
+
+    #    exponential as expovariate with 1/argument,
+    def expovariate(self, scale):
+        return self._rng.exponential(1 / scale)
+
+    #    pareto as paretovariate with 1/argument,
+    def paretovariate(self, shape):
+        return self._rng.pareto(shape)
+
+
+#    weibull as weibullvariate multiplied by beta,
+#    def weibullvariate(self, alpha, beta):
+#        return self._rng.weibull(alpha) * beta
+#
+#    def triangular(self, low, high, mode):
+#        return self._rng.triangular(low, mode, high)
+#
+#    def choices(self, seq, weights=None, cum_weights=None, k=1):
+#        return self._rng.choice(seq
+
+
+def create_py_random_state(random_state=None):
+    """Returns a random.Random instance depending on input.
+
+    Parameters
+    ----------
+    random_state : int or random number generator or None (default=None)
+        - If int, return a `random.Random` instance set with seed=int.
+        - If `random.Random` instance, return it.
+        - If None or the `np.random` package, return the global random number
+          generator used by `np.random`.
+        - If an `np.random.Generator` instance, or the `np.random` package, or
+          the global numpy random number generator, then return it.
+          wrapped in a `PythonRandomViaNumpyBits` class.
+        - If a `PythonRandomViaNumpyBits` instance, return it.
+        - If a `PythonRandomInterface` instance, return it.
+        - If a `np.random.RandomState` instance and not the global numpy default,
+          return it wrapped in `PythonRandomInterface` for backward bit-stream
+          matching with legacy code.
+
+    Notes
+    -----
+    - A diagram intending to illustrate the relationships behind our support
+      for numpy random numbers is called
+      `NetworkX Numpy Random Numbers <https://excalidraw.com/#room=b5303f2b03d3af7ccc6a,e5ZDIWdWWCTTsg8OqoRvPA>`_.
+    - More discussion about this support also appears in
+      `gh-6869#comment <https://github.com/networkx/networkx/pull/6869#issuecomment-1944799534>`_.
+    - Wrappers of numpy.random number generators allow them to mimic the Python random
+      number generation algorithms. For example, Python can create arbitrarily large
+      random ints, and the wrappers use Numpy bit-streams with CPython's random module
+      to choose arbitrarily large random integers too.
+    - We provide two wrapper classes:
+      `PythonRandomViaNumpyBits` is usually what you want and is always used for
+      `np.Generator` instances. But for users who need to recreate random numbers
+      produced in NetworkX 3.2 or earlier, we maintain the `PythonRandomInterface`
+      wrapper as well. We use it only used if passed a (non-default) `np.RandomState`
+      instance pre-initialized from a seed. Otherwise the newer wrapper is used.
+    """
+    if random_state is None or random_state is random:
+        return random._inst
+    if isinstance(random_state, random.Random):
+        return random_state
+    if isinstance(random_state, int):
+        return random.Random(random_state)
+
+    try:
+        import numpy as np
+    except ImportError:
+        pass
+    else:
+        if isinstance(random_state, PythonRandomInterface | PythonRandomViaNumpyBits):
+            return random_state
+        if isinstance(random_state, np.random.Generator):
+            return PythonRandomViaNumpyBits(random_state)
+        if random_state is np.random:
+            return PythonRandomViaNumpyBits(np.random.mtrand._rand)
+
+        if isinstance(random_state, np.random.RandomState):
+            if random_state is np.random.mtrand._rand:
+                return PythonRandomViaNumpyBits(random_state)
+            # Only need older interface if specially constructed RandomState used
+            return PythonRandomInterface(random_state)
+
+    msg = f"{random_state} cannot be used to generate a random.Random instance"
+    raise ValueError(msg)
+
+
+def nodes_equal(nodes1, nodes2):
+    """Check if nodes are equal.
+
+    Equality here means equal as Python objects.
+    Node data must match if included.
+    The order of nodes is not relevant.
+
+    Parameters
+    ----------
+    nodes1, nodes2 : iterables of nodes, or (node, datadict) tuples
+
+    Returns
+    -------
+    bool
+        True if nodes are equal, False otherwise.
+    """
+    nlist1 = list(nodes1)
+    nlist2 = list(nodes2)
+    try:
+        d1 = dict(nlist1)
+        d2 = dict(nlist2)
+    except (ValueError, TypeError):
+        d1 = dict.fromkeys(nlist1)
+        d2 = dict.fromkeys(nlist2)
+    return d1 == d2
+
+
+def edges_equal(edges1, edges2):
+    """Check if edges are equal.
+
+    Equality here means equal as Python objects.
+    Edge data must match if included.
+    The order of the edges is not relevant.
+
+    Parameters
+    ----------
+    edges1, edges2 : iterables of with u, v nodes as
+        edge tuples (u, v), or
+        edge tuples with data dicts (u, v, d), or
+        edge tuples with keys and data dicts (u, v, k, d)
+
+    Returns
+    -------
+    bool
+        True if edges are equal, False otherwise.
+    """
+    from collections import defaultdict
+
+    d1 = defaultdict(dict)
+    d2 = defaultdict(dict)
+    c1 = 0
+    for c1, e in enumerate(edges1):
+        u, v = e[0], e[1]
+        data = [e[2:]]
+        if v in d1[u]:
+            data = d1[u][v] + data
+        d1[u][v] = data
+        d1[v][u] = data
+    c2 = 0
+    for c2, e in enumerate(edges2):
+        u, v = e[0], e[1]
+        data = [e[2:]]
+        if v in d2[u]:
+            data = d2[u][v] + data
+        d2[u][v] = data
+        d2[v][u] = data
+    if c1 != c2:
+        return False
+    # can check one direction because lengths are the same.
+    for n, nbrdict in d1.items():
+        for nbr, datalist in nbrdict.items():
+            if n not in d2:
+                return False
+            if nbr not in d2[n]:
+                return False
+            d2datalist = d2[n][nbr]
+            for data in datalist:
+                if datalist.count(data) != d2datalist.count(data):
+                    return False
+    return True
+
+
+def graphs_equal(graph1, graph2):
+    """Check if graphs are equal.
+
+    Equality here means equal as Python objects (not isomorphism).
+    Node, edge and graph data must match.
+
+    Parameters
+    ----------
+    graph1, graph2 : graph
+
+    Returns
+    -------
+    bool
+        True if graphs are equal, False otherwise.
+    """
+    return (
+        graph1.adj == graph2.adj
+        and graph1.nodes == graph2.nodes
+        and graph1.graph == graph2.graph
+    )
+
+
+def _clear_cache(G):
+    """Clear the cache of a graph (currently stores converted graphs).
+
+    Caching is controlled via ``nx.config.cache_converted_graphs`` configuration.
+    """
+    if cache := getattr(G, "__networkx_cache__", None):
+        cache.clear()
+
+
+def check_create_using(create_using, *, directed=None, multigraph=None, default=None):
+    """Assert that create_using has good properties
+
+    This checks for desired directedness and multi-edge properties.
+    It returns `create_using` unless that is `None` when it returns
+    the optionally specified default value.
+
+    Parameters
+    ----------
+    create_using : None, graph class or instance
+        The input value of create_using for a function.
+    directed : None or bool
+        Whether to check `create_using.is_directed() == directed`.
+        If None, do not assert directedness.
+    multigraph : None or bool
+        Whether to check `create_using.is_multigraph() == multigraph`.
+        If None, do not assert multi-edge property.
+    default : None or graph class
+        The graph class to return if create_using is None.
+
+    Returns
+    -------
+    create_using : graph class or instance
+        The provided graph class or instance, or if None, the `default` value.
+
+    Raises
+    ------
+    NetworkXError
+        When `create_using` doesn't match the properties specified by `directed`
+        or `multigraph` parameters.
+    """
+    if default is None:
+        default = nx.Graph
+    G = create_using if create_using is not None else default
+
+    G_directed = G.is_directed(None) if isinstance(G, type) else G.is_directed()
+    G_multigraph = G.is_multigraph(None) if isinstance(G, type) else G.is_multigraph()
+
+    if directed is not None:
+        if directed and not G_directed:
+            raise nx.NetworkXError("create_using must be directed")
+        if not directed and G_directed:
+            raise nx.NetworkXError("create_using must not be directed")
+
+    if multigraph is not None:
+        if multigraph and not G_multigraph:
+            raise nx.NetworkXError("create_using must be a multi-graph")
+        if not multigraph and G_multigraph:
+            raise nx.NetworkXError("create_using must not be a multi-graph")
+    return G
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/random_sequence.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/random_sequence.py
new file mode 100644
index 0000000000000000000000000000000000000000..20a7b5e0a7fcc426ed9840f8bed2abf500e357e5
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/random_sequence.py
@@ -0,0 +1,164 @@
+"""
+Utilities for generating random numbers, random sequences, and
+random selections.
+"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "powerlaw_sequence",
+    "zipf_rv",
+    "cumulative_distribution",
+    "discrete_sequence",
+    "random_weighted_sample",
+    "weighted_choice",
+]
+
+
+# The same helpers for choosing random sequences from distributions
+# uses Python's random module
+# https://docs.python.org/3/library/random.html
+
+
+@py_random_state(2)
+def powerlaw_sequence(n, exponent=2.0, seed=None):
+    """
+    Return sample sequence of length n from a power law distribution.
+    """
+    return [seed.paretovariate(exponent - 1) for i in range(n)]
+
+
+@py_random_state(2)
+def zipf_rv(alpha, xmin=1, seed=None):
+    r"""Returns a random value chosen from the Zipf distribution.
+
+    The return value is an integer drawn from the probability distribution
+
+    .. math::
+
+        p(x)=\frac{x^{-\alpha}}{\zeta(\alpha, x_{\min})},
+
+    where $\zeta(\alpha, x_{\min})$ is the Hurwitz zeta function.
+
+    Parameters
+    ----------
+    alpha : float
+      Exponent value of the distribution
+    xmin : int
+      Minimum value
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    x : int
+      Random value from Zipf distribution
+
+    Raises
+    ------
+    ValueError:
+      If xmin < 1 or
+      If alpha <= 1
+
+    Notes
+    -----
+    The rejection algorithm generates random values for a the power-law
+    distribution in uniformly bounded expected time dependent on
+    parameters.  See [1]_ for details on its operation.
+
+    Examples
+    --------
+    >>> nx.utils.zipf_rv(alpha=2, xmin=3, seed=42)
+    8
+
+    References
+    ----------
+    .. [1] Luc Devroye, Non-Uniform Random Variate Generation,
+       Springer-Verlag, New York, 1986.
+    """
+    if xmin < 1:
+        raise ValueError("xmin < 1")
+    if alpha <= 1:
+        raise ValueError("a <= 1.0")
+    a1 = alpha - 1.0
+    b = 2**a1
+    while True:
+        u = 1.0 - seed.random()  # u in (0,1]
+        v = seed.random()  # v in [0,1)
+        x = int(xmin * u ** -(1.0 / a1))
+        t = (1.0 + (1.0 / x)) ** a1
+        if v * x * (t - 1.0) / (b - 1.0) <= t / b:
+            break
+    return x
+
+
+def cumulative_distribution(distribution):
+    """Returns normalized cumulative distribution from discrete distribution."""
+
+    cdf = [0.0]
+    psum = sum(distribution)
+    for i in range(len(distribution)):
+        cdf.append(cdf[i] + distribution[i] / psum)
+    return cdf
+
+
+@py_random_state(3)
+def discrete_sequence(n, distribution=None, cdistribution=None, seed=None):
+    """
+    Return sample sequence of length n from a given discrete distribution
+    or discrete cumulative distribution.
+
+    One of the following must be specified.
+
+    distribution = histogram of values, will be normalized
+
+    cdistribution = normalized discrete cumulative distribution
+
+    """
+    import bisect
+
+    if cdistribution is not None:
+        cdf = cdistribution
+    elif distribution is not None:
+        cdf = cumulative_distribution(distribution)
+    else:
+        raise nx.NetworkXError(
+            "discrete_sequence: distribution or cdistribution missing"
+        )
+
+    # get a uniform random number
+    inputseq = [seed.random() for i in range(n)]
+
+    # choose from CDF
+    seq = [bisect.bisect_left(cdf, s) - 1 for s in inputseq]
+    return seq
+
+
+@py_random_state(2)
+def random_weighted_sample(mapping, k, seed=None):
+    """Returns k items without replacement from a weighted sample.
+
+    The input is a dictionary of items with weights as values.
+    """
+    if k > len(mapping):
+        raise ValueError("sample larger than population")
+    sample = set()
+    while len(sample) < k:
+        sample.add(weighted_choice(mapping, seed))
+    return list(sample)
+
+
+@py_random_state(1)
+def weighted_choice(mapping, seed=None):
+    """Returns a single element from a weighted sample.
+
+    The input is a dictionary of items with weights as values.
+    """
+    # use roulette method
+    rnd = seed.random() * sum(mapping.values())
+    for k, w in mapping.items():
+        rnd -= w
+        if rnd < 0:
+            return k
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/rcm.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/rcm.py
new file mode 100644
index 0000000000000000000000000000000000000000..7465c50d5af49095e421c509e36b33f2476ae157
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/rcm.py
@@ -0,0 +1,159 @@
+"""
+Cuthill-McKee ordering of graph nodes to produce sparse matrices
+"""
+
+from collections import deque
+from operator import itemgetter
+
+import networkx as nx
+
+from ..utils import arbitrary_element
+
+__all__ = ["cuthill_mckee_ordering", "reverse_cuthill_mckee_ordering"]
+
+
+def cuthill_mckee_ordering(G, heuristic=None):
+    """Generate an ordering (permutation) of the graph nodes to make
+    a sparse matrix.
+
+    Uses the Cuthill-McKee heuristic (based on breadth-first search) [1]_.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    heuristic : function, optional
+      Function to choose starting node for RCM algorithm.  If None
+      a node from a pseudo-peripheral pair is used.  A user-defined function
+      can be supplied that takes a graph object and returns a single node.
+
+    Returns
+    -------
+    nodes : generator
+       Generator of nodes in Cuthill-McKee ordering.
+
+    Examples
+    --------
+    >>> from networkx.utils import cuthill_mckee_ordering
+    >>> G = nx.path_graph(4)
+    >>> rcm = list(cuthill_mckee_ordering(G))
+    >>> A = nx.adjacency_matrix(G, nodelist=rcm)
+
+    Smallest degree node as heuristic function:
+
+    >>> def smallest_degree(G):
+    ...     return min(G, key=G.degree)
+    >>> rcm = list(cuthill_mckee_ordering(G, heuristic=smallest_degree))
+
+
+    See Also
+    --------
+    reverse_cuthill_mckee_ordering
+
+    Notes
+    -----
+    The optimal solution the bandwidth reduction is NP-complete [2]_.
+
+
+    References
+    ----------
+    .. [1] E. Cuthill and J. McKee.
+       Reducing the bandwidth of sparse symmetric matrices,
+       In Proc. 24th Nat. Conf. ACM, pages 157-172, 1969.
+       http://doi.acm.org/10.1145/800195.805928
+    .. [2]  Steven S. Skiena. 1997. The Algorithm Design Manual.
+       Springer-Verlag New York, Inc., New York, NY, USA.
+    """
+    for c in nx.connected_components(G):
+        yield from connected_cuthill_mckee_ordering(G.subgraph(c), heuristic)
+
+
+def reverse_cuthill_mckee_ordering(G, heuristic=None):
+    """Generate an ordering (permutation) of the graph nodes to make
+    a sparse matrix.
+
+    Uses the reverse Cuthill-McKee heuristic (based on breadth-first search)
+    [1]_.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    heuristic : function, optional
+      Function to choose starting node for RCM algorithm.  If None
+      a node from a pseudo-peripheral pair is used.  A user-defined function
+      can be supplied that takes a graph object and returns a single node.
+
+    Returns
+    -------
+    nodes : generator
+       Generator of nodes in reverse Cuthill-McKee ordering.
+
+    Examples
+    --------
+    >>> from networkx.utils import reverse_cuthill_mckee_ordering
+    >>> G = nx.path_graph(4)
+    >>> rcm = list(reverse_cuthill_mckee_ordering(G))
+    >>> A = nx.adjacency_matrix(G, nodelist=rcm)
+
+    Smallest degree node as heuristic function:
+
+    >>> def smallest_degree(G):
+    ...     return min(G, key=G.degree)
+    >>> rcm = list(reverse_cuthill_mckee_ordering(G, heuristic=smallest_degree))
+
+
+    See Also
+    --------
+    cuthill_mckee_ordering
+
+    Notes
+    -----
+    The optimal solution the bandwidth reduction is NP-complete [2]_.
+
+    References
+    ----------
+    .. [1] E. Cuthill and J. McKee.
+       Reducing the bandwidth of sparse symmetric matrices,
+       In Proc. 24th Nat. Conf. ACM, pages 157-72, 1969.
+       http://doi.acm.org/10.1145/800195.805928
+    .. [2]  Steven S. Skiena. 1997. The Algorithm Design Manual.
+       Springer-Verlag New York, Inc., New York, NY, USA.
+    """
+    return reversed(list(cuthill_mckee_ordering(G, heuristic=heuristic)))
+
+
+def connected_cuthill_mckee_ordering(G, heuristic=None):
+    # the cuthill mckee algorithm for connected graphs
+    if heuristic is None:
+        start = pseudo_peripheral_node(G)
+    else:
+        start = heuristic(G)
+    visited = {start}
+    queue = deque([start])
+    while queue:
+        parent = queue.popleft()
+        yield parent
+        nd = sorted(G.degree(set(G[parent]) - visited), key=itemgetter(1))
+        children = [n for n, d in nd]
+        visited.update(children)
+        queue.extend(children)
+
+
+def pseudo_peripheral_node(G):
+    # helper for cuthill-mckee to find a node in a "pseudo peripheral pair"
+    # to use as good starting node
+    u = arbitrary_element(G)
+    lp = 0
+    v = u
+    while True:
+        spl = nx.shortest_path_length(G, v)
+        l = max(spl.values())
+        if l <= lp:
+            break
+        lp = l
+        farthest = (n for n, dist in spl.items() if dist == l)
+        v, deg = min(G.degree(farthest), key=itemgetter(1))
+    return v
diff --git a/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/union_find.py b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/union_find.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a07129f5427cd8a3caf30095efee125bc3d853b
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/networkx/utils/union_find.py
@@ -0,0 +1,106 @@
+"""
+Union-find data structure.
+"""
+
+from networkx.utils import groups
+
+
+class UnionFind:
+    """Union-find data structure.
+
+    Each unionFind instance X maintains a family of disjoint sets of
+    hashable objects, supporting the following two methods:
+
+    - X[item] returns a name for the set containing the given item.
+      Each set is named by an arbitrarily-chosen one of its members; as
+      long as the set remains unchanged it will keep the same name. If
+      the item is not yet part of a set in X, a new singleton set is
+      created for it.
+
+    - X.union(item1, item2, ...) merges the sets containing each item
+      into a single larger set.  If any item is not yet part of a set
+      in X, it is added to X as one of the members of the merged set.
+
+      Union-find data structure. Based on Josiah Carlson's code,
+      https://code.activestate.com/recipes/215912/
+      with significant additional changes by D. Eppstein.
+      http://www.ics.uci.edu/~eppstein/PADS/UnionFind.py
+
+    """
+
+    def __init__(self, elements=None):
+        """Create a new empty union-find structure.
+
+        If *elements* is an iterable, this structure will be initialized
+        with the discrete partition on the given set of elements.
+
+        """
+        if elements is None:
+            elements = ()
+        self.parents = {}
+        self.weights = {}
+        for x in elements:
+            self.weights[x] = 1
+            self.parents[x] = x
+
+    def __getitem__(self, object):
+        """Find and return the name of the set containing the object."""
+
+        # check for previously unknown object
+        if object not in self.parents:
+            self.parents[object] = object
+            self.weights[object] = 1
+            return object
+
+        # find path of objects leading to the root
+        path = []
+        root = self.parents[object]
+        while root != object:
+            path.append(object)
+            object = root
+            root = self.parents[object]
+
+        # compress the path and return
+        for ancestor in path:
+            self.parents[ancestor] = root
+        return root
+
+    def __iter__(self):
+        """Iterate through all items ever found or unioned by this structure."""
+        return iter(self.parents)
+
+    def to_sets(self):
+        """Iterates over the sets stored in this structure.
+
+        For example::
+
+            >>> partition = UnionFind("xyz")
+            >>> sorted(map(sorted, partition.to_sets()))
+            [['x'], ['y'], ['z']]
+            >>> partition.union("x", "y")
+            >>> sorted(map(sorted, partition.to_sets()))
+            [['x', 'y'], ['z']]
+
+        """
+        # Ensure fully pruned paths
+        for x in self.parents:
+            _ = self[x]  # Evaluated for side-effect only
+
+        yield from groups(self.parents).values()
+
+    def union(self, *objects):
+        """Find the sets containing the objects and merge them all."""
+        # Find the heaviest root according to its weight.
+        roots = iter(
+            sorted(
+                {self[x] for x in objects}, key=lambda r: self.weights[r], reverse=True
+            )
+        )
+        try:
+            root = next(roots)
+        except StopIteration:
+            return
+
+        for r in roots:
+            self.weights[root] += self.weights[r]
+            self.parents[r] = root
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diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/__init__.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d4b3abb7965010010f1810b6007bc912e1887bf
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/__init__.py
@@ -0,0 +1,2025 @@
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Test utilities."""
+
+
+import atexit
+import contextlib
+import ctypes
+import enum
+import errno
+import functools
+import gc
+import importlib
+import ipaddress
+import os
+import platform
+import random
+import re
+import select
+import shlex
+import shutil
+import signal
+import socket
+import stat
+import subprocess
+import sys
+import tempfile
+import textwrap
+import threading
+import time
+import unittest
+import warnings
+from socket import AF_INET
+from socket import AF_INET6
+from socket import SOCK_STREAM
+
+
+try:
+    import pytest
+except ImportError:
+    pytest = None
+
+import psutil
+from psutil import AIX
+from psutil import LINUX
+from psutil import MACOS
+from psutil import NETBSD
+from psutil import OPENBSD
+from psutil import POSIX
+from psutil import SUNOS
+from psutil import WINDOWS
+from psutil._common import bytes2human
+from psutil._common import debug
+from psutil._common import memoize
+from psutil._common import print_color
+from psutil._common import supports_ipv6
+
+
+if POSIX:
+    from psutil._psposix import wait_pid
+
+
+# fmt: off
+__all__ = [
+    # constants
+    'DEVNULL', 'GLOBAL_TIMEOUT', 'TOLERANCE_SYS_MEM', 'NO_RETRIES',
+    'PYPY', 'PYTHON_EXE', 'PYTHON_EXE_ENV', 'ROOT_DIR', 'SCRIPTS_DIR',
+    'TESTFN_PREFIX', 'UNICODE_SUFFIX', 'INVALID_UNICODE_SUFFIX',
+    'CI_TESTING', 'VALID_PROC_STATUSES', 'TOLERANCE_DISK_USAGE', 'IS_64BIT',
+    "HAS_CPU_AFFINITY", "HAS_CPU_FREQ", "HAS_ENVIRON", "HAS_PROC_IO_COUNTERS",
+    "HAS_IONICE", "HAS_MEMORY_MAPS", "HAS_PROC_CPU_NUM", "HAS_RLIMIT",
+    "HAS_SENSORS_BATTERY", "HAS_BATTERY", "HAS_SENSORS_FANS",
+    "HAS_SENSORS_TEMPERATURES", "HAS_NET_CONNECTIONS_UNIX", "MACOS_11PLUS",
+    "MACOS_12PLUS", "COVERAGE", 'AARCH64', "PYTEST_PARALLEL",
+    # subprocesses
+    'pyrun', 'terminate', 'reap_children', 'spawn_testproc', 'spawn_zombie',
+    'spawn_children_pair',
+    # threads
+    'ThreadTask',
+    # test utils
+    'unittest', 'skip_on_access_denied', 'skip_on_not_implemented',
+    'retry_on_failure', 'TestMemoryLeak', 'PsutilTestCase',
+    'process_namespace', 'system_namespace', 'print_sysinfo',
+    'is_win_secure_system_proc', 'fake_pytest',
+    # fs utils
+    'chdir', 'safe_rmpath', 'create_py_exe', 'create_c_exe', 'get_testfn',
+    # os
+    'get_winver', 'kernel_version',
+    # sync primitives
+    'call_until', 'wait_for_pid', 'wait_for_file',
+    # network
+    'check_net_address', 'filter_proc_net_connections',
+    'get_free_port', 'bind_socket', 'bind_unix_socket', 'tcp_socketpair',
+    'unix_socketpair', 'create_sockets',
+    # compat
+    'reload_module', 'import_module_by_path',
+    # others
+    'warn', 'copyload_shared_lib', 'is_namedtuple',
+]
+# fmt: on
+
+
+# ===================================================================
+# --- constants
+# ===================================================================
+
+# --- platforms
+
+PYPY = '__pypy__' in sys.builtin_module_names
+# whether we're running this test suite on a Continuous Integration service
+GITHUB_ACTIONS = 'GITHUB_ACTIONS' in os.environ or 'CIBUILDWHEEL' in os.environ
+CI_TESTING = GITHUB_ACTIONS
+COVERAGE = 'COVERAGE_RUN' in os.environ
+PYTEST_PARALLEL = "PYTEST_XDIST_WORKER" in os.environ  # `make test-parallel`
+# are we a 64 bit process?
+IS_64BIT = sys.maxsize > 2**32
+AARCH64 = platform.machine() == "aarch64"
+
+
+@memoize
+def macos_version():
+    version_str = platform.mac_ver()[0]
+    version = tuple(map(int, version_str.split(".")[:2]))
+    if version == (10, 16):
+        # When built against an older macOS SDK, Python will report
+        # macOS 10.16 instead of the real version.
+        version_str = subprocess.check_output(
+            [
+                sys.executable,
+                "-sS",
+                "-c",
+                "import platform; print(platform.mac_ver()[0])",
+            ],
+            env={"SYSTEM_VERSION_COMPAT": "0"},
+            universal_newlines=True,
+        )
+        version = tuple(map(int, version_str.split(".")[:2]))
+    return version
+
+
+if MACOS:
+    MACOS_11PLUS = macos_version() > (10, 15)
+    MACOS_12PLUS = macos_version() >= (12, 0)
+else:
+    MACOS_11PLUS = False
+    MACOS_12PLUS = False
+
+
+# --- configurable defaults
+
+# how many times retry_on_failure() decorator will retry
+NO_RETRIES = 10
+# bytes tolerance for system-wide related tests
+TOLERANCE_SYS_MEM = 5 * 1024 * 1024  # 5MB
+TOLERANCE_DISK_USAGE = 10 * 1024 * 1024  # 10MB
+# the timeout used in functions which have to wait
+GLOBAL_TIMEOUT = 5
+# be more tolerant if we're on CI in order to avoid false positives
+if CI_TESTING:
+    NO_RETRIES *= 3
+    GLOBAL_TIMEOUT *= 3
+    TOLERANCE_SYS_MEM *= 4
+    TOLERANCE_DISK_USAGE *= 3
+
+# --- file names
+
+# Disambiguate TESTFN for parallel testing.
+if os.name == 'java':
+    # Jython disallows @ in module names
+    TESTFN_PREFIX = f"$psutil-{os.getpid()}-"
+else:
+    TESTFN_PREFIX = f"@psutil-{os.getpid()}-"
+UNICODE_SUFFIX = "-ƒőő"
+# An invalid unicode string.
+INVALID_UNICODE_SUFFIX = b"f\xc0\x80".decode('utf8', 'surrogateescape')
+ASCII_FS = sys.getfilesystemencoding().lower() in {"ascii", "us-ascii"}
+
+# --- paths
+
+ROOT_DIR = os.path.realpath(
+    os.path.join(os.path.dirname(__file__), '..', '..')
+)
+SCRIPTS_DIR = os.environ.get(
+    "PSUTIL_SCRIPTS_DIR", os.path.join(ROOT_DIR, 'scripts')
+)
+HERE = os.path.realpath(os.path.dirname(__file__))
+
+# --- support
+
+HAS_CPU_AFFINITY = hasattr(psutil.Process, "cpu_affinity")
+HAS_CPU_FREQ = hasattr(psutil, "cpu_freq")
+HAS_ENVIRON = hasattr(psutil.Process, "environ")
+HAS_GETLOADAVG = hasattr(psutil, "getloadavg")
+HAS_IONICE = hasattr(psutil.Process, "ionice")
+HAS_MEMORY_MAPS = hasattr(psutil.Process, "memory_maps")
+HAS_NET_CONNECTIONS_UNIX = POSIX and not SUNOS
+HAS_NET_IO_COUNTERS = hasattr(psutil, "net_io_counters")
+HAS_PROC_CPU_NUM = hasattr(psutil.Process, "cpu_num")
+HAS_PROC_IO_COUNTERS = hasattr(psutil.Process, "io_counters")
+HAS_RLIMIT = hasattr(psutil.Process, "rlimit")
+HAS_SENSORS_BATTERY = hasattr(psutil, "sensors_battery")
+try:
+    HAS_BATTERY = HAS_SENSORS_BATTERY and bool(psutil.sensors_battery())
+except Exception:  # noqa: BLE001
+    HAS_BATTERY = False
+HAS_SENSORS_FANS = hasattr(psutil, "sensors_fans")
+HAS_SENSORS_TEMPERATURES = hasattr(psutil, "sensors_temperatures")
+HAS_THREADS = hasattr(psutil.Process, "threads")
+SKIP_SYSCONS = (MACOS or AIX) and os.getuid() != 0
+
+# --- misc
+
+
+def _get_py_exe():
+    def attempt(exe):
+        try:
+            subprocess.check_call(
+                [exe, "-V"], stdout=subprocess.PIPE, stderr=subprocess.PIPE
+            )
+        except subprocess.CalledProcessError:
+            return None
+        else:
+            return exe
+
+    env = os.environ.copy()
+
+    # On Windows, starting with python 3.7, virtual environments use a
+    # venv launcher startup process. This does not play well when
+    # counting spawned processes, or when relying on the PID of the
+    # spawned process to do some checks, e.g. connections check per PID.
+    # Let's use the base python in this case.
+    base = getattr(sys, "_base_executable", None)
+    if WINDOWS and sys.version_info >= (3, 7) and base is not None:
+        # We need to set __PYVENV_LAUNCHER__ to sys.executable for the
+        # base python executable to know about the environment.
+        env["__PYVENV_LAUNCHER__"] = sys.executable
+        return base, env
+    elif GITHUB_ACTIONS:
+        return sys.executable, env
+    elif MACOS:
+        exe = (
+            attempt(sys.executable)
+            or attempt(os.path.realpath(sys.executable))
+            or attempt(
+                shutil.which("python{}.{}".format(*sys.version_info[:2]))
+            )
+            or attempt(psutil.Process().exe())
+        )
+        if not exe:
+            raise ValueError("can't find python exe real abspath")
+        return exe, env
+    else:
+        exe = os.path.realpath(sys.executable)
+        assert os.path.exists(exe), exe
+        return exe, env
+
+
+PYTHON_EXE, PYTHON_EXE_ENV = _get_py_exe()
+DEVNULL = open(os.devnull, 'r+')  # noqa: SIM115
+atexit.register(DEVNULL.close)
+
+VALID_PROC_STATUSES = [
+    getattr(psutil, x) for x in dir(psutil) if x.startswith('STATUS_')
+]
+AF_UNIX = getattr(socket, "AF_UNIX", object())
+
+_subprocesses_started = set()
+_pids_started = set()
+
+
+# ===================================================================
+# --- threads
+# ===================================================================
+
+
+class ThreadTask(threading.Thread):
+    """A thread task which does nothing expect staying alive."""
+
+    def __init__(self):
+        super().__init__()
+        self._running = False
+        self._interval = 0.001
+        self._flag = threading.Event()
+
+    def __repr__(self):
+        name = self.__class__.__name__
+        return f"<{name} running={self._running} at {id(self):#x}>"
+
+    def __enter__(self):
+        self.start()
+        return self
+
+    def __exit__(self, *args, **kwargs):
+        self.stop()
+
+    def start(self):
+        """Start thread and keep it running until an explicit
+        stop() request. Polls for shutdown every 'timeout' seconds.
+        """
+        if self._running:
+            raise ValueError("already started")
+        threading.Thread.start(self)
+        self._flag.wait()
+
+    def run(self):
+        self._running = True
+        self._flag.set()
+        while self._running:
+            time.sleep(self._interval)
+
+    def stop(self):
+        """Stop thread execution and and waits until it is stopped."""
+        if not self._running:
+            raise ValueError("already stopped")
+        self._running = False
+        self.join()
+
+
+# ===================================================================
+# --- subprocesses
+# ===================================================================
+
+
+def _reap_children_on_err(fun):
+    @functools.wraps(fun)
+    def wrapper(*args, **kwargs):
+        try:
+            return fun(*args, **kwargs)
+        except Exception:
+            reap_children()
+            raise
+
+    return wrapper
+
+
+@_reap_children_on_err
+def spawn_testproc(cmd=None, **kwds):
+    """Create a python subprocess which does nothing for some secs and
+    return it as a subprocess.Popen instance.
+    If "cmd" is specified that is used instead of python.
+    By default stdin and stdout are redirected to /dev/null.
+    It also attempts to make sure the process is in a reasonably
+    initialized state.
+    The process is registered for cleanup on reap_children().
+    """
+    kwds.setdefault("stdin", DEVNULL)
+    kwds.setdefault("stdout", DEVNULL)
+    kwds.setdefault("cwd", os.getcwd())
+    kwds.setdefault("env", PYTHON_EXE_ENV)
+    if WINDOWS:
+        # Prevents the subprocess to open error dialogs. This will also
+        # cause stderr to be suppressed, which is suboptimal in order
+        # to debug broken tests.
+        CREATE_NO_WINDOW = 0x8000000
+        kwds.setdefault("creationflags", CREATE_NO_WINDOW)
+    if cmd is None:
+        testfn = get_testfn(dir=os.getcwd())
+        try:
+            safe_rmpath(testfn)
+            pyline = (
+                "import time;"
+                f"open(r'{testfn}', 'w').close();"
+                "[time.sleep(0.1) for x in range(100)];"  # 10 secs
+            )
+            cmd = [PYTHON_EXE, "-c", pyline]
+            sproc = subprocess.Popen(cmd, **kwds)
+            _subprocesses_started.add(sproc)
+            wait_for_file(testfn, delete=True, empty=True)
+        finally:
+            safe_rmpath(testfn)
+    else:
+        sproc = subprocess.Popen(cmd, **kwds)
+        _subprocesses_started.add(sproc)
+        wait_for_pid(sproc.pid)
+    return sproc
+
+
+@_reap_children_on_err
+def spawn_children_pair():
+    """Create a subprocess which creates another one as in:
+    A (us) -> B (child) -> C (grandchild).
+    Return a (child, grandchild) tuple.
+    The 2 processes are fully initialized and will live for 60 secs
+    and are registered for cleanup on reap_children().
+    """
+    tfile = None
+    testfn = get_testfn(dir=os.getcwd())
+    try:
+        s = textwrap.dedent(f"""\
+            import subprocess, os, sys, time
+            s = "import os, time;"
+            s += "f = open('{os.path.basename(testfn)}', 'w');"
+            s += "f.write(str(os.getpid()));"
+            s += "f.close();"
+            s += "[time.sleep(0.1) for x in range(100 * 6)];"
+            p = subprocess.Popen([r'{PYTHON_EXE}', '-c', s])
+            p.wait()
+            """)
+        # On Windows if we create a subprocess with CREATE_NO_WINDOW flag
+        # set (which is the default) a "conhost.exe" extra process will be
+        # spawned as a child. We don't want that.
+        if WINDOWS:
+            subp, tfile = pyrun(s, creationflags=0)
+        else:
+            subp, tfile = pyrun(s)
+        child = psutil.Process(subp.pid)
+        grandchild_pid = int(wait_for_file(testfn, delete=True, empty=False))
+        _pids_started.add(grandchild_pid)
+        grandchild = psutil.Process(grandchild_pid)
+        return (child, grandchild)
+    finally:
+        safe_rmpath(testfn)
+        if tfile is not None:
+            safe_rmpath(tfile)
+
+
+def spawn_zombie():
+    """Create a zombie process and return a (parent, zombie) process tuple.
+    In order to kill the zombie parent must be terminate()d first, then
+    zombie must be wait()ed on.
+    """
+    assert psutil.POSIX
+    unix_file = get_testfn()
+    src = textwrap.dedent(f"""\
+        import os, sys, time, socket, contextlib
+        child_pid = os.fork()
+        if child_pid > 0:
+            time.sleep(3000)
+        else:
+            # this is the zombie process
+            with socket.socket(socket.AF_UNIX) as s:
+                s.connect('{unix_file}')
+                pid = bytes(str(os.getpid()), 'ascii')
+                s.sendall(pid)
+        """)
+    tfile = None
+    sock = bind_unix_socket(unix_file)
+    try:
+        sock.settimeout(GLOBAL_TIMEOUT)
+        parent, tfile = pyrun(src)
+        conn, _ = sock.accept()
+        try:
+            select.select([conn.fileno()], [], [], GLOBAL_TIMEOUT)
+            zpid = int(conn.recv(1024))
+            _pids_started.add(zpid)
+            zombie = psutil.Process(zpid)
+            call_until(lambda: zombie.status() == psutil.STATUS_ZOMBIE)
+            return (parent, zombie)
+        finally:
+            conn.close()
+    finally:
+        sock.close()
+        safe_rmpath(unix_file)
+        if tfile is not None:
+            safe_rmpath(tfile)
+
+
+@_reap_children_on_err
+def pyrun(src, **kwds):
+    """Run python 'src' code string in a separate interpreter.
+    Returns a subprocess.Popen instance and the test file where the source
+    code was written.
+    """
+    kwds.setdefault("stdout", None)
+    kwds.setdefault("stderr", None)
+    srcfile = get_testfn()
+    try:
+        with open(srcfile, "w") as f:
+            f.write(src)
+        subp = spawn_testproc([PYTHON_EXE, f.name], **kwds)
+        wait_for_pid(subp.pid)
+        return (subp, srcfile)
+    except Exception:
+        safe_rmpath(srcfile)
+        raise
+
+
+@_reap_children_on_err
+def sh(cmd, **kwds):
+    """Run cmd in a subprocess and return its output.
+    raises RuntimeError on error.
+    """
+    # Prevents subprocess to open error dialogs in case of error.
+    flags = 0x8000000 if WINDOWS else 0
+    kwds.setdefault("stdout", subprocess.PIPE)
+    kwds.setdefault("stderr", subprocess.PIPE)
+    kwds.setdefault("universal_newlines", True)
+    kwds.setdefault("creationflags", flags)
+    if isinstance(cmd, str):
+        cmd = shlex.split(cmd)
+    p = subprocess.Popen(cmd, **kwds)
+    _subprocesses_started.add(p)
+    stdout, stderr = p.communicate(timeout=GLOBAL_TIMEOUT)
+    if p.returncode != 0:
+        raise RuntimeError(stdout + stderr)
+    if stderr:
+        warn(stderr)
+    if stdout.endswith('\n'):
+        stdout = stdout[:-1]
+    return stdout
+
+
+def terminate(proc_or_pid, sig=signal.SIGTERM, wait_timeout=GLOBAL_TIMEOUT):
+    """Terminate a process and wait() for it.
+    Process can be a PID or an instance of psutil.Process(),
+    subprocess.Popen() or psutil.Popen().
+    If it's a subprocess.Popen() or psutil.Popen() instance also closes
+    its stdin / stdout / stderr fds.
+    PID is wait()ed even if the process is already gone (kills zombies).
+    Does nothing if the process does not exist.
+    Return process exit status.
+    """
+
+    def wait(proc, timeout):
+        proc.wait(timeout)
+        if WINDOWS and isinstance(proc, subprocess.Popen):
+            # Otherwise PID may still hang around.
+            try:
+                return psutil.Process(proc.pid).wait(timeout)
+            except psutil.NoSuchProcess:
+                pass
+
+    def sendsig(proc, sig):
+        # XXX: otherwise the build hangs for some reason.
+        if MACOS and GITHUB_ACTIONS:
+            sig = signal.SIGKILL
+        # If the process received SIGSTOP, SIGCONT is necessary first,
+        # otherwise SIGTERM won't work.
+        if POSIX and sig != signal.SIGKILL:
+            proc.send_signal(signal.SIGCONT)
+        proc.send_signal(sig)
+
+    def term_subprocess_proc(proc, timeout):
+        try:
+            sendsig(proc, sig)
+        except ProcessLookupError:
+            pass
+        except OSError as err:
+            if WINDOWS and err.winerror == 6:  # "invalid handle"
+                pass
+            raise
+        return wait(proc, timeout)
+
+    def term_psutil_proc(proc, timeout):
+        try:
+            sendsig(proc, sig)
+        except psutil.NoSuchProcess:
+            pass
+        return wait(proc, timeout)
+
+    def term_pid(pid, timeout):
+        try:
+            proc = psutil.Process(pid)
+        except psutil.NoSuchProcess:
+            # Needed to kill zombies.
+            if POSIX:
+                return wait_pid(pid, timeout)
+        else:
+            return term_psutil_proc(proc, timeout)
+
+    def flush_popen(proc):
+        if proc.stdout:
+            proc.stdout.close()
+        if proc.stderr:
+            proc.stderr.close()
+        # Flushing a BufferedWriter may raise an error.
+        if proc.stdin:
+            proc.stdin.close()
+
+    p = proc_or_pid
+    try:
+        if isinstance(p, int):
+            return term_pid(p, wait_timeout)
+        elif isinstance(p, (psutil.Process, psutil.Popen)):
+            return term_psutil_proc(p, wait_timeout)
+        elif isinstance(p, subprocess.Popen):
+            return term_subprocess_proc(p, wait_timeout)
+        else:
+            raise TypeError(f"wrong type {p!r}")
+    finally:
+        if isinstance(p, (subprocess.Popen, psutil.Popen)):
+            flush_popen(p)
+        pid = p if isinstance(p, int) else p.pid
+        assert not psutil.pid_exists(pid), pid
+
+
+def reap_children(recursive=False):
+    """Terminate and wait() any subprocess started by this test suite
+    and any children currently running, ensuring that no processes stick
+    around to hog resources.
+    If recursive is True it also tries to terminate and wait()
+    all grandchildren started by this process.
+    """
+    # Get the children here before terminating them, as in case of
+    # recursive=True we don't want to lose the intermediate reference
+    # pointing to the grandchildren.
+    children = psutil.Process().children(recursive=recursive)
+
+    # Terminate subprocess.Popen.
+    while _subprocesses_started:
+        subp = _subprocesses_started.pop()
+        terminate(subp)
+
+    # Collect started pids.
+    while _pids_started:
+        pid = _pids_started.pop()
+        terminate(pid)
+
+    # Terminate children.
+    if children:
+        for p in children:
+            terminate(p, wait_timeout=None)
+        _, alive = psutil.wait_procs(children, timeout=GLOBAL_TIMEOUT)
+        for p in alive:
+            warn(f"couldn't terminate process {p!r}; attempting kill()")
+            terminate(p, sig=signal.SIGKILL)
+
+
+# ===================================================================
+# --- OS
+# ===================================================================
+
+
+def kernel_version():
+    """Return a tuple such as (2, 6, 36)."""
+    if not POSIX:
+        raise NotImplementedError("not POSIX")
+    s = ""
+    uname = os.uname()[2]
+    for c in uname:
+        if c.isdigit() or c == '.':
+            s += c
+        else:
+            break
+    if not s:
+        raise ValueError(f"can't parse {uname!r}")
+    minor = 0
+    micro = 0
+    nums = s.split('.')
+    major = int(nums[0])
+    if len(nums) >= 2:
+        minor = int(nums[1])
+    if len(nums) >= 3:
+        micro = int(nums[2])
+    return (major, minor, micro)
+
+
+def get_winver():
+    if not WINDOWS:
+        raise NotImplementedError("not WINDOWS")
+    wv = sys.getwindowsversion()
+    sp = wv.service_pack_major or 0
+    return (wv[0], wv[1], sp)
+
+
+# ===================================================================
+# --- sync primitives
+# ===================================================================
+
+
+class retry:
+    """A retry decorator."""
+
+    def __init__(
+        self,
+        exception=Exception,
+        timeout=None,
+        retries=None,
+        interval=0.001,
+        logfun=None,
+    ):
+        if timeout and retries:
+            raise ValueError("timeout and retries args are mutually exclusive")
+        self.exception = exception
+        self.timeout = timeout
+        self.retries = retries
+        self.interval = interval
+        self.logfun = logfun
+
+    def __iter__(self):
+        if self.timeout:
+            stop_at = time.time() + self.timeout
+            while time.time() < stop_at:
+                yield
+        elif self.retries:
+            for _ in range(self.retries):
+                yield
+        else:
+            while True:
+                yield
+
+    def sleep(self):
+        if self.interval is not None:
+            time.sleep(self.interval)
+
+    def __call__(self, fun):
+        @functools.wraps(fun)
+        def wrapper(*args, **kwargs):
+            exc = None
+            for _ in self:
+                try:
+                    return fun(*args, **kwargs)
+                except self.exception as _:
+                    exc = _
+                    if self.logfun is not None:
+                        self.logfun(exc)
+                    self.sleep()
+                    continue
+
+            raise exc
+
+        # This way the user of the decorated function can change config
+        # parameters.
+        wrapper.decorator = self
+        return wrapper
+
+
+@retry(
+    exception=psutil.NoSuchProcess,
+    logfun=None,
+    timeout=GLOBAL_TIMEOUT,
+    interval=0.001,
+)
+def wait_for_pid(pid):
+    """Wait for pid to show up in the process list then return.
+    Used in the test suite to give time the sub process to initialize.
+    """
+    if pid not in psutil.pids():
+        raise psutil.NoSuchProcess(pid)
+    psutil.Process(pid)
+
+
+@retry(
+    exception=(FileNotFoundError, AssertionError),
+    logfun=None,
+    timeout=GLOBAL_TIMEOUT,
+    interval=0.001,
+)
+def wait_for_file(fname, delete=True, empty=False):
+    """Wait for a file to be written on disk with some content."""
+    with open(fname, "rb") as f:
+        data = f.read()
+    if not empty:
+        assert data
+    if delete:
+        safe_rmpath(fname)
+    return data
+
+
+@retry(
+    exception=AssertionError,
+    logfun=None,
+    timeout=GLOBAL_TIMEOUT,
+    interval=0.001,
+)
+def call_until(fun):
+    """Keep calling function until it evaluates to True."""
+    ret = fun()
+    assert ret
+    return ret
+
+
+# ===================================================================
+# --- fs
+# ===================================================================
+
+
+def safe_rmpath(path):
+    """Convenience function for removing temporary test files or dirs."""
+
+    def retry_fun(fun):
+        # On Windows it could happen that the file or directory has
+        # open handles or references preventing the delete operation
+        # to succeed immediately, so we retry for a while. See:
+        # https://bugs.python.org/issue33240
+        stop_at = time.time() + GLOBAL_TIMEOUT
+        while time.time() < stop_at:
+            try:
+                return fun()
+            except FileNotFoundError:
+                pass
+            except OSError as _:
+                err = _
+                warn(f"ignoring {err}")
+            time.sleep(0.01)
+        raise err
+
+    try:
+        st = os.stat(path)
+        if stat.S_ISDIR(st.st_mode):
+            fun = functools.partial(shutil.rmtree, path)
+        else:
+            fun = functools.partial(os.remove, path)
+        if POSIX:
+            fun()
+        else:
+            retry_fun(fun)
+    except FileNotFoundError:
+        pass
+
+
+def safe_mkdir(dir):
+    """Convenience function for creating a directory."""
+    try:
+        os.mkdir(dir)
+    except FileExistsError:
+        pass
+
+
+@contextlib.contextmanager
+def chdir(dirname):
+    """Context manager which temporarily changes the current directory."""
+    curdir = os.getcwd()
+    try:
+        os.chdir(dirname)
+        yield
+    finally:
+        os.chdir(curdir)
+
+
+def create_py_exe(path):
+    """Create a Python executable file in the given location."""
+    assert not os.path.exists(path), path
+    atexit.register(safe_rmpath, path)
+    shutil.copyfile(PYTHON_EXE, path)
+    if POSIX:
+        st = os.stat(path)
+        os.chmod(path, st.st_mode | stat.S_IEXEC)
+    return path
+
+
+def create_c_exe(path, c_code=None):
+    """Create a compiled C executable in the given location."""
+    assert not os.path.exists(path), path
+    if not shutil.which("gcc"):
+        raise pytest.skip("gcc is not installed")
+    if c_code is None:
+        c_code = textwrap.dedent("""
+            #include <unistd.h>
+            int main() {
+                pause();
+                return 1;
+            }
+            """)
+    else:
+        assert isinstance(c_code, str), c_code
+
+    atexit.register(safe_rmpath, path)
+    with open(get_testfn(suffix='.c'), "w") as f:
+        f.write(c_code)
+    try:
+        subprocess.check_call(["gcc", f.name, "-o", path])
+    finally:
+        safe_rmpath(f.name)
+    return path
+
+
+def get_testfn(suffix="", dir=None):
+    """Return an absolute pathname of a file or dir that did not
+    exist at the time this call is made. Also schedule it for safe
+    deletion at interpreter exit. It's technically racy but probably
+    not really due to the time variant.
+    """
+    while True:
+        name = tempfile.mktemp(prefix=TESTFN_PREFIX, suffix=suffix, dir=dir)
+        if not os.path.exists(name):  # also include dirs
+            path = os.path.realpath(name)  # needed for OSX
+            atexit.register(safe_rmpath, path)
+            return path
+
+
+# ===================================================================
+# --- testing
+# ===================================================================
+
+
+class fake_pytest:
+    """A class that mimics some basic pytest APIs. This is meant for
+    when unit tests are run in production, where pytest may not be
+    installed. Still, the user can test psutil installation via:
+
+        $ python3 -m psutil.tests
+    """
+
+    @staticmethod
+    def main(*args, **kw):  # noqa: ARG004
+        """Mimics pytest.main(). It has the same effect as running
+        `python3 -m unittest -v` from the project root directory.
+        """
+        suite = unittest.TestLoader().discover(HERE)
+        unittest.TextTestRunner(verbosity=2).run(suite)
+        warnings.warn(
+            "Fake pytest module was used. Test results may be inaccurate.",
+            UserWarning,
+            stacklevel=1,
+        )
+        return suite
+
+    @staticmethod
+    def raises(exc, match=None):
+        """Mimics `pytest.raises`."""
+
+        class ExceptionInfo:
+            _exc = None
+
+            @property
+            def value(self):
+                return self._exc
+
+        @contextlib.contextmanager
+        def context(exc, match=None):
+            einfo = ExceptionInfo()
+            try:
+                yield einfo
+            except exc as err:
+                if match and not re.search(match, str(err)):
+                    msg = f'"{match}" does not match "{err}"'
+                    raise AssertionError(msg)
+                einfo._exc = err
+            else:
+                raise AssertionError(f"{exc!r} not raised")
+
+        return context(exc, match=match)
+
+    @staticmethod
+    def warns(warning, match=None):
+        """Mimics `pytest.warns`."""
+        if match:
+            return unittest.TestCase().assertWarnsRegex(warning, match)
+        return unittest.TestCase().assertWarns(warning)
+
+    @staticmethod
+    def skip(reason=""):
+        """Mimics `unittest.SkipTest`."""
+        raise unittest.SkipTest(reason)
+
+    class mark:
+
+        @staticmethod
+        def skipif(condition, reason=""):
+            """Mimics `@pytest.mark.skipif` decorator."""
+            return unittest.skipIf(condition, reason)
+
+        class xdist_group:
+            """Mimics `@pytest.mark.xdist_group` decorator (no-op)."""
+
+            def __init__(self, name=None):
+                pass
+
+            def __call__(self, cls_or_meth):
+                return cls_or_meth
+
+
+if pytest is None:
+    pytest = fake_pytest
+
+
+class PsutilTestCase(unittest.TestCase):
+    """Test class providing auto-cleanup wrappers on top of process
+    test utilities. All test classes should derive from this one, even
+    if we use pytest.
+    """
+
+    def get_testfn(self, suffix="", dir=None):
+        fname = get_testfn(suffix=suffix, dir=dir)
+        self.addCleanup(safe_rmpath, fname)
+        return fname
+
+    def spawn_testproc(self, *args, **kwds):
+        sproc = spawn_testproc(*args, **kwds)
+        self.addCleanup(terminate, sproc)
+        return sproc
+
+    def spawn_children_pair(self):
+        child1, child2 = spawn_children_pair()
+        self.addCleanup(terminate, child2)
+        self.addCleanup(terminate, child1)  # executed first
+        return (child1, child2)
+
+    def spawn_zombie(self):
+        parent, zombie = spawn_zombie()
+        self.addCleanup(terminate, zombie)
+        self.addCleanup(terminate, parent)  # executed first
+        return (parent, zombie)
+
+    def pyrun(self, *args, **kwds):
+        sproc, srcfile = pyrun(*args, **kwds)
+        self.addCleanup(safe_rmpath, srcfile)
+        self.addCleanup(terminate, sproc)  # executed first
+        return sproc
+
+    def _check_proc_exc(self, proc, exc):
+        assert isinstance(exc, psutil.Error)
+        assert exc.pid == proc.pid
+        assert exc.name == proc._name
+        if exc.name:
+            assert exc.name
+        if isinstance(exc, psutil.ZombieProcess):
+            assert exc.ppid == proc._ppid
+            if exc.ppid is not None:
+                assert exc.ppid >= 0
+        str(exc)
+        repr(exc)
+
+    def assertPidGone(self, pid):
+        with pytest.raises(psutil.NoSuchProcess) as cm:
+            try:
+                psutil.Process(pid)
+            except psutil.ZombieProcess:
+                raise AssertionError("wasn't supposed to raise ZombieProcess")
+        assert cm.value.pid == pid
+        assert cm.value.name is None
+        assert not psutil.pid_exists(pid), pid
+        assert pid not in psutil.pids()
+        assert pid not in [x.pid for x in psutil.process_iter()]
+
+    def assertProcessGone(self, proc):
+        self.assertPidGone(proc.pid)
+        ns = process_namespace(proc)
+        for fun, name in ns.iter(ns.all, clear_cache=True):
+            with self.subTest(proc=proc, name=name):
+                try:
+                    ret = fun()
+                except psutil.ZombieProcess:
+                    raise
+                except psutil.NoSuchProcess as exc:
+                    self._check_proc_exc(proc, exc)
+                else:
+                    msg = (
+                        f"Process.{name}() didn't raise NSP and returned"
+                        f" {ret!r}"
+                    )
+                    raise AssertionError(msg)
+        proc.wait(timeout=0)  # assert not raise TimeoutExpired
+
+    def assertProcessZombie(self, proc):
+        # A zombie process should always be instantiable.
+        clone = psutil.Process(proc.pid)
+        # Cloned zombie on Open/NetBSD has null creation time, see:
+        # https://github.com/giampaolo/psutil/issues/2287
+        assert proc == clone
+        if not (OPENBSD or NETBSD):
+            assert hash(proc) == hash(clone)
+        # Its status always be querable.
+        assert proc.status() == psutil.STATUS_ZOMBIE
+        # It should be considered 'running'.
+        assert proc.is_running()
+        assert psutil.pid_exists(proc.pid)
+        # as_dict() shouldn't crash.
+        proc.as_dict()
+        # It should show up in pids() and process_iter().
+        assert proc.pid in psutil.pids()
+        assert proc.pid in [x.pid for x in psutil.process_iter()]
+        psutil._pmap = {}
+        assert proc.pid in [x.pid for x in psutil.process_iter()]
+        # Call all methods.
+        ns = process_namespace(proc)
+        for fun, name in ns.iter(ns.all, clear_cache=True):
+            with self.subTest(proc=proc, name=name):
+                try:
+                    fun()
+                except (psutil.ZombieProcess, psutil.AccessDenied) as exc:
+                    self._check_proc_exc(proc, exc)
+        if LINUX:
+            # https://github.com/giampaolo/psutil/pull/2288
+            with pytest.raises(psutil.ZombieProcess) as cm:
+                proc.cmdline()
+            self._check_proc_exc(proc, cm.value)
+            with pytest.raises(psutil.ZombieProcess) as cm:
+                proc.exe()
+            self._check_proc_exc(proc, cm.value)
+            with pytest.raises(psutil.ZombieProcess) as cm:
+                proc.memory_maps()
+            self._check_proc_exc(proc, cm.value)
+        # Zombie cannot be signaled or terminated.
+        proc.suspend()
+        proc.resume()
+        proc.terminate()
+        proc.kill()
+        assert proc.is_running()
+        assert psutil.pid_exists(proc.pid)
+        assert proc.pid in psutil.pids()
+        assert proc.pid in [x.pid for x in psutil.process_iter()]
+        psutil._pmap = {}
+        assert proc.pid in [x.pid for x in psutil.process_iter()]
+
+        # Its parent should 'see' it (edit: not true on BSD and MACOS).
+        # descendants = [x.pid for x in psutil.Process().children(
+        #                recursive=True)]
+        # self.assertIn(proc.pid, descendants)
+
+        # __eq__ can't be relied upon because creation time may not be
+        # querable.
+        # self.assertEqual(proc, psutil.Process(proc.pid))
+
+        # XXX should we also assume ppid() to be usable? Note: this
+        # would be an important use case as the only way to get
+        # rid of a zombie is to kill its parent.
+        # self.assertEqual(proc.ppid(), os.getpid())
+
+
+@pytest.mark.skipif(PYPY, reason="unreliable on PYPY")
+class TestMemoryLeak(PsutilTestCase):
+    """Test framework class for detecting function memory leaks,
+    typically functions implemented in C which forgot to free() memory
+    from the heap. It does so by checking whether the process memory
+    usage increased before and after calling the function many times.
+
+    Note that this is hard (probably impossible) to do reliably, due
+    to how the OS handles memory, the GC and so on (memory can even
+    decrease!). In order to avoid false positives, in case of failure
+    (mem > 0) we retry the test for up to 5 times, increasing call
+    repetitions each time. If the memory keeps increasing then it's a
+    failure.
+
+    If available (Linux, OSX, Windows), USS memory is used for comparison,
+    since it's supposed to be more precise, see:
+    https://gmpy.dev/blog/2016/real-process-memory-and-environ-in-python
+    If not, RSS memory is used. mallinfo() on Linux and _heapwalk() on
+    Windows may give even more precision, but at the moment are not
+    implemented.
+
+    PyPy appears to be completely unstable for this framework, probably
+    because of its JIT, so tests on PYPY are skipped.
+
+    Usage:
+
+        class TestLeaks(psutil.tests.TestMemoryLeak):
+
+            def test_fun(self):
+                self.execute(some_function)
+    """
+
+    # Configurable class attrs.
+    times = 200
+    warmup_times = 10
+    tolerance = 0  # memory
+    retries = 10 if CI_TESTING else 5
+    verbose = True
+    _thisproc = psutil.Process()
+    _psutil_debug_orig = bool(os.getenv('PSUTIL_DEBUG'))
+
+    @classmethod
+    def setUpClass(cls):
+        psutil._set_debug(False)  # avoid spamming to stderr
+
+    @classmethod
+    def tearDownClass(cls):
+        psutil._set_debug(cls._psutil_debug_orig)
+
+    def _get_mem(self):
+        # USS is the closest thing we have to "real" memory usage and it
+        # should be less likely to produce false positives.
+        mem = self._thisproc.memory_full_info()
+        return getattr(mem, "uss", mem.rss)
+
+    def _get_num_fds(self):
+        if POSIX:
+            return self._thisproc.num_fds()
+        else:
+            return self._thisproc.num_handles()
+
+    def _log(self, msg):
+        if self.verbose:
+            print_color(msg, color="yellow", file=sys.stderr)
+
+    def _check_fds(self, fun):
+        """Makes sure num_fds() (POSIX) or num_handles() (Windows) does
+        not increase after calling a function.  Used to discover forgotten
+        close(2) and CloseHandle syscalls.
+        """
+        before = self._get_num_fds()
+        self.call(fun)
+        after = self._get_num_fds()
+        diff = after - before
+        if diff < 0:
+            msg = (
+                f"negative diff {diff!r} (gc probably collected a"
+                " resource from a previous test)"
+            )
+            raise self.fail(msg)
+        if diff > 0:
+            type_ = "fd" if POSIX else "handle"
+            if diff > 1:
+                type_ += "s"
+            msg = f"{diff} unclosed {type_} after calling {fun!r}"
+            raise self.fail(msg)
+
+    def _call_ntimes(self, fun, times):
+        """Get 2 distinct memory samples, before and after having
+        called fun repeatedly, and return the memory difference.
+        """
+        gc.collect(generation=1)
+        mem1 = self._get_mem()
+        for x in range(times):
+            ret = self.call(fun)
+            del x, ret
+        gc.collect(generation=1)
+        mem2 = self._get_mem()
+        assert gc.garbage == []
+        diff = mem2 - mem1  # can also be negative
+        return diff
+
+    def _check_mem(self, fun, times, retries, tolerance):
+        messages = []
+        prev_mem = 0
+        increase = times
+        for idx in range(1, retries + 1):
+            mem = self._call_ntimes(fun, times)
+            msg = "Run #{}: extra-mem={}, per-call={}, calls={}".format(
+                idx,
+                bytes2human(mem),
+                bytes2human(mem / times),
+                times,
+            )
+            messages.append(msg)
+            success = mem <= tolerance or mem <= prev_mem
+            if success:
+                if idx > 1:
+                    self._log(msg)
+                return
+            else:
+                if idx == 1:
+                    print()  # noqa: T201
+                self._log(msg)
+                times += increase
+                prev_mem = mem
+        raise self.fail(". ".join(messages))
+
+    # ---
+
+    def call(self, fun):
+        return fun()
+
+    def execute(
+        self, fun, times=None, warmup_times=None, retries=None, tolerance=None
+    ):
+        """Test a callable."""
+        times = times if times is not None else self.times
+        warmup_times = (
+            warmup_times if warmup_times is not None else self.warmup_times
+        )
+        retries = retries if retries is not None else self.retries
+        tolerance = tolerance if tolerance is not None else self.tolerance
+        try:
+            assert times >= 1, "times must be >= 1"
+            assert warmup_times >= 0, "warmup_times must be >= 0"
+            assert retries >= 0, "retries must be >= 0"
+            assert tolerance >= 0, "tolerance must be >= 0"
+        except AssertionError as err:
+            raise ValueError(str(err))
+
+        self._call_ntimes(fun, warmup_times)  # warm up
+        self._check_fds(fun)
+        self._check_mem(fun, times=times, retries=retries, tolerance=tolerance)
+
+    def execute_w_exc(self, exc, fun, **kwargs):
+        """Convenience method to test a callable while making sure it
+        raises an exception on every call.
+        """
+
+        def call():
+            self.assertRaises(exc, fun)
+
+        self.execute(call, **kwargs)
+
+
+def print_sysinfo():
+    import collections
+    import datetime
+    import getpass
+    import locale
+    import pprint
+
+    try:
+        import pip
+    except ImportError:
+        pip = None
+    try:
+        import wheel
+    except ImportError:
+        wheel = None
+
+    info = collections.OrderedDict()
+
+    # OS
+    if psutil.LINUX and shutil.which("lsb_release"):
+        info['OS'] = sh('lsb_release -d -s')
+    elif psutil.OSX:
+        info['OS'] = f"Darwin {platform.mac_ver()[0]}"
+    elif psutil.WINDOWS:
+        info['OS'] = "Windows " + ' '.join(map(str, platform.win32_ver()))
+        if hasattr(platform, 'win32_edition'):
+            info['OS'] += ", " + platform.win32_edition()
+    else:
+        info['OS'] = f"{platform.system()} {platform.version()}"
+    info['arch'] = ', '.join(
+        list(platform.architecture()) + [platform.machine()]
+    )
+    if psutil.POSIX:
+        info['kernel'] = platform.uname()[2]
+
+    # python
+    info['python'] = ', '.join([
+        platform.python_implementation(),
+        platform.python_version(),
+        platform.python_compiler(),
+    ])
+    info['pip'] = getattr(pip, '__version__', 'not installed')
+    if wheel is not None:
+        info['pip'] += f" (wheel={wheel.__version__})"
+
+    # UNIX
+    if psutil.POSIX:
+        if shutil.which("gcc"):
+            out = sh(['gcc', '--version'])
+            info['gcc'] = str(out).split('\n')[0]
+        else:
+            info['gcc'] = 'not installed'
+        s = platform.libc_ver()[1]
+        if s:
+            info['glibc'] = s
+
+    # system
+    info['fs-encoding'] = sys.getfilesystemencoding()
+    lang = locale.getlocale()
+    info['lang'] = f"{lang[0]}, {lang[1]}"
+    info['boot-time'] = datetime.datetime.fromtimestamp(
+        psutil.boot_time()
+    ).strftime("%Y-%m-%d %H:%M:%S")
+    info['time'] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    info['user'] = getpass.getuser()
+    info['home'] = os.path.expanduser("~")
+    info['cwd'] = os.getcwd()
+    info['pyexe'] = PYTHON_EXE
+    info['hostname'] = platform.node()
+    info['PID'] = os.getpid()
+
+    # metrics
+    info['cpus'] = psutil.cpu_count()
+    info['loadavg'] = "{:.1f}%, {:.1f}%, {:.1f}%".format(
+        *tuple(x / psutil.cpu_count() * 100 for x in psutil.getloadavg())
+    )
+    mem = psutil.virtual_memory()
+    info['memory'] = "{}%%, used={}, total={}".format(
+        int(mem.percent),
+        bytes2human(mem.used),
+        bytes2human(mem.total),
+    )
+    swap = psutil.swap_memory()
+    info['swap'] = "{}%%, used={}, total={}".format(
+        int(swap.percent),
+        bytes2human(swap.used),
+        bytes2human(swap.total),
+    )
+    info['pids'] = len(psutil.pids())
+    pinfo = psutil.Process().as_dict()
+    pinfo.pop('memory_maps', None)
+    info['proc'] = pprint.pformat(pinfo)
+
+    print("=" * 70, file=sys.stderr)  # noqa: T201
+    for k, v in info.items():
+        print("{:<17} {}".format(k + ":", v), file=sys.stderr)  # noqa: T201
+    print("=" * 70, file=sys.stderr)  # noqa: T201
+    sys.stdout.flush()
+
+    # if WINDOWS:
+    #     os.system("tasklist")
+    # elif shutil.which("ps"):
+    #     os.system("ps aux")
+    # print("=" * 70, file=sys.stderr)
+
+    sys.stdout.flush()
+
+
+def is_win_secure_system_proc(pid):
+    # see: https://github.com/giampaolo/psutil/issues/2338
+    @memoize
+    def get_procs():
+        ret = {}
+        out = sh("tasklist.exe /NH /FO csv")
+        for line in out.splitlines()[1:]:
+            bits = [x.replace('"', "") for x in line.split(",")]
+            name, pid = bits[0], int(bits[1])
+            ret[pid] = name
+        return ret
+
+    try:
+        return get_procs()[pid] == "Secure System"
+    except KeyError:
+        return False
+
+
+def _get_eligible_cpu():
+    p = psutil.Process()
+    if hasattr(p, "cpu_num"):
+        return p.cpu_num()
+    elif hasattr(p, "cpu_affinity"):
+        return random.choice(p.cpu_affinity())
+    return 0
+
+
+class process_namespace:
+    """A container that lists all Process class method names + some
+    reasonable parameters to be called with. Utility methods (parent(),
+    children(), ...) are excluded.
+
+    >>> ns = process_namespace(psutil.Process())
+    >>> for fun, name in ns.iter(ns.getters):
+    ...    fun()
+    """
+
+    utils = [('cpu_percent', (), {}), ('memory_percent', (), {})]
+
+    ignored = [
+        ('as_dict', (), {}),
+        ('children', (), {'recursive': True}),
+        ('connections', (), {}),  # deprecated
+        ('is_running', (), {}),
+        ('oneshot', (), {}),
+        ('parent', (), {}),
+        ('parents', (), {}),
+        ('pid', (), {}),
+        ('wait', (0,), {}),
+    ]
+
+    getters = [
+        ('cmdline', (), {}),
+        ('cpu_times', (), {}),
+        ('create_time', (), {}),
+        ('cwd', (), {}),
+        ('exe', (), {}),
+        ('memory_full_info', (), {}),
+        ('memory_info', (), {}),
+        ('name', (), {}),
+        ('net_connections', (), {'kind': 'all'}),
+        ('nice', (), {}),
+        ('num_ctx_switches', (), {}),
+        ('num_threads', (), {}),
+        ('open_files', (), {}),
+        ('ppid', (), {}),
+        ('status', (), {}),
+        ('threads', (), {}),
+        ('username', (), {}),
+    ]
+    if POSIX:
+        getters += [('uids', (), {})]
+        getters += [('gids', (), {})]
+        getters += [('terminal', (), {})]
+        getters += [('num_fds', (), {})]
+    if HAS_PROC_IO_COUNTERS:
+        getters += [('io_counters', (), {})]
+    if HAS_IONICE:
+        getters += [('ionice', (), {})]
+    if HAS_RLIMIT:
+        getters += [('rlimit', (psutil.RLIMIT_NOFILE,), {})]
+    if HAS_CPU_AFFINITY:
+        getters += [('cpu_affinity', (), {})]
+    if HAS_PROC_CPU_NUM:
+        getters += [('cpu_num', (), {})]
+    if HAS_ENVIRON:
+        getters += [('environ', (), {})]
+    if WINDOWS:
+        getters += [('num_handles', (), {})]
+    if HAS_MEMORY_MAPS:
+        getters += [('memory_maps', (), {'grouped': False})]
+
+    setters = []
+    if POSIX:
+        setters += [('nice', (0,), {})]
+    else:
+        setters += [('nice', (psutil.NORMAL_PRIORITY_CLASS,), {})]
+    if HAS_RLIMIT:
+        setters += [('rlimit', (psutil.RLIMIT_NOFILE, (1024, 4096)), {})]
+    if HAS_IONICE:
+        if LINUX:
+            setters += [('ionice', (psutil.IOPRIO_CLASS_NONE, 0), {})]
+        else:
+            setters += [('ionice', (psutil.IOPRIO_NORMAL,), {})]
+    if HAS_CPU_AFFINITY:
+        setters += [('cpu_affinity', ([_get_eligible_cpu()],), {})]
+
+    killers = [
+        ('send_signal', (signal.SIGTERM,), {}),
+        ('suspend', (), {}),
+        ('resume', (), {}),
+        ('terminate', (), {}),
+        ('kill', (), {}),
+    ]
+    if WINDOWS:
+        killers += [('send_signal', (signal.CTRL_C_EVENT,), {})]
+        killers += [('send_signal', (signal.CTRL_BREAK_EVENT,), {})]
+
+    all = utils + getters + setters + killers
+
+    def __init__(self, proc):
+        self._proc = proc
+
+    def iter(self, ls, clear_cache=True):
+        """Given a list of tuples yields a set of (fun, fun_name) tuples
+        in random order.
+        """
+        ls = list(ls)
+        random.shuffle(ls)
+        for fun_name, args, kwds in ls:
+            if clear_cache:
+                self.clear_cache()
+            fun = getattr(self._proc, fun_name)
+            fun = functools.partial(fun, *args, **kwds)
+            yield (fun, fun_name)
+
+    def clear_cache(self):
+        """Clear the cache of a Process instance."""
+        self._proc._init(self._proc.pid, _ignore_nsp=True)
+
+    @classmethod
+    def test_class_coverage(cls, test_class, ls):
+        """Given a TestCase instance and a list of tuples checks that
+        the class defines the required test method names.
+        """
+        for fun_name, _, _ in ls:
+            meth_name = 'test_' + fun_name
+            if not hasattr(test_class, meth_name):
+                msg = (
+                    f"{test_class.__class__.__name__!r} class should define a"
+                    f" {meth_name!r} method"
+                )
+                raise AttributeError(msg)
+
+    @classmethod
+    def test(cls):
+        this = {x[0] for x in cls.all}
+        ignored = {x[0] for x in cls.ignored}
+        klass = {x for x in dir(psutil.Process) if x[0] != '_'}
+        leftout = (this | ignored) ^ klass
+        if leftout:
+            raise ValueError(f"uncovered Process class names: {leftout!r}")
+
+
+class system_namespace:
+    """A container that lists all the module-level, system-related APIs.
+    Utilities such as cpu_percent() are excluded. Usage:
+
+    >>> ns = system_namespace
+    >>> for fun, name in ns.iter(ns.getters):
+    ...    fun()
+    """
+
+    getters = [
+        ('boot_time', (), {}),
+        ('cpu_count', (), {'logical': False}),
+        ('cpu_count', (), {'logical': True}),
+        ('cpu_stats', (), {}),
+        ('cpu_times', (), {'percpu': False}),
+        ('cpu_times', (), {'percpu': True}),
+        ('disk_io_counters', (), {'perdisk': True}),
+        ('disk_partitions', (), {'all': True}),
+        ('disk_usage', (os.getcwd(),), {}),
+        ('net_connections', (), {'kind': 'all'}),
+        ('net_if_addrs', (), {}),
+        ('net_if_stats', (), {}),
+        ('net_io_counters', (), {'pernic': True}),
+        ('pid_exists', (os.getpid(),), {}),
+        ('pids', (), {}),
+        ('swap_memory', (), {}),
+        ('users', (), {}),
+        ('virtual_memory', (), {}),
+    ]
+    if HAS_CPU_FREQ:
+        if MACOS and platform.machine() == 'arm64':  # skipped due to #1892
+            pass
+        else:
+            getters += [('cpu_freq', (), {'percpu': True})]
+    if HAS_GETLOADAVG:
+        getters += [('getloadavg', (), {})]
+    if HAS_SENSORS_TEMPERATURES:
+        getters += [('sensors_temperatures', (), {})]
+    if HAS_SENSORS_FANS:
+        getters += [('sensors_fans', (), {})]
+    if HAS_SENSORS_BATTERY:
+        getters += [('sensors_battery', (), {})]
+    if WINDOWS:
+        getters += [('win_service_iter', (), {})]
+        getters += [('win_service_get', ('alg',), {})]
+
+    ignored = [
+        ('process_iter', (), {}),
+        ('wait_procs', ([psutil.Process()],), {}),
+        ('cpu_percent', (), {}),
+        ('cpu_times_percent', (), {}),
+    ]
+
+    all = getters
+
+    @staticmethod
+    def iter(ls):
+        """Given a list of tuples yields a set of (fun, fun_name) tuples
+        in random order.
+        """
+        ls = list(ls)
+        random.shuffle(ls)
+        for fun_name, args, kwds in ls:
+            fun = getattr(psutil, fun_name)
+            fun = functools.partial(fun, *args, **kwds)
+            yield (fun, fun_name)
+
+    test_class_coverage = process_namespace.test_class_coverage
+
+
+def retry_on_failure(retries=NO_RETRIES):
+    """Decorator which runs a test function and retries N times before
+    actually failing.
+    """
+
+    def logfun(exc):
+        print(f"{exc!r}, retrying", file=sys.stderr)  # noqa: T201
+
+    return retry(
+        exception=AssertionError, timeout=None, retries=retries, logfun=logfun
+    )
+
+
+def skip_on_access_denied(only_if=None):
+    """Decorator to Ignore AccessDenied exceptions."""
+
+    def decorator(fun):
+        @functools.wraps(fun)
+        def wrapper(*args, **kwargs):
+            try:
+                return fun(*args, **kwargs)
+            except psutil.AccessDenied:
+                if only_if is not None:
+                    if not only_if:
+                        raise
+                raise pytest.skip("raises AccessDenied")
+
+        return wrapper
+
+    return decorator
+
+
+def skip_on_not_implemented(only_if=None):
+    """Decorator to Ignore NotImplementedError exceptions."""
+
+    def decorator(fun):
+        @functools.wraps(fun)
+        def wrapper(*args, **kwargs):
+            try:
+                return fun(*args, **kwargs)
+            except NotImplementedError:
+                if only_if is not None:
+                    if not only_if:
+                        raise
+                msg = (
+                    f"{fun.__name__!r} was skipped because it raised"
+                    " NotImplementedError"
+                )
+                raise pytest.skip(msg)
+
+        return wrapper
+
+    return decorator
+
+
+# ===================================================================
+# --- network
+# ===================================================================
+
+
+# XXX: no longer used
+def get_free_port(host='127.0.0.1'):
+    """Return an unused TCP port. Subject to race conditions."""
+    with socket.socket() as sock:
+        sock.bind((host, 0))
+        return sock.getsockname()[1]
+
+
+def bind_socket(family=AF_INET, type=SOCK_STREAM, addr=None):
+    """Binds a generic socket."""
+    if addr is None and family in {AF_INET, AF_INET6}:
+        addr = ("", 0)
+    sock = socket.socket(family, type)
+    try:
+        if os.name not in {'nt', 'cygwin'}:
+            sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+        sock.bind(addr)
+        if type == socket.SOCK_STREAM:
+            sock.listen(5)
+        return sock
+    except Exception:
+        sock.close()
+        raise
+
+
+def bind_unix_socket(name, type=socket.SOCK_STREAM):
+    """Bind a UNIX socket."""
+    assert psutil.POSIX
+    assert not os.path.exists(name), name
+    sock = socket.socket(socket.AF_UNIX, type)
+    try:
+        sock.bind(name)
+        if type == socket.SOCK_STREAM:
+            sock.listen(5)
+    except Exception:
+        sock.close()
+        raise
+    return sock
+
+
+def tcp_socketpair(family, addr=("", 0)):
+    """Build a pair of TCP sockets connected to each other.
+    Return a (server, client) tuple.
+    """
+    with socket.socket(family, SOCK_STREAM) as ll:
+        ll.bind(addr)
+        ll.listen(5)
+        addr = ll.getsockname()
+        c = socket.socket(family, SOCK_STREAM)
+        try:
+            c.connect(addr)
+            caddr = c.getsockname()
+            while True:
+                a, addr = ll.accept()
+                # check that we've got the correct client
+                if addr == caddr:
+                    return (a, c)
+                a.close()
+        except OSError:
+            c.close()
+            raise
+
+
+def unix_socketpair(name):
+    """Build a pair of UNIX sockets connected to each other through
+    the same UNIX file name.
+    Return a (server, client) tuple.
+    """
+    assert psutil.POSIX
+    server = client = None
+    try:
+        server = bind_unix_socket(name, type=socket.SOCK_STREAM)
+        server.setblocking(0)
+        client = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
+        client.setblocking(0)
+        client.connect(name)
+        # new = server.accept()
+    except Exception:
+        if server is not None:
+            server.close()
+        if client is not None:
+            client.close()
+        raise
+    return (server, client)
+
+
+@contextlib.contextmanager
+def create_sockets():
+    """Open as many socket families / types as possible."""
+    socks = []
+    fname1 = fname2 = None
+    try:
+        socks.extend((
+            bind_socket(socket.AF_INET, socket.SOCK_STREAM),
+            bind_socket(socket.AF_INET, socket.SOCK_DGRAM),
+        ))
+        if supports_ipv6():
+            socks.extend((
+                bind_socket(socket.AF_INET6, socket.SOCK_STREAM),
+                bind_socket(socket.AF_INET6, socket.SOCK_DGRAM),
+            ))
+        if POSIX and HAS_NET_CONNECTIONS_UNIX:
+            fname1 = get_testfn()
+            fname2 = get_testfn()
+            s1, s2 = unix_socketpair(fname1)
+            s3 = bind_unix_socket(fname2, type=socket.SOCK_DGRAM)
+            for s in (s1, s2, s3):
+                socks.append(s)
+        yield socks
+    finally:
+        for s in socks:
+            s.close()
+        for fname in (fname1, fname2):
+            if fname is not None:
+                safe_rmpath(fname)
+
+
+def check_net_address(addr, family):
+    """Check a net address validity. Supported families are IPv4,
+    IPv6 and MAC addresses.
+    """
+    assert isinstance(family, enum.IntEnum), family
+    if family == socket.AF_INET:
+        octs = [int(x) for x in addr.split('.')]
+        assert len(octs) == 4, addr
+        for num in octs:
+            assert 0 <= num <= 255, addr
+        ipaddress.IPv4Address(addr)
+    elif family == socket.AF_INET6:
+        assert isinstance(addr, str), addr
+        ipaddress.IPv6Address(addr)
+    elif family == psutil.AF_LINK:
+        assert re.match(r'([a-fA-F0-9]{2}[:|\-]?){6}', addr) is not None, addr
+    else:
+        raise ValueError(f"unknown family {family!r}")
+
+
+def check_connection_ntuple(conn):
+    """Check validity of a connection namedtuple."""
+
+    def check_ntuple(conn):
+        has_pid = len(conn) == 7
+        assert len(conn) in {6, 7}, len(conn)
+        assert conn[0] == conn.fd, conn.fd
+        assert conn[1] == conn.family, conn.family
+        assert conn[2] == conn.type, conn.type
+        assert conn[3] == conn.laddr, conn.laddr
+        assert conn[4] == conn.raddr, conn.raddr
+        assert conn[5] == conn.status, conn.status
+        if has_pid:
+            assert conn[6] == conn.pid, conn.pid
+
+    def check_family(conn):
+        assert conn.family in {AF_INET, AF_INET6, AF_UNIX}, conn.family
+        assert isinstance(conn.family, enum.IntEnum), conn
+        if conn.family == AF_INET:
+            # actually try to bind the local socket; ignore IPv6
+            # sockets as their address might be represented as
+            # an IPv4-mapped-address (e.g. "::127.0.0.1")
+            # and that's rejected by bind()
+            with socket.socket(conn.family, conn.type) as s:
+                try:
+                    s.bind((conn.laddr[0], 0))
+                except OSError as err:
+                    if err.errno != errno.EADDRNOTAVAIL:
+                        raise
+        elif conn.family == AF_UNIX:
+            assert conn.status == psutil.CONN_NONE, conn.status
+
+    def check_type(conn):
+        # SOCK_SEQPACKET may happen in case of AF_UNIX socks
+        SOCK_SEQPACKET = getattr(socket, "SOCK_SEQPACKET", object())
+        assert conn.type in {
+            socket.SOCK_STREAM,
+            socket.SOCK_DGRAM,
+            SOCK_SEQPACKET,
+        }, conn.type
+        assert isinstance(conn.type, enum.IntEnum), conn
+        if conn.type == socket.SOCK_DGRAM:
+            assert conn.status == psutil.CONN_NONE, conn.status
+
+    def check_addrs(conn):
+        # check IP address and port sanity
+        for addr in (conn.laddr, conn.raddr):
+            if conn.family in {AF_INET, AF_INET6}:
+                assert isinstance(addr, tuple), type(addr)
+                if not addr:
+                    continue
+                assert isinstance(addr.port, int), type(addr.port)
+                assert 0 <= addr.port <= 65535, addr.port
+                check_net_address(addr.ip, conn.family)
+            elif conn.family == AF_UNIX:
+                assert isinstance(addr, str), type(addr)
+
+    def check_status(conn):
+        assert isinstance(conn.status, str), conn.status
+        valids = [
+            getattr(psutil, x) for x in dir(psutil) if x.startswith('CONN_')
+        ]
+        assert conn.status in valids, conn.status
+        if conn.family in {AF_INET, AF_INET6} and conn.type == SOCK_STREAM:
+            assert conn.status != psutil.CONN_NONE, conn.status
+        else:
+            assert conn.status == psutil.CONN_NONE, conn.status
+
+    check_ntuple(conn)
+    check_family(conn)
+    check_type(conn)
+    check_addrs(conn)
+    check_status(conn)
+
+
+def filter_proc_net_connections(cons):
+    """Our process may start with some open UNIX sockets which are not
+    initialized by us, invalidating unit tests.
+    """
+    new = []
+    for conn in cons:
+        if POSIX and conn.family == socket.AF_UNIX:
+            if MACOS and "/syslog" in conn.raddr:
+                debug(f"skipping {conn}")
+                continue
+        new.append(conn)
+    return new
+
+
+# ===================================================================
+# --- import utils
+# ===================================================================
+
+
+def reload_module(module):
+    return importlib.reload(module)
+
+
+def import_module_by_path(path):
+    name = os.path.splitext(os.path.basename(path))[0]
+    spec = importlib.util.spec_from_file_location(name, path)
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod
+
+
+# ===================================================================
+# --- others
+# ===================================================================
+
+
+def warn(msg):
+    """Raise a warning msg."""
+    warnings.warn(msg, UserWarning, stacklevel=2)
+
+
+def is_namedtuple(x):
+    """Check if object is an instance of namedtuple."""
+    t = type(x)
+    b = t.__bases__
+    if len(b) != 1 or b[0] is not tuple:
+        return False
+    f = getattr(t, '_fields', None)
+    if not isinstance(f, tuple):
+        return False
+    return all(isinstance(n, str) for n in f)
+
+
+if POSIX:
+
+    @contextlib.contextmanager
+    def copyload_shared_lib(suffix=""):
+        """Ctx manager which picks up a random shared CO lib used
+        by this process, copies it in another location and loads it
+        in memory via ctypes. Return the new absolutized path.
+        """
+        exe = 'pypy' if PYPY else 'python'
+        ext = ".so"
+        dst = get_testfn(suffix=suffix + ext)
+        libs = [
+            x.path
+            for x in psutil.Process().memory_maps()
+            if os.path.splitext(x.path)[1] == ext and exe in x.path.lower()
+        ]
+        src = random.choice(libs)
+        shutil.copyfile(src, dst)
+        try:
+            ctypes.CDLL(dst)
+            yield dst
+        finally:
+            safe_rmpath(dst)
+
+else:
+
+    @contextlib.contextmanager
+    def copyload_shared_lib(suffix=""):
+        """Ctx manager which picks up a random shared DLL lib used
+        by this process, copies it in another location and loads it
+        in memory via ctypes.
+        Return the new absolutized, normcased path.
+        """
+        from ctypes import WinError
+        from ctypes import wintypes
+
+        ext = ".dll"
+        dst = get_testfn(suffix=suffix + ext)
+        libs = [
+            x.path
+            for x in psutil.Process().memory_maps()
+            if x.path.lower().endswith(ext)
+            and 'python' in os.path.basename(x.path).lower()
+            and 'wow64' not in x.path.lower()
+        ]
+        if PYPY and not libs:
+            libs = [
+                x.path
+                for x in psutil.Process().memory_maps()
+                if 'pypy' in os.path.basename(x.path).lower()
+            ]
+        src = random.choice(libs)
+        shutil.copyfile(src, dst)
+        cfile = None
+        try:
+            cfile = ctypes.WinDLL(dst)
+            yield dst
+        finally:
+            # Work around OverflowError:
+            # - https://ci.appveyor.com/project/giampaolo/psutil/build/1207/
+            #       job/o53330pbnri9bcw7
+            # - http://bugs.python.org/issue30286
+            # - http://stackoverflow.com/questions/23522055
+            if cfile is not None:
+                FreeLibrary = ctypes.windll.kernel32.FreeLibrary
+                FreeLibrary.argtypes = [wintypes.HMODULE]
+                ret = FreeLibrary(cfile._handle)
+                if ret == 0:
+                    raise WinError()
+            safe_rmpath(dst)
+
+
+# ===================================================================
+# --- Exit funs (first is executed last)
+# ===================================================================
+
+
+# this is executed first
+@atexit.register
+def cleanup_test_procs():
+    reap_children(recursive=True)
+
+
+# atexit module does not execute exit functions in case of SIGTERM, which
+# gets sent to test subprocesses, which is a problem if they import this
+# module. With this it will. See:
+# https://gmpy.dev/blog/2016/how-to-always-execute-exit-functions-in-python
+if POSIX:
+    signal.signal(signal.SIGTERM, lambda sig, _: sys.exit(sig))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/__main__.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce6fc24c7f09a273983c81d9ae5b2180e83fbb9f
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/__main__.py
@@ -0,0 +1,12 @@
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Run unit tests. This is invoked by:
+$ python -m psutil.tests.
+"""
+
+from psutil.tests import pytest
+
+
+pytest.main(["-v", "-s", "--tb=short"])
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+size 134965
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index 0000000000000000000000000000000000000000..10934c12d41ee19b641fca103bee992b9336fc16
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_aix.py
@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'
+# Copyright (c) 2017, Arnon Yaari
+# All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""AIX specific tests."""
+
+import re
+
+import psutil
+from psutil import AIX
+from psutil.tests import PsutilTestCase
+from psutil.tests import pytest
+from psutil.tests import sh
+
+
+@pytest.mark.skipif(not AIX, reason="AIX only")
+class AIXSpecificTestCase(PsutilTestCase):
+    def test_virtual_memory(self):
+        out = sh('/usr/bin/svmon -O unit=KB')
+        re_pattern = r"memory\s*"
+        for field in [
+            "size",
+            "inuse",
+            "free",
+            "pin",
+            "virtual",
+            "available",
+            "mmode",
+        ]:
+            re_pattern += rf"(?P<{field}>\S+)\s+"
+        matchobj = re.search(re_pattern, out)
+
+        assert matchobj is not None
+
+        KB = 1024
+        total = int(matchobj.group("size")) * KB
+        available = int(matchobj.group("available")) * KB
+        used = int(matchobj.group("inuse")) * KB
+        free = int(matchobj.group("free")) * KB
+
+        psutil_result = psutil.virtual_memory()
+
+        # TOLERANCE_SYS_MEM from psutil.tests is not enough. For some reason
+        # we're seeing differences of ~1.2 MB. 2 MB is still a good tolerance
+        # when compared to GBs.
+        TOLERANCE_SYS_MEM = 2 * KB * KB  # 2 MB
+        assert psutil_result.total == total
+        assert abs(psutil_result.used - used) < TOLERANCE_SYS_MEM
+        assert abs(psutil_result.available - available) < TOLERANCE_SYS_MEM
+        assert abs(psutil_result.free - free) < TOLERANCE_SYS_MEM
+
+    def test_swap_memory(self):
+        out = sh('/usr/sbin/lsps -a')
+        # From the man page, "The size is given in megabytes" so we assume
+        # we'll always have 'MB' in the result
+        # TODO maybe try to use "swap -l" to check "used" too, but its units
+        # are not guaranteed to be "MB" so parsing may not be consistent
+        matchobj = re.search(
+            r"(?P<space>\S+)\s+"
+            r"(?P<vol>\S+)\s+"
+            r"(?P<vg>\S+)\s+"
+            r"(?P<size>\d+)MB",
+            out,
+        )
+
+        assert matchobj is not None
+
+        total_mb = int(matchobj.group("size"))
+        MB = 1024**2
+        psutil_result = psutil.swap_memory()
+        # we divide our result by MB instead of multiplying the lsps value by
+        # MB because lsps may round down, so we round down too
+        assert int(psutil_result.total / MB) == total_mb
+
+    def test_cpu_stats(self):
+        out = sh('/usr/bin/mpstat -a')
+
+        re_pattern = r"ALL\s*"
+        for field in [
+            "min",
+            "maj",
+            "mpcs",
+            "mpcr",
+            "dev",
+            "soft",
+            "dec",
+            "ph",
+            "cs",
+            "ics",
+            "bound",
+            "rq",
+            "push",
+            "S3pull",
+            "S3grd",
+            "S0rd",
+            "S1rd",
+            "S2rd",
+            "S3rd",
+            "S4rd",
+            "S5rd",
+            "sysc",
+        ]:
+            re_pattern += rf"(?P<{field}>\S+)\s+"
+        matchobj = re.search(re_pattern, out)
+
+        assert matchobj is not None
+
+        # numbers are usually in the millions so 1000 is ok for tolerance
+        CPU_STATS_TOLERANCE = 1000
+        psutil_result = psutil.cpu_stats()
+        assert (
+            abs(psutil_result.ctx_switches - int(matchobj.group("cs")))
+            < CPU_STATS_TOLERANCE
+        )
+        assert (
+            abs(psutil_result.syscalls - int(matchobj.group("sysc")))
+            < CPU_STATS_TOLERANCE
+        )
+        assert (
+            abs(psutil_result.interrupts - int(matchobj.group("dev")))
+            < CPU_STATS_TOLERANCE
+        )
+        assert (
+            abs(psutil_result.soft_interrupts - int(matchobj.group("soft")))
+            < CPU_STATS_TOLERANCE
+        )
+
+    def test_cpu_count_logical(self):
+        out = sh('/usr/bin/mpstat -a')
+        mpstat_lcpu = int(re.search(r"lcpu=(\d+)", out).group(1))
+        psutil_lcpu = psutil.cpu_count(logical=True)
+        assert mpstat_lcpu == psutil_lcpu
+
+    def test_net_if_addrs_names(self):
+        out = sh('/etc/ifconfig -l')
+        ifconfig_names = set(out.split())
+        psutil_names = set(psutil.net_if_addrs().keys())
+        assert ifconfig_names == psutil_names
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_bsd.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_bsd.py
new file mode 100644
index 0000000000000000000000000000000000000000..2786c348577855207cc4169714721bebb8776828
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_bsd.py
@@ -0,0 +1,593 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+# TODO: (FreeBSD) add test for comparing connections with 'sockstat' cmd.
+
+
+"""Tests specific to all BSD platforms."""
+
+import datetime
+import os
+import re
+import shutil
+import time
+
+import psutil
+from psutil import BSD
+from psutil import FREEBSD
+from psutil import NETBSD
+from psutil import OPENBSD
+from psutil.tests import HAS_BATTERY
+from psutil.tests import TOLERANCE_SYS_MEM
+from psutil.tests import PsutilTestCase
+from psutil.tests import pytest
+from psutil.tests import retry_on_failure
+from psutil.tests import sh
+from psutil.tests import spawn_testproc
+from psutil.tests import terminate
+
+
+if BSD:
+    from psutil._psutil_posix import getpagesize
+
+    PAGESIZE = getpagesize()
+    # muse requires root privileges
+    MUSE_AVAILABLE = os.getuid() == 0 and shutil.which("muse")
+else:
+    PAGESIZE = None
+    MUSE_AVAILABLE = False
+
+
+def sysctl(cmdline):
+    """Expects a sysctl command with an argument and parse the result
+    returning only the value of interest.
+    """
+    result = sh("sysctl " + cmdline)
+    if FREEBSD:
+        result = result[result.find(": ") + 2 :]
+    elif OPENBSD or NETBSD:
+        result = result[result.find("=") + 1 :]
+    try:
+        return int(result)
+    except ValueError:
+        return result
+
+
+def muse(field):
+    """Thin wrapper around 'muse' cmdline utility."""
+    out = sh('muse')
+    for line in out.split('\n'):
+        if line.startswith(field):
+            break
+    else:
+        raise ValueError("line not found")
+    return int(line.split()[1])
+
+
+# =====================================================================
+# --- All BSD*
+# =====================================================================
+
+
+@pytest.mark.skipif(not BSD, reason="BSD only")
+class BSDTestCase(PsutilTestCase):
+    """Generic tests common to all BSD variants."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.pid = spawn_testproc().pid
+
+    @classmethod
+    def tearDownClass(cls):
+        terminate(cls.pid)
+
+    @pytest.mark.skipif(NETBSD, reason="-o lstart doesn't work on NETBSD")
+    def test_process_create_time(self):
+        output = sh(f"ps -o lstart -p {self.pid}")
+        start_ps = output.replace('STARTED', '').strip()
+        start_psutil = psutil.Process(self.pid).create_time()
+        start_psutil = time.strftime(
+            "%a %b %e %H:%M:%S %Y", time.localtime(start_psutil)
+        )
+        assert start_ps == start_psutil
+
+    def test_disks(self):
+        # test psutil.disk_usage() and psutil.disk_partitions()
+        # against "df -a"
+        def df(path):
+            out = sh(f'df -k "{path}"').strip()
+            lines = out.split('\n')
+            lines.pop(0)
+            line = lines.pop(0)
+            dev, total, used, free = line.split()[:4]
+            if dev == 'none':
+                dev = ''
+            total = int(total) * 1024
+            used = int(used) * 1024
+            free = int(free) * 1024
+            return dev, total, used, free
+
+        for part in psutil.disk_partitions(all=False):
+            usage = psutil.disk_usage(part.mountpoint)
+            dev, total, used, free = df(part.mountpoint)
+            assert part.device == dev
+            assert usage.total == total
+            # 10 MB tolerance
+            if abs(usage.free - free) > 10 * 1024 * 1024:
+                raise self.fail(f"psutil={usage.free}, df={free}")
+            if abs(usage.used - used) > 10 * 1024 * 1024:
+                raise self.fail(f"psutil={usage.used}, df={used}")
+
+    @pytest.mark.skipif(
+        not shutil.which("sysctl"), reason="sysctl cmd not available"
+    )
+    def test_cpu_count_logical(self):
+        syst = sysctl("hw.ncpu")
+        assert psutil.cpu_count(logical=True) == syst
+
+    @pytest.mark.skipif(
+        not shutil.which("sysctl"), reason="sysctl cmd not available"
+    )
+    @pytest.mark.skipif(
+        NETBSD, reason="skipped on NETBSD"  # we check /proc/meminfo
+    )
+    def test_virtual_memory_total(self):
+        num = sysctl('hw.physmem')
+        assert num == psutil.virtual_memory().total
+
+    @pytest.mark.skipif(
+        not shutil.which("ifconfig"), reason="ifconfig cmd not available"
+    )
+    def test_net_if_stats(self):
+        for name, stats in psutil.net_if_stats().items():
+            try:
+                out = sh(f"ifconfig {name}")
+            except RuntimeError:
+                pass
+            else:
+                assert stats.isup == ('RUNNING' in out)
+                if "mtu" in out:
+                    assert stats.mtu == int(re.findall(r'mtu (\d+)', out)[0])
+
+
+# =====================================================================
+# --- FreeBSD
+# =====================================================================
+
+
+@pytest.mark.skipif(not FREEBSD, reason="FREEBSD only")
+class FreeBSDPsutilTestCase(PsutilTestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.pid = spawn_testproc().pid
+
+    @classmethod
+    def tearDownClass(cls):
+        terminate(cls.pid)
+
+    @retry_on_failure()
+    def test_memory_maps(self):
+        out = sh(f"procstat -v {self.pid}")
+        maps = psutil.Process(self.pid).memory_maps(grouped=False)
+        lines = out.split('\n')[1:]
+        while lines:
+            line = lines.pop()
+            fields = line.split()
+            _, start, stop, _perms, res = fields[:5]
+            map = maps.pop()
+            assert f"{start}-{stop}" == map.addr
+            assert int(res) == map.rss
+            if not map.path.startswith('['):
+                assert fields[10] == map.path
+
+    def test_exe(self):
+        out = sh(f"procstat -b {self.pid}")
+        assert psutil.Process(self.pid).exe() == out.split('\n')[1].split()[-1]
+
+    def test_cmdline(self):
+        out = sh(f"procstat -c {self.pid}")
+        assert ' '.join(psutil.Process(self.pid).cmdline()) == ' '.join(
+            out.split('\n')[1].split()[2:]
+        )
+
+    def test_uids_gids(self):
+        out = sh(f"procstat -s {self.pid}")
+        euid, ruid, suid, egid, rgid, sgid = out.split('\n')[1].split()[2:8]
+        p = psutil.Process(self.pid)
+        uids = p.uids()
+        gids = p.gids()
+        assert uids.real == int(ruid)
+        assert uids.effective == int(euid)
+        assert uids.saved == int(suid)
+        assert gids.real == int(rgid)
+        assert gids.effective == int(egid)
+        assert gids.saved == int(sgid)
+
+    @retry_on_failure()
+    def test_ctx_switches(self):
+        tested = []
+        out = sh(f"procstat -r {self.pid}")
+        p = psutil.Process(self.pid)
+        for line in out.split('\n'):
+            line = line.lower().strip()
+            if ' voluntary context' in line:
+                pstat_value = int(line.split()[-1])
+                psutil_value = p.num_ctx_switches().voluntary
+                assert pstat_value == psutil_value
+                tested.append(None)
+            elif ' involuntary context' in line:
+                pstat_value = int(line.split()[-1])
+                psutil_value = p.num_ctx_switches().involuntary
+                assert pstat_value == psutil_value
+                tested.append(None)
+        if len(tested) != 2:
+            raise RuntimeError("couldn't find lines match in procstat out")
+
+    @retry_on_failure()
+    def test_cpu_times(self):
+        tested = []
+        out = sh(f"procstat -r {self.pid}")
+        p = psutil.Process(self.pid)
+        for line in out.split('\n'):
+            line = line.lower().strip()
+            if 'user time' in line:
+                pstat_value = float('0.' + line.split()[-1].split('.')[-1])
+                psutil_value = p.cpu_times().user
+                assert pstat_value == psutil_value
+                tested.append(None)
+            elif 'system time' in line:
+                pstat_value = float('0.' + line.split()[-1].split('.')[-1])
+                psutil_value = p.cpu_times().system
+                assert pstat_value == psutil_value
+                tested.append(None)
+        if len(tested) != 2:
+            raise RuntimeError("couldn't find lines match in procstat out")
+
+
+@pytest.mark.skipif(not FREEBSD, reason="FREEBSD only")
+class FreeBSDSystemTestCase(PsutilTestCase):
+    @staticmethod
+    def parse_swapinfo():
+        # the last line is always the total
+        output = sh("swapinfo -k").splitlines()[-1]
+        parts = re.split(r'\s+', output)
+
+        if not parts:
+            raise ValueError(f"Can't parse swapinfo: {output}")
+
+        # the size is in 1k units, so multiply by 1024
+        total, used, free = (int(p) * 1024 for p in parts[1:4])
+        return total, used, free
+
+    def test_cpu_frequency_against_sysctl(self):
+        # Currently only cpu 0 is frequency is supported in FreeBSD
+        # All other cores use the same frequency.
+        sensor = "dev.cpu.0.freq"
+        try:
+            sysctl_result = int(sysctl(sensor))
+        except RuntimeError:
+            raise pytest.skip("frequencies not supported by kernel")
+        assert psutil.cpu_freq().current == sysctl_result
+
+        sensor = "dev.cpu.0.freq_levels"
+        sysctl_result = sysctl(sensor)
+        # sysctl returns a string of the format:
+        # <freq_level_1>/<voltage_level_1> <freq_level_2>/<voltage_level_2>...
+        # Ordered highest available to lowest available.
+        max_freq = int(sysctl_result.split()[0].split("/")[0])
+        min_freq = int(sysctl_result.split()[-1].split("/")[0])
+        assert psutil.cpu_freq().max == max_freq
+        assert psutil.cpu_freq().min == min_freq
+
+    # --- virtual_memory(); tests against sysctl
+
+    @retry_on_failure()
+    def test_vmem_active(self):
+        syst = sysctl("vm.stats.vm.v_active_count") * PAGESIZE
+        assert abs(psutil.virtual_memory().active - syst) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_vmem_inactive(self):
+        syst = sysctl("vm.stats.vm.v_inactive_count") * PAGESIZE
+        assert abs(psutil.virtual_memory().inactive - syst) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_vmem_wired(self):
+        syst = sysctl("vm.stats.vm.v_wire_count") * PAGESIZE
+        assert abs(psutil.virtual_memory().wired - syst) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_vmem_cached(self):
+        syst = sysctl("vm.stats.vm.v_cache_count") * PAGESIZE
+        assert abs(psutil.virtual_memory().cached - syst) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_vmem_free(self):
+        syst = sysctl("vm.stats.vm.v_free_count") * PAGESIZE
+        assert abs(psutil.virtual_memory().free - syst) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_vmem_buffers(self):
+        syst = sysctl("vfs.bufspace")
+        assert abs(psutil.virtual_memory().buffers - syst) < TOLERANCE_SYS_MEM
+
+    # --- virtual_memory(); tests against muse
+
+    @pytest.mark.skipif(not MUSE_AVAILABLE, reason="muse not installed")
+    def test_muse_vmem_total(self):
+        num = muse('Total')
+        assert psutil.virtual_memory().total == num
+
+    @pytest.mark.skipif(not MUSE_AVAILABLE, reason="muse not installed")
+    @retry_on_failure()
+    def test_muse_vmem_active(self):
+        num = muse('Active')
+        assert abs(psutil.virtual_memory().active - num) < TOLERANCE_SYS_MEM
+
+    @pytest.mark.skipif(not MUSE_AVAILABLE, reason="muse not installed")
+    @retry_on_failure()
+    def test_muse_vmem_inactive(self):
+        num = muse('Inactive')
+        assert abs(psutil.virtual_memory().inactive - num) < TOLERANCE_SYS_MEM
+
+    @pytest.mark.skipif(not MUSE_AVAILABLE, reason="muse not installed")
+    @retry_on_failure()
+    def test_muse_vmem_wired(self):
+        num = muse('Wired')
+        assert abs(psutil.virtual_memory().wired - num) < TOLERANCE_SYS_MEM
+
+    @pytest.mark.skipif(not MUSE_AVAILABLE, reason="muse not installed")
+    @retry_on_failure()
+    def test_muse_vmem_cached(self):
+        num = muse('Cache')
+        assert abs(psutil.virtual_memory().cached - num) < TOLERANCE_SYS_MEM
+
+    @pytest.mark.skipif(not MUSE_AVAILABLE, reason="muse not installed")
+    @retry_on_failure()
+    def test_muse_vmem_free(self):
+        num = muse('Free')
+        assert abs(psutil.virtual_memory().free - num) < TOLERANCE_SYS_MEM
+
+    @pytest.mark.skipif(not MUSE_AVAILABLE, reason="muse not installed")
+    @retry_on_failure()
+    def test_muse_vmem_buffers(self):
+        num = muse('Buffer')
+        assert abs(psutil.virtual_memory().buffers - num) < TOLERANCE_SYS_MEM
+
+    def test_cpu_stats_ctx_switches(self):
+        assert (
+            abs(
+                psutil.cpu_stats().ctx_switches
+                - sysctl('vm.stats.sys.v_swtch')
+            )
+            < 1000
+        )
+
+    def test_cpu_stats_interrupts(self):
+        assert (
+            abs(psutil.cpu_stats().interrupts - sysctl('vm.stats.sys.v_intr'))
+            < 1000
+        )
+
+    def test_cpu_stats_soft_interrupts(self):
+        assert (
+            abs(
+                psutil.cpu_stats().soft_interrupts
+                - sysctl('vm.stats.sys.v_soft')
+            )
+            < 1000
+        )
+
+    @retry_on_failure()
+    def test_cpu_stats_syscalls(self):
+        # pretty high tolerance but it looks like it's OK.
+        assert (
+            abs(psutil.cpu_stats().syscalls - sysctl('vm.stats.sys.v_syscall'))
+            < 200000
+        )
+
+    # def test_cpu_stats_traps(self):
+    #    self.assertAlmostEqual(psutil.cpu_stats().traps,
+    #                           sysctl('vm.stats.sys.v_trap'), delta=1000)
+
+    # --- swap memory
+
+    def test_swapmem_free(self):
+        _total, _used, free = self.parse_swapinfo()
+        assert abs(psutil.swap_memory().free - free) < TOLERANCE_SYS_MEM
+
+    def test_swapmem_used(self):
+        _total, used, _free = self.parse_swapinfo()
+        assert abs(psutil.swap_memory().used - used) < TOLERANCE_SYS_MEM
+
+    def test_swapmem_total(self):
+        total, _used, _free = self.parse_swapinfo()
+        assert abs(psutil.swap_memory().total - total) < TOLERANCE_SYS_MEM
+
+    # --- others
+
+    def test_boot_time(self):
+        s = sysctl('sysctl kern.boottime')
+        s = s[s.find(" sec = ") + 7 :]
+        s = s[: s.find(',')]
+        btime = int(s)
+        assert btime == psutil.boot_time()
+
+    # --- sensors_battery
+
+    @pytest.mark.skipif(not HAS_BATTERY, reason="no battery")
+    def test_sensors_battery(self):
+        def secs2hours(secs):
+            m, _s = divmod(secs, 60)
+            h, m = divmod(m, 60)
+            return f"{int(h)}:{int(m):02}"
+
+        out = sh("acpiconf -i 0")
+        fields = {x.split('\t')[0]: x.split('\t')[-1] for x in out.split("\n")}
+        metrics = psutil.sensors_battery()
+        percent = int(fields['Remaining capacity:'].replace('%', ''))
+        remaining_time = fields['Remaining time:']
+        assert metrics.percent == percent
+        if remaining_time == 'unknown':
+            assert metrics.secsleft == psutil.POWER_TIME_UNLIMITED
+        else:
+            assert secs2hours(metrics.secsleft) == remaining_time
+
+    @pytest.mark.skipif(not HAS_BATTERY, reason="no battery")
+    def test_sensors_battery_against_sysctl(self):
+        assert psutil.sensors_battery().percent == sysctl(
+            "hw.acpi.battery.life"
+        )
+        assert psutil.sensors_battery().power_plugged == (
+            sysctl("hw.acpi.acline") == 1
+        )
+        secsleft = psutil.sensors_battery().secsleft
+        if secsleft < 0:
+            assert sysctl("hw.acpi.battery.time") == -1
+        else:
+            assert secsleft == sysctl("hw.acpi.battery.time") * 60
+
+    @pytest.mark.skipif(HAS_BATTERY, reason="has battery")
+    def test_sensors_battery_no_battery(self):
+        # If no battery is present one of these calls is supposed
+        # to fail, see:
+        # https://github.com/giampaolo/psutil/issues/1074
+        with pytest.raises(RuntimeError):
+            sysctl("hw.acpi.battery.life")
+            sysctl("hw.acpi.battery.time")
+            sysctl("hw.acpi.acline")
+        assert psutil.sensors_battery() is None
+
+    # --- sensors_temperatures
+
+    def test_sensors_temperatures_against_sysctl(self):
+        num_cpus = psutil.cpu_count(True)
+        for cpu in range(num_cpus):
+            sensor = f"dev.cpu.{cpu}.temperature"
+            # sysctl returns a string in the format 46.0C
+            try:
+                sysctl_result = int(float(sysctl(sensor)[:-1]))
+            except RuntimeError:
+                raise pytest.skip("temperatures not supported by kernel")
+            assert (
+                abs(
+                    psutil.sensors_temperatures()["coretemp"][cpu].current
+                    - sysctl_result
+                )
+                < 10
+            )
+
+            sensor = f"dev.cpu.{cpu}.coretemp.tjmax"
+            sysctl_result = int(float(sysctl(sensor)[:-1]))
+            assert (
+                psutil.sensors_temperatures()["coretemp"][cpu].high
+                == sysctl_result
+            )
+
+
+# =====================================================================
+# --- OpenBSD
+# =====================================================================
+
+
+@pytest.mark.skipif(not OPENBSD, reason="OPENBSD only")
+class OpenBSDTestCase(PsutilTestCase):
+    def test_boot_time(self):
+        s = sysctl('kern.boottime')
+        sys_bt = datetime.datetime.strptime(s, "%a %b %d %H:%M:%S %Y")
+        psutil_bt = datetime.datetime.fromtimestamp(psutil.boot_time())
+        assert sys_bt == psutil_bt
+
+
+# =====================================================================
+# --- NetBSD
+# =====================================================================
+
+
+@pytest.mark.skipif(not NETBSD, reason="NETBSD only")
+class NetBSDTestCase(PsutilTestCase):
+    @staticmethod
+    def parse_meminfo(look_for):
+        with open('/proc/meminfo') as f:
+            for line in f:
+                if line.startswith(look_for):
+                    return int(line.split()[1]) * 1024
+        raise ValueError(f"can't find {look_for}")
+
+    # --- virtual mem
+
+    def test_vmem_total(self):
+        assert psutil.virtual_memory().total == self.parse_meminfo("MemTotal:")
+
+    def test_vmem_free(self):
+        assert (
+            abs(psutil.virtual_memory().free - self.parse_meminfo("MemFree:"))
+            < TOLERANCE_SYS_MEM
+        )
+
+    def test_vmem_buffers(self):
+        assert (
+            abs(
+                psutil.virtual_memory().buffers
+                - self.parse_meminfo("Buffers:")
+            )
+            < TOLERANCE_SYS_MEM
+        )
+
+    def test_vmem_shared(self):
+        assert (
+            abs(
+                psutil.virtual_memory().shared
+                - self.parse_meminfo("MemShared:")
+            )
+            < TOLERANCE_SYS_MEM
+        )
+
+    def test_vmem_cached(self):
+        assert (
+            abs(psutil.virtual_memory().cached - self.parse_meminfo("Cached:"))
+            < TOLERANCE_SYS_MEM
+        )
+
+    # --- swap mem
+
+    def test_swapmem_total(self):
+        assert (
+            abs(psutil.swap_memory().total - self.parse_meminfo("SwapTotal:"))
+            < TOLERANCE_SYS_MEM
+        )
+
+    def test_swapmem_free(self):
+        assert (
+            abs(psutil.swap_memory().free - self.parse_meminfo("SwapFree:"))
+            < TOLERANCE_SYS_MEM
+        )
+
+    def test_swapmem_used(self):
+        smem = psutil.swap_memory()
+        assert smem.used == smem.total - smem.free
+
+    # --- others
+
+    def test_cpu_stats_interrupts(self):
+        with open('/proc/stat', 'rb') as f:
+            for line in f:
+                if line.startswith(b'intr'):
+                    interrupts = int(line.split()[1])
+                    break
+            else:
+                raise ValueError("couldn't find line")
+        assert abs(psutil.cpu_stats().interrupts - interrupts) < 1000
+
+    def test_cpu_stats_ctx_switches(self):
+        with open('/proc/stat', 'rb') as f:
+            for line in f:
+                if line.startswith(b'ctxt'):
+                    ctx_switches = int(line.split()[1])
+                    break
+            else:
+                raise ValueError("couldn't find line")
+        assert abs(psutil.cpu_stats().ctx_switches - ctx_switches) < 1000
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_connections.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_connections.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ddeb855f2a78ba21132c2694b6f426f548cbb48
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_connections.py
@@ -0,0 +1,566 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Tests for psutil.net_connections() and Process.net_connections() APIs."""
+
+import os
+import socket
+import textwrap
+from contextlib import closing
+from socket import AF_INET
+from socket import AF_INET6
+from socket import SOCK_DGRAM
+from socket import SOCK_STREAM
+
+import psutil
+from psutil import FREEBSD
+from psutil import LINUX
+from psutil import MACOS
+from psutil import NETBSD
+from psutil import OPENBSD
+from psutil import POSIX
+from psutil import SUNOS
+from psutil import WINDOWS
+from psutil._common import supports_ipv6
+from psutil.tests import AF_UNIX
+from psutil.tests import HAS_NET_CONNECTIONS_UNIX
+from psutil.tests import SKIP_SYSCONS
+from psutil.tests import PsutilTestCase
+from psutil.tests import bind_socket
+from psutil.tests import bind_unix_socket
+from psutil.tests import check_connection_ntuple
+from psutil.tests import create_sockets
+from psutil.tests import filter_proc_net_connections
+from psutil.tests import pytest
+from psutil.tests import reap_children
+from psutil.tests import retry_on_failure
+from psutil.tests import skip_on_access_denied
+from psutil.tests import tcp_socketpair
+from psutil.tests import unix_socketpair
+from psutil.tests import wait_for_file
+
+
+SOCK_SEQPACKET = getattr(socket, "SOCK_SEQPACKET", object())
+
+
+def this_proc_net_connections(kind):
+    cons = psutil.Process().net_connections(kind=kind)
+    if kind in {"all", "unix"}:
+        return filter_proc_net_connections(cons)
+    return cons
+
+
+@pytest.mark.xdist_group(name="serial")
+class ConnectionTestCase(PsutilTestCase):
+    def setUp(self):
+        assert this_proc_net_connections(kind='all') == []
+
+    def tearDown(self):
+        # Make sure we closed all resources.
+        assert this_proc_net_connections(kind='all') == []
+
+    def compare_procsys_connections(self, pid, proc_cons, kind='all'):
+        """Given a process PID and its list of connections compare
+        those against system-wide connections retrieved via
+        psutil.net_connections.
+        """
+        try:
+            sys_cons = psutil.net_connections(kind=kind)
+        except psutil.AccessDenied:
+            # On MACOS, system-wide connections are retrieved by iterating
+            # over all processes
+            if MACOS:
+                return
+            else:
+                raise
+        # Filter for this proc PID and exlucde PIDs from the tuple.
+        sys_cons = [c[:-1] for c in sys_cons if c.pid == pid]
+        sys_cons.sort()
+        proc_cons.sort()
+        assert proc_cons == sys_cons
+
+
+class TestBasicOperations(ConnectionTestCase):
+    @pytest.mark.skipif(SKIP_SYSCONS, reason="requires root")
+    def test_system(self):
+        with create_sockets():
+            for conn in psutil.net_connections(kind='all'):
+                check_connection_ntuple(conn)
+
+    def test_process(self):
+        with create_sockets():
+            for conn in this_proc_net_connections(kind='all'):
+                check_connection_ntuple(conn)
+
+    def test_invalid_kind(self):
+        with pytest.raises(ValueError):
+            this_proc_net_connections(kind='???')
+        with pytest.raises(ValueError):
+            psutil.net_connections(kind='???')
+
+
+@pytest.mark.xdist_group(name="serial")
+class TestUnconnectedSockets(ConnectionTestCase):
+    """Tests sockets which are open but not connected to anything."""
+
+    def get_conn_from_sock(self, sock):
+        cons = this_proc_net_connections(kind='all')
+        smap = {c.fd: c for c in cons}
+        if NETBSD or FREEBSD:
+            # NetBSD opens a UNIX socket to /var/log/run
+            # so there may be more connections.
+            return smap[sock.fileno()]
+        else:
+            assert len(cons) == 1
+            if cons[0].fd != -1:
+                assert smap[sock.fileno()].fd == sock.fileno()
+            return cons[0]
+
+    def check_socket(self, sock):
+        """Given a socket, makes sure it matches the one obtained
+        via psutil. It assumes this process created one connection
+        only (the one supposed to be checked).
+        """
+        conn = self.get_conn_from_sock(sock)
+        check_connection_ntuple(conn)
+
+        # fd, family, type
+        if conn.fd != -1:
+            assert conn.fd == sock.fileno()
+        assert conn.family == sock.family
+        # see: http://bugs.python.org/issue30204
+        assert conn.type == sock.getsockopt(socket.SOL_SOCKET, socket.SO_TYPE)
+
+        # local address
+        laddr = sock.getsockname()
+        if not laddr and isinstance(laddr, bytes):
+            # See: http://bugs.python.org/issue30205
+            laddr = laddr.decode()
+        if sock.family == AF_INET6:
+            laddr = laddr[:2]
+        assert conn.laddr == laddr
+
+        # XXX Solaris can't retrieve system-wide UNIX sockets
+        if sock.family == AF_UNIX and HAS_NET_CONNECTIONS_UNIX:
+            cons = this_proc_net_connections(kind='all')
+            self.compare_procsys_connections(os.getpid(), cons, kind='all')
+        return conn
+
+    def test_tcp_v4(self):
+        addr = ("127.0.0.1", 0)
+        with closing(bind_socket(AF_INET, SOCK_STREAM, addr=addr)) as sock:
+            conn = self.check_socket(sock)
+            assert conn.raddr == ()
+            assert conn.status == psutil.CONN_LISTEN
+
+    @pytest.mark.skipif(not supports_ipv6(), reason="IPv6 not supported")
+    def test_tcp_v6(self):
+        addr = ("::1", 0)
+        with closing(bind_socket(AF_INET6, SOCK_STREAM, addr=addr)) as sock:
+            conn = self.check_socket(sock)
+            assert conn.raddr == ()
+            assert conn.status == psutil.CONN_LISTEN
+
+    def test_udp_v4(self):
+        addr = ("127.0.0.1", 0)
+        with closing(bind_socket(AF_INET, SOCK_DGRAM, addr=addr)) as sock:
+            conn = self.check_socket(sock)
+            assert conn.raddr == ()
+            assert conn.status == psutil.CONN_NONE
+
+    @pytest.mark.skipif(not supports_ipv6(), reason="IPv6 not supported")
+    def test_udp_v6(self):
+        addr = ("::1", 0)
+        with closing(bind_socket(AF_INET6, SOCK_DGRAM, addr=addr)) as sock:
+            conn = self.check_socket(sock)
+            assert conn.raddr == ()
+            assert conn.status == psutil.CONN_NONE
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_unix_tcp(self):
+        testfn = self.get_testfn()
+        with closing(bind_unix_socket(testfn, type=SOCK_STREAM)) as sock:
+            conn = self.check_socket(sock)
+            assert conn.raddr == ""
+            assert conn.status == psutil.CONN_NONE
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_unix_udp(self):
+        testfn = self.get_testfn()
+        with closing(bind_unix_socket(testfn, type=SOCK_STREAM)) as sock:
+            conn = self.check_socket(sock)
+            assert conn.raddr == ""
+            assert conn.status == psutil.CONN_NONE
+
+
+@pytest.mark.xdist_group(name="serial")
+class TestConnectedSocket(ConnectionTestCase):
+    """Test socket pairs which are actually connected to
+    each other.
+    """
+
+    # On SunOS, even after we close() it, the server socket stays around
+    # in TIME_WAIT state.
+    @pytest.mark.skipif(SUNOS, reason="unreliable on SUONS")
+    def test_tcp(self):
+        addr = ("127.0.0.1", 0)
+        assert this_proc_net_connections(kind='tcp4') == []
+        server, client = tcp_socketpair(AF_INET, addr=addr)
+        try:
+            cons = this_proc_net_connections(kind='tcp4')
+            assert len(cons) == 2
+            assert cons[0].status == psutil.CONN_ESTABLISHED
+            assert cons[1].status == psutil.CONN_ESTABLISHED
+            # May not be fast enough to change state so it stays
+            # commenteed.
+            # client.close()
+            # cons = this_proc_net_connections(kind='all')
+            # self.assertEqual(len(cons), 1)
+            # self.assertEqual(cons[0].status, psutil.CONN_CLOSE_WAIT)
+        finally:
+            server.close()
+            client.close()
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_unix(self):
+        testfn = self.get_testfn()
+        server, client = unix_socketpair(testfn)
+        try:
+            cons = this_proc_net_connections(kind='unix')
+            assert not (cons[0].laddr and cons[0].raddr), cons
+            assert not (cons[1].laddr and cons[1].raddr), cons
+            if NETBSD or FREEBSD:
+                # On NetBSD creating a UNIX socket will cause
+                # a UNIX connection to  /var/run/log.
+                cons = [c for c in cons if c.raddr != '/var/run/log']
+            assert len(cons) == 2
+            if LINUX or FREEBSD or SUNOS or OPENBSD:
+                # remote path is never set
+                assert cons[0].raddr == ""
+                assert cons[1].raddr == ""
+                # one local address should though
+                assert testfn == (cons[0].laddr or cons[1].laddr)
+            else:
+                # On other systems either the laddr or raddr
+                # of both peers are set.
+                assert (cons[0].laddr or cons[1].laddr) == testfn
+        finally:
+            server.close()
+            client.close()
+
+
+class TestFilters(ConnectionTestCase):
+    def test_filters(self):
+        def check(kind, families, types):
+            for conn in this_proc_net_connections(kind=kind):
+                assert conn.family in families
+                assert conn.type in types
+            if not SKIP_SYSCONS:
+                for conn in psutil.net_connections(kind=kind):
+                    assert conn.family in families
+                    assert conn.type in types
+
+        with create_sockets():
+            check(
+                'all',
+                [AF_INET, AF_INET6, AF_UNIX],
+                [SOCK_STREAM, SOCK_DGRAM, SOCK_SEQPACKET],
+            )
+            check('inet', [AF_INET, AF_INET6], [SOCK_STREAM, SOCK_DGRAM])
+            check('inet4', [AF_INET], [SOCK_STREAM, SOCK_DGRAM])
+            check('tcp', [AF_INET, AF_INET6], [SOCK_STREAM])
+            check('tcp4', [AF_INET], [SOCK_STREAM])
+            check('tcp6', [AF_INET6], [SOCK_STREAM])
+            check('udp', [AF_INET, AF_INET6], [SOCK_DGRAM])
+            check('udp4', [AF_INET], [SOCK_DGRAM])
+            check('udp6', [AF_INET6], [SOCK_DGRAM])
+            if HAS_NET_CONNECTIONS_UNIX:
+                check(
+                    'unix',
+                    [AF_UNIX],
+                    [SOCK_STREAM, SOCK_DGRAM, SOCK_SEQPACKET],
+                )
+
+    @skip_on_access_denied(only_if=MACOS)
+    def test_combos(self):
+        reap_children()
+
+        def check_conn(proc, conn, family, type, laddr, raddr, status, kinds):
+            all_kinds = (
+                "all",
+                "inet",
+                "inet4",
+                "inet6",
+                "tcp",
+                "tcp4",
+                "tcp6",
+                "udp",
+                "udp4",
+                "udp6",
+            )
+            check_connection_ntuple(conn)
+            assert conn.family == family
+            assert conn.type == type
+            assert conn.laddr == laddr
+            assert conn.raddr == raddr
+            assert conn.status == status
+            for kind in all_kinds:
+                cons = proc.net_connections(kind=kind)
+                if kind in kinds:
+                    assert cons != []
+                else:
+                    assert cons == []
+            # compare against system-wide connections
+            # XXX Solaris can't retrieve system-wide UNIX
+            # sockets.
+            if HAS_NET_CONNECTIONS_UNIX:
+                self.compare_procsys_connections(proc.pid, [conn])
+
+        tcp_template = textwrap.dedent("""
+            import socket, time
+            s = socket.socket({family}, socket.SOCK_STREAM)
+            s.bind(('{addr}', 0))
+            s.listen(5)
+            with open('{testfn}', 'w') as f:
+                f.write(str(s.getsockname()[:2]))
+            [time.sleep(0.1) for x in range(100)]
+            """)
+
+        udp_template = textwrap.dedent("""
+            import socket, time
+            s = socket.socket({family}, socket.SOCK_DGRAM)
+            s.bind(('{addr}', 0))
+            with open('{testfn}', 'w') as f:
+                f.write(str(s.getsockname()[:2]))
+            [time.sleep(0.1) for x in range(100)]
+            """)
+
+        # must be relative on Windows
+        testfile = os.path.basename(self.get_testfn(dir=os.getcwd()))
+        tcp4_template = tcp_template.format(
+            family=int(AF_INET), addr="127.0.0.1", testfn=testfile
+        )
+        udp4_template = udp_template.format(
+            family=int(AF_INET), addr="127.0.0.1", testfn=testfile
+        )
+        tcp6_template = tcp_template.format(
+            family=int(AF_INET6), addr="::1", testfn=testfile
+        )
+        udp6_template = udp_template.format(
+            family=int(AF_INET6), addr="::1", testfn=testfile
+        )
+
+        # launch various subprocess instantiating a socket of various
+        # families and types to enrich psutil results
+        tcp4_proc = self.pyrun(tcp4_template)
+        tcp4_addr = eval(wait_for_file(testfile, delete=True))
+        udp4_proc = self.pyrun(udp4_template)
+        udp4_addr = eval(wait_for_file(testfile, delete=True))
+        if supports_ipv6():
+            tcp6_proc = self.pyrun(tcp6_template)
+            tcp6_addr = eval(wait_for_file(testfile, delete=True))
+            udp6_proc = self.pyrun(udp6_template)
+            udp6_addr = eval(wait_for_file(testfile, delete=True))
+        else:
+            tcp6_proc = None
+            udp6_proc = None
+            tcp6_addr = None
+            udp6_addr = None
+
+        for p in psutil.Process().children():
+            cons = p.net_connections()
+            assert len(cons) == 1
+            for conn in cons:
+                # TCP v4
+                if p.pid == tcp4_proc.pid:
+                    check_conn(
+                        p,
+                        conn,
+                        AF_INET,
+                        SOCK_STREAM,
+                        tcp4_addr,
+                        (),
+                        psutil.CONN_LISTEN,
+                        ("all", "inet", "inet4", "tcp", "tcp4"),
+                    )
+                # UDP v4
+                elif p.pid == udp4_proc.pid:
+                    check_conn(
+                        p,
+                        conn,
+                        AF_INET,
+                        SOCK_DGRAM,
+                        udp4_addr,
+                        (),
+                        psutil.CONN_NONE,
+                        ("all", "inet", "inet4", "udp", "udp4"),
+                    )
+                # TCP v6
+                elif p.pid == getattr(tcp6_proc, "pid", None):
+                    check_conn(
+                        p,
+                        conn,
+                        AF_INET6,
+                        SOCK_STREAM,
+                        tcp6_addr,
+                        (),
+                        psutil.CONN_LISTEN,
+                        ("all", "inet", "inet6", "tcp", "tcp6"),
+                    )
+                # UDP v6
+                elif p.pid == getattr(udp6_proc, "pid", None):
+                    check_conn(
+                        p,
+                        conn,
+                        AF_INET6,
+                        SOCK_DGRAM,
+                        udp6_addr,
+                        (),
+                        psutil.CONN_NONE,
+                        ("all", "inet", "inet6", "udp", "udp6"),
+                    )
+
+    def test_count(self):
+        with create_sockets():
+            # tcp
+            cons = this_proc_net_connections(kind='tcp')
+            assert len(cons) == (2 if supports_ipv6() else 1)
+            for conn in cons:
+                assert conn.family in {AF_INET, AF_INET6}
+                assert conn.type == SOCK_STREAM
+            # tcp4
+            cons = this_proc_net_connections(kind='tcp4')
+            assert len(cons) == 1
+            assert cons[0].family == AF_INET
+            assert cons[0].type == SOCK_STREAM
+            # tcp6
+            if supports_ipv6():
+                cons = this_proc_net_connections(kind='tcp6')
+                assert len(cons) == 1
+                assert cons[0].family == AF_INET6
+                assert cons[0].type == SOCK_STREAM
+            # udp
+            cons = this_proc_net_connections(kind='udp')
+            assert len(cons) == (2 if supports_ipv6() else 1)
+            for conn in cons:
+                assert conn.family in {AF_INET, AF_INET6}
+                assert conn.type == SOCK_DGRAM
+            # udp4
+            cons = this_proc_net_connections(kind='udp4')
+            assert len(cons) == 1
+            assert cons[0].family == AF_INET
+            assert cons[0].type == SOCK_DGRAM
+            # udp6
+            if supports_ipv6():
+                cons = this_proc_net_connections(kind='udp6')
+                assert len(cons) == 1
+                assert cons[0].family == AF_INET6
+                assert cons[0].type == SOCK_DGRAM
+            # inet
+            cons = this_proc_net_connections(kind='inet')
+            assert len(cons) == (4 if supports_ipv6() else 2)
+            for conn in cons:
+                assert conn.family in {AF_INET, AF_INET6}
+                assert conn.type in {SOCK_STREAM, SOCK_DGRAM}
+            # inet6
+            if supports_ipv6():
+                cons = this_proc_net_connections(kind='inet6')
+                assert len(cons) == 2
+                for conn in cons:
+                    assert conn.family == AF_INET6
+                    assert conn.type in {SOCK_STREAM, SOCK_DGRAM}
+            # Skipped on BSD becayse by default the Python process
+            # creates a UNIX socket to '/var/run/log'.
+            if HAS_NET_CONNECTIONS_UNIX and not (FREEBSD or NETBSD):
+                cons = this_proc_net_connections(kind='unix')
+                assert len(cons) == 3
+                for conn in cons:
+                    assert conn.family == AF_UNIX
+                    assert conn.type in {SOCK_STREAM, SOCK_DGRAM}
+
+
+@pytest.mark.skipif(SKIP_SYSCONS, reason="requires root")
+class TestSystemWideConnections(ConnectionTestCase):
+    """Tests for net_connections()."""
+
+    def test_it(self):
+        def check(cons, families, types_):
+            for conn in cons:
+                assert conn.family in families
+                if conn.family != AF_UNIX:
+                    assert conn.type in types_
+                check_connection_ntuple(conn)
+
+        with create_sockets():
+            from psutil._common import conn_tmap
+
+            for kind, groups in conn_tmap.items():
+                # XXX: SunOS does not retrieve UNIX sockets.
+                if kind == 'unix' and not HAS_NET_CONNECTIONS_UNIX:
+                    continue
+                families, types_ = groups
+                cons = psutil.net_connections(kind)
+                assert len(cons) == len(set(cons))
+                check(cons, families, types_)
+
+    @retry_on_failure()
+    def test_multi_sockets_procs(self):
+        # Creates multiple sub processes, each creating different
+        # sockets. For each process check that proc.net_connections()
+        # and psutil.net_connections() return the same results.
+        # This is done mainly to check whether net_connections()'s
+        # pid is properly set, see:
+        # https://github.com/giampaolo/psutil/issues/1013
+        with create_sockets() as socks:
+            expected = len(socks)
+        pids = []
+        times = 10
+        fnames = []
+        for _ in range(times):
+            fname = self.get_testfn()
+            fnames.append(fname)
+            src = textwrap.dedent(f"""\
+                import time, os
+                from psutil.tests import create_sockets
+                with create_sockets():
+                    with open(r'{fname}', 'w') as f:
+                        f.write("hello")
+                    [time.sleep(0.1) for x in range(100)]
+                """)
+            sproc = self.pyrun(src)
+            pids.append(sproc.pid)
+
+        # sync
+        for fname in fnames:
+            wait_for_file(fname)
+
+        syscons = [
+            x for x in psutil.net_connections(kind='all') if x.pid in pids
+        ]
+        for pid in pids:
+            assert len([x for x in syscons if x.pid == pid]) == expected
+            p = psutil.Process(pid)
+            assert len(p.net_connections('all')) == expected
+
+
+class TestMisc(PsutilTestCase):
+    def test_net_connection_constants(self):
+        ints = []
+        strs = []
+        for name in dir(psutil):
+            if name.startswith('CONN_'):
+                num = getattr(psutil, name)
+                str_ = str(num)
+                assert str_.isupper(), str_
+                assert str not in strs
+                assert num not in ints
+                ints.append(num)
+                strs.append(str_)
+        if SUNOS:
+            psutil.CONN_IDLE  # noqa: B018
+            psutil.CONN_BOUND  # noqa: B018
+        if WINDOWS:
+            psutil.CONN_DELETE_TCB  # noqa: B018
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_contracts.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_contracts.py
new file mode 100644
index 0000000000000000000000000000000000000000..55f3a5ddb82beb9363e92eb4a2d61f3fac9ea329
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_contracts.py
@@ -0,0 +1,325 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Contracts tests. These tests mainly check API sanity in terms of
+returned types and APIs availability.
+Some of these are duplicates of tests test_system.py and test_process.py.
+"""
+
+import platform
+import signal
+
+import psutil
+from psutil import AIX
+from psutil import FREEBSD
+from psutil import LINUX
+from psutil import MACOS
+from psutil import NETBSD
+from psutil import OPENBSD
+from psutil import POSIX
+from psutil import SUNOS
+from psutil import WINDOWS
+from psutil.tests import GITHUB_ACTIONS
+from psutil.tests import HAS_CPU_FREQ
+from psutil.tests import HAS_NET_IO_COUNTERS
+from psutil.tests import HAS_SENSORS_FANS
+from psutil.tests import HAS_SENSORS_TEMPERATURES
+from psutil.tests import SKIP_SYSCONS
+from psutil.tests import PsutilTestCase
+from psutil.tests import create_sockets
+from psutil.tests import enum
+from psutil.tests import is_namedtuple
+from psutil.tests import kernel_version
+from psutil.tests import pytest
+
+
+# ===================================================================
+# --- APIs availability
+# ===================================================================
+
+# Make sure code reflects what doc promises in terms of APIs
+# availability.
+
+
+class TestAvailConstantsAPIs(PsutilTestCase):
+    def test_PROCFS_PATH(self):
+        assert hasattr(psutil, "PROCFS_PATH") == (LINUX or SUNOS or AIX)
+
+    def test_win_priority(self):
+        ae = self.assertEqual
+        ae(hasattr(psutil, "ABOVE_NORMAL_PRIORITY_CLASS"), WINDOWS)
+        ae(hasattr(psutil, "BELOW_NORMAL_PRIORITY_CLASS"), WINDOWS)
+        ae(hasattr(psutil, "HIGH_PRIORITY_CLASS"), WINDOWS)
+        ae(hasattr(psutil, "IDLE_PRIORITY_CLASS"), WINDOWS)
+        ae(hasattr(psutil, "NORMAL_PRIORITY_CLASS"), WINDOWS)
+        ae(hasattr(psutil, "REALTIME_PRIORITY_CLASS"), WINDOWS)
+
+    def test_linux_ioprio_linux(self):
+        ae = self.assertEqual
+        ae(hasattr(psutil, "IOPRIO_CLASS_NONE"), LINUX)
+        ae(hasattr(psutil, "IOPRIO_CLASS_RT"), LINUX)
+        ae(hasattr(psutil, "IOPRIO_CLASS_BE"), LINUX)
+        ae(hasattr(psutil, "IOPRIO_CLASS_IDLE"), LINUX)
+
+    def test_linux_ioprio_windows(self):
+        ae = self.assertEqual
+        ae(hasattr(psutil, "IOPRIO_HIGH"), WINDOWS)
+        ae(hasattr(psutil, "IOPRIO_NORMAL"), WINDOWS)
+        ae(hasattr(psutil, "IOPRIO_LOW"), WINDOWS)
+        ae(hasattr(psutil, "IOPRIO_VERYLOW"), WINDOWS)
+
+    @pytest.mark.skipif(
+        GITHUB_ACTIONS and LINUX,
+        reason="unsupported on GITHUB_ACTIONS + LINUX",
+    )
+    def test_rlimit(self):
+        ae = self.assertEqual
+        ae(hasattr(psutil, "RLIM_INFINITY"), LINUX or FREEBSD)
+        ae(hasattr(psutil, "RLIMIT_AS"), LINUX or FREEBSD)
+        ae(hasattr(psutil, "RLIMIT_CORE"), LINUX or FREEBSD)
+        ae(hasattr(psutil, "RLIMIT_CPU"), LINUX or FREEBSD)
+        ae(hasattr(psutil, "RLIMIT_DATA"), LINUX or FREEBSD)
+        ae(hasattr(psutil, "RLIMIT_FSIZE"), LINUX or FREEBSD)
+        ae(hasattr(psutil, "RLIMIT_MEMLOCK"), LINUX or FREEBSD)
+        ae(hasattr(psutil, "RLIMIT_NOFILE"), LINUX or FREEBSD)
+        ae(hasattr(psutil, "RLIMIT_NPROC"), LINUX or FREEBSD)
+        ae(hasattr(psutil, "RLIMIT_RSS"), LINUX or FREEBSD)
+        ae(hasattr(psutil, "RLIMIT_STACK"), LINUX or FREEBSD)
+
+        ae(hasattr(psutil, "RLIMIT_LOCKS"), LINUX)
+        if POSIX:
+            if kernel_version() >= (2, 6, 8):
+                ae(hasattr(psutil, "RLIMIT_MSGQUEUE"), LINUX)
+            if kernel_version() >= (2, 6, 12):
+                ae(hasattr(psutil, "RLIMIT_NICE"), LINUX)
+            if kernel_version() >= (2, 6, 12):
+                ae(hasattr(psutil, "RLIMIT_RTPRIO"), LINUX)
+            if kernel_version() >= (2, 6, 25):
+                ae(hasattr(psutil, "RLIMIT_RTTIME"), LINUX)
+            if kernel_version() >= (2, 6, 8):
+                ae(hasattr(psutil, "RLIMIT_SIGPENDING"), LINUX)
+
+        ae(hasattr(psutil, "RLIMIT_SWAP"), FREEBSD)
+        ae(hasattr(psutil, "RLIMIT_SBSIZE"), FREEBSD)
+        ae(hasattr(psutil, "RLIMIT_NPTS"), FREEBSD)
+
+
+class TestAvailSystemAPIs(PsutilTestCase):
+    def test_win_service_iter(self):
+        assert hasattr(psutil, "win_service_iter") == WINDOWS
+
+    def test_win_service_get(self):
+        assert hasattr(psutil, "win_service_get") == WINDOWS
+
+    def test_cpu_freq(self):
+        assert hasattr(psutil, "cpu_freq") == (
+            LINUX or MACOS or WINDOWS or FREEBSD or OPENBSD
+        )
+
+    def test_sensors_temperatures(self):
+        assert hasattr(psutil, "sensors_temperatures") == (LINUX or FREEBSD)
+
+    def test_sensors_fans(self):
+        assert hasattr(psutil, "sensors_fans") == LINUX
+
+    def test_battery(self):
+        assert hasattr(psutil, "sensors_battery") == (
+            LINUX or WINDOWS or FREEBSD or MACOS
+        )
+
+
+class TestAvailProcessAPIs(PsutilTestCase):
+    def test_environ(self):
+        assert hasattr(psutil.Process, "environ") == (
+            LINUX
+            or MACOS
+            or WINDOWS
+            or AIX
+            or SUNOS
+            or FREEBSD
+            or OPENBSD
+            or NETBSD
+        )
+
+    def test_uids(self):
+        assert hasattr(psutil.Process, "uids") == POSIX
+
+    def test_gids(self):
+        assert hasattr(psutil.Process, "uids") == POSIX
+
+    def test_terminal(self):
+        assert hasattr(psutil.Process, "terminal") == POSIX
+
+    def test_ionice(self):
+        assert hasattr(psutil.Process, "ionice") == (LINUX or WINDOWS)
+
+    @pytest.mark.skipif(
+        GITHUB_ACTIONS and LINUX,
+        reason="unsupported on GITHUB_ACTIONS + LINUX",
+    )
+    def test_rlimit(self):
+        assert hasattr(psutil.Process, "rlimit") == (LINUX or FREEBSD)
+
+    def test_io_counters(self):
+        hasit = hasattr(psutil.Process, "io_counters")
+        assert hasit == (not (MACOS or SUNOS))
+
+    def test_num_fds(self):
+        assert hasattr(psutil.Process, "num_fds") == POSIX
+
+    def test_num_handles(self):
+        assert hasattr(psutil.Process, "num_handles") == WINDOWS
+
+    def test_cpu_affinity(self):
+        assert hasattr(psutil.Process, "cpu_affinity") == (
+            LINUX or WINDOWS or FREEBSD
+        )
+
+    def test_cpu_num(self):
+        assert hasattr(psutil.Process, "cpu_num") == (
+            LINUX or FREEBSD or SUNOS
+        )
+
+    def test_memory_maps(self):
+        hasit = hasattr(psutil.Process, "memory_maps")
+        assert hasit == (not (OPENBSD or NETBSD or AIX or MACOS))
+
+
+# ===================================================================
+# --- API types
+# ===================================================================
+
+
+class TestSystemAPITypes(PsutilTestCase):
+    """Check the return types of system related APIs.
+    https://github.com/giampaolo/psutil/issues/1039.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        cls.proc = psutil.Process()
+
+    def assert_ntuple_of_nums(self, nt, type_=float, gezero=True):
+        assert is_namedtuple(nt)
+        for n in nt:
+            assert isinstance(n, type_)
+            if gezero:
+                assert n >= 0
+
+    def test_cpu_times(self):
+        self.assert_ntuple_of_nums(psutil.cpu_times())
+        for nt in psutil.cpu_times(percpu=True):
+            self.assert_ntuple_of_nums(nt)
+
+    def test_cpu_percent(self):
+        assert isinstance(psutil.cpu_percent(interval=None), float)
+        assert isinstance(psutil.cpu_percent(interval=0.00001), float)
+
+    def test_cpu_times_percent(self):
+        self.assert_ntuple_of_nums(psutil.cpu_times_percent(interval=None))
+        self.assert_ntuple_of_nums(psutil.cpu_times_percent(interval=0.0001))
+
+    def test_cpu_count(self):
+        assert isinstance(psutil.cpu_count(), int)
+
+    # TODO: remove this once 1892 is fixed
+    @pytest.mark.skipif(
+        MACOS and platform.machine() == 'arm64', reason="skipped due to #1892"
+    )
+    @pytest.mark.skipif(not HAS_CPU_FREQ, reason="not supported")
+    def test_cpu_freq(self):
+        if psutil.cpu_freq() is None:
+            raise pytest.skip("cpu_freq() returns None")
+        self.assert_ntuple_of_nums(psutil.cpu_freq(), type_=(float, int))
+
+    def test_disk_io_counters(self):
+        # Duplicate of test_system.py. Keep it anyway.
+        for k, v in psutil.disk_io_counters(perdisk=True).items():
+            assert isinstance(k, str)
+            self.assert_ntuple_of_nums(v, type_=int)
+
+    def test_disk_partitions(self):
+        # Duplicate of test_system.py. Keep it anyway.
+        for disk in psutil.disk_partitions():
+            assert isinstance(disk.device, str)
+            assert isinstance(disk.mountpoint, str)
+            assert isinstance(disk.fstype, str)
+            assert isinstance(disk.opts, str)
+
+    @pytest.mark.skipif(SKIP_SYSCONS, reason="requires root")
+    def test_net_connections(self):
+        with create_sockets():
+            ret = psutil.net_connections('all')
+            assert len(ret) == len(set(ret))
+            for conn in ret:
+                assert is_namedtuple(conn)
+
+    def test_net_if_addrs(self):
+        # Duplicate of test_system.py. Keep it anyway.
+        for ifname, addrs in psutil.net_if_addrs().items():
+            assert isinstance(ifname, str)
+            for addr in addrs:
+                assert isinstance(addr.family, enum.IntEnum)
+                assert isinstance(addr.address, str)
+                assert isinstance(addr.netmask, (str, type(None)))
+                assert isinstance(addr.broadcast, (str, type(None)))
+
+    def test_net_if_stats(self):
+        # Duplicate of test_system.py. Keep it anyway.
+        for ifname, info in psutil.net_if_stats().items():
+            assert isinstance(ifname, str)
+            assert isinstance(info.isup, bool)
+            assert isinstance(info.duplex, enum.IntEnum)
+            assert isinstance(info.speed, int)
+            assert isinstance(info.mtu, int)
+
+    @pytest.mark.skipif(not HAS_NET_IO_COUNTERS, reason="not supported")
+    def test_net_io_counters(self):
+        # Duplicate of test_system.py. Keep it anyway.
+        for ifname in psutil.net_io_counters(pernic=True):
+            assert isinstance(ifname, str)
+
+    @pytest.mark.skipif(not HAS_SENSORS_FANS, reason="not supported")
+    def test_sensors_fans(self):
+        # Duplicate of test_system.py. Keep it anyway.
+        for name, units in psutil.sensors_fans().items():
+            assert isinstance(name, str)
+            for unit in units:
+                assert isinstance(unit.label, str)
+                assert isinstance(unit.current, (float, int, type(None)))
+
+    @pytest.mark.skipif(not HAS_SENSORS_TEMPERATURES, reason="not supported")
+    def test_sensors_temperatures(self):
+        # Duplicate of test_system.py. Keep it anyway.
+        for name, units in psutil.sensors_temperatures().items():
+            assert isinstance(name, str)
+            for unit in units:
+                assert isinstance(unit.label, str)
+                assert isinstance(unit.current, (float, int, type(None)))
+                assert isinstance(unit.high, (float, int, type(None)))
+                assert isinstance(unit.critical, (float, int, type(None)))
+
+    def test_boot_time(self):
+        # Duplicate of test_system.py. Keep it anyway.
+        assert isinstance(psutil.boot_time(), float)
+
+    def test_users(self):
+        # Duplicate of test_system.py. Keep it anyway.
+        for user in psutil.users():
+            assert isinstance(user.name, str)
+            assert isinstance(user.terminal, (str, type(None)))
+            assert isinstance(user.host, (str, type(None)))
+            assert isinstance(user.pid, (int, type(None)))
+
+
+class TestProcessWaitType(PsutilTestCase):
+    @pytest.mark.skipif(not POSIX, reason="not POSIX")
+    def test_negative_signal(self):
+        p = psutil.Process(self.spawn_testproc().pid)
+        p.terminate()
+        code = p.wait()
+        assert code == -signal.SIGTERM
+        assert isinstance(code, enum.IntEnum)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_linux.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_linux.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4342d7aa34a74168e74f6fcb306d5b59ea638b6
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_linux.py
@@ -0,0 +1,2292 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Linux specific tests."""
+
+
+import collections
+import contextlib
+import errno
+import io
+import os
+import platform
+import re
+import shutil
+import socket
+import struct
+import textwrap
+import time
+import warnings
+from unittest import mock
+
+import psutil
+from psutil import LINUX
+from psutil.tests import AARCH64
+from psutil.tests import GITHUB_ACTIONS
+from psutil.tests import GLOBAL_TIMEOUT
+from psutil.tests import HAS_BATTERY
+from psutil.tests import HAS_CPU_FREQ
+from psutil.tests import HAS_GETLOADAVG
+from psutil.tests import HAS_RLIMIT
+from psutil.tests import PYPY
+from psutil.tests import PYTEST_PARALLEL
+from psutil.tests import TOLERANCE_DISK_USAGE
+from psutil.tests import TOLERANCE_SYS_MEM
+from psutil.tests import PsutilTestCase
+from psutil.tests import ThreadTask
+from psutil.tests import call_until
+from psutil.tests import pytest
+from psutil.tests import reload_module
+from psutil.tests import retry_on_failure
+from psutil.tests import safe_rmpath
+from psutil.tests import sh
+from psutil.tests import skip_on_not_implemented
+
+
+if LINUX:
+    from psutil._pslinux import CLOCK_TICKS
+    from psutil._pslinux import RootFsDeviceFinder
+    from psutil._pslinux import calculate_avail_vmem
+    from psutil._pslinux import open_binary
+
+
+HERE = os.path.abspath(os.path.dirname(__file__))
+SIOCGIFADDR = 0x8915
+SIOCGIFHWADDR = 0x8927
+SIOCGIFNETMASK = 0x891B
+SIOCGIFBRDADDR = 0x8919
+if LINUX:
+    SECTOR_SIZE = 512
+# =====================================================================
+# --- utils
+# =====================================================================
+
+
+def get_ipv4_address(ifname):
+    import fcntl
+
+    ifname = bytes(ifname[:15], "ascii")
+    with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
+        return socket.inet_ntoa(
+            fcntl.ioctl(s.fileno(), SIOCGIFADDR, struct.pack('256s', ifname))[
+                20:24
+            ]
+        )
+
+
+def get_ipv4_netmask(ifname):
+    import fcntl
+
+    ifname = bytes(ifname[:15], "ascii")
+    with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
+        return socket.inet_ntoa(
+            fcntl.ioctl(
+                s.fileno(), SIOCGIFNETMASK, struct.pack('256s', ifname)
+            )[20:24]
+        )
+
+
+def get_ipv4_broadcast(ifname):
+    import fcntl
+
+    ifname = bytes(ifname[:15], "ascii")
+    with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
+        return socket.inet_ntoa(
+            fcntl.ioctl(
+                s.fileno(), SIOCGIFBRDADDR, struct.pack('256s', ifname)
+            )[20:24]
+        )
+
+
+def get_ipv6_addresses(ifname):
+    with open("/proc/net/if_inet6") as f:
+        all_fields = []
+        for line in f:
+            fields = line.split()
+            if fields[-1] == ifname:
+                all_fields.append(fields)
+
+        if len(all_fields) == 0:
+            raise ValueError(f"could not find interface {ifname!r}")
+
+    for i in range(len(all_fields)):
+        unformatted = all_fields[i][0]
+        groups = [
+            unformatted[j : j + 4] for j in range(0, len(unformatted), 4)
+        ]
+        formatted = ":".join(groups)
+        packed = socket.inet_pton(socket.AF_INET6, formatted)
+        all_fields[i] = socket.inet_ntop(socket.AF_INET6, packed)
+    return all_fields
+
+
+def get_mac_address(ifname):
+    import fcntl
+
+    ifname = bytes(ifname[:15], "ascii")
+    with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
+        info = fcntl.ioctl(
+            s.fileno(), SIOCGIFHWADDR, struct.pack('256s', ifname)
+        )
+        return "".join([f"{char:02x}:" for char in info[18:24]])[:-1]
+
+
+def free_swap():
+    """Parse 'free' cmd and return swap memory's s total, used and free
+    values.
+    """
+    out = sh(["free", "-b"], env={"LANG": "C.UTF-8"})
+    lines = out.split('\n')
+    for line in lines:
+        if line.startswith('Swap'):
+            _, total, used, free = line.split()
+            nt = collections.namedtuple('free', 'total used free')
+            return nt(int(total), int(used), int(free))
+    raise ValueError(f"can't find 'Swap' in 'free' output:\n{out}")
+
+
+def free_physmem():
+    """Parse 'free' cmd and return physical memory's total, used
+    and free values.
+    """
+    # Note: free can have 2 different formats, invalidating 'shared'
+    # and 'cached' memory which may have different positions so we
+    # do not return them.
+    # https://github.com/giampaolo/psutil/issues/538#issuecomment-57059946
+    out = sh(["free", "-b"], env={"LANG": "C.UTF-8"})
+    lines = out.split('\n')
+    for line in lines:
+        if line.startswith('Mem'):
+            total, used, free, shared = (int(x) for x in line.split()[1:5])
+            nt = collections.namedtuple(
+                'free', 'total used free shared output'
+            )
+            return nt(total, used, free, shared, out)
+    raise ValueError(f"can't find 'Mem' in 'free' output:\n{out}")
+
+
+def vmstat(stat):
+    out = sh(["vmstat", "-s"], env={"LANG": "C.UTF-8"})
+    for line in out.split("\n"):
+        line = line.strip()
+        if stat in line:
+            return int(line.split(' ')[0])
+    raise ValueError(f"can't find {stat!r} in 'vmstat' output")
+
+
+def get_free_version_info():
+    out = sh(["free", "-V"]).strip()
+    if 'UNKNOWN' in out:
+        raise pytest.skip("can't determine free version")
+    return tuple(map(int, re.findall(r'\d+', out.split()[-1])))
+
+
+@contextlib.contextmanager
+def mock_open_content(pairs):
+    """Mock open() builtin and forces it to return a certain content
+    for a given path. `pairs` is a {"path": "content", ...} dict.
+    """
+
+    def open_mock(name, *args, **kwargs):
+        if name in pairs:
+            content = pairs[name]
+            if isinstance(content, str):
+                return io.StringIO(content)
+            else:
+                return io.BytesIO(content)
+        else:
+            return orig_open(name, *args, **kwargs)
+
+    orig_open = open
+    with mock.patch("builtins.open", create=True, side_effect=open_mock) as m:
+        yield m
+
+
+@contextlib.contextmanager
+def mock_open_exception(for_path, exc):
+    """Mock open() builtin and raises `exc` if the path being opened
+    matches `for_path`.
+    """
+
+    def open_mock(name, *args, **kwargs):
+        if name == for_path:
+            raise exc
+        return orig_open(name, *args, **kwargs)
+
+    orig_open = open
+    with mock.patch("builtins.open", create=True, side_effect=open_mock) as m:
+        yield m
+
+
+# =====================================================================
+# --- system virtual memory
+# =====================================================================
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemVirtualMemoryAgainstFree(PsutilTestCase):
+    def test_total(self):
+        cli_value = free_physmem().total
+        psutil_value = psutil.virtual_memory().total
+        assert cli_value == psutil_value
+
+    @retry_on_failure()
+    def test_used(self):
+        # Older versions of procps used slab memory to calculate used memory.
+        # This got changed in:
+        # https://gitlab.com/procps-ng/procps/commit/
+        #     05d751c4f076a2f0118b914c5e51cfbb4762ad8e
+        # Newer versions of procps are using yet another way to compute used
+        # memory.
+        # https://gitlab.com/procps-ng/procps/commit/
+        #     2184e90d2e7cdb582f9a5b706b47015e56707e4d
+        if get_free_version_info() < (3, 3, 12):
+            raise pytest.skip("free version too old")
+        if get_free_version_info() >= (4, 0, 0):
+            raise pytest.skip("free version too recent")
+        cli_value = free_physmem().used
+        psutil_value = psutil.virtual_memory().used
+        assert abs(cli_value - psutil_value) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_free(self):
+        cli_value = free_physmem().free
+        psutil_value = psutil.virtual_memory().free
+        assert abs(cli_value - psutil_value) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_shared(self):
+        free = free_physmem()
+        free_value = free.shared
+        if free_value == 0:
+            raise pytest.skip("free does not support 'shared' column")
+        psutil_value = psutil.virtual_memory().shared
+        assert (
+            abs(free_value - psutil_value) < TOLERANCE_SYS_MEM
+        ), f"{free_value} {psutil_value} \n{free.output}"
+
+    @retry_on_failure()
+    def test_available(self):
+        # "free" output format has changed at some point:
+        # https://github.com/giampaolo/psutil/issues/538#issuecomment-147192098
+        out = sh(["free", "-b"])
+        lines = out.split('\n')
+        if 'available' not in lines[0]:
+            raise pytest.skip("free does not support 'available' column")
+        free_value = int(lines[1].split()[-1])
+        psutil_value = psutil.virtual_memory().available
+        assert abs(free_value - psutil_value) < TOLERANCE_SYS_MEM
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemVirtualMemoryAgainstVmstat(PsutilTestCase):
+    def test_total(self):
+        vmstat_value = vmstat('total memory') * 1024
+        psutil_value = psutil.virtual_memory().total
+        assert abs(vmstat_value - psutil_value) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_used(self):
+        # Older versions of procps used slab memory to calculate used memory.
+        # This got changed in:
+        # https://gitlab.com/procps-ng/procps/commit/
+        #     05d751c4f076a2f0118b914c5e51cfbb4762ad8e
+        # Newer versions of procps are using yet another way to compute used
+        # memory.
+        # https://gitlab.com/procps-ng/procps/commit/
+        #     2184e90d2e7cdb582f9a5b706b47015e56707e4d
+        if get_free_version_info() < (3, 3, 12):
+            raise pytest.skip("free version too old")
+        if get_free_version_info() >= (4, 0, 0):
+            raise pytest.skip("free version too recent")
+        vmstat_value = vmstat('used memory') * 1024
+        psutil_value = psutil.virtual_memory().used
+        assert abs(vmstat_value - psutil_value) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_free(self):
+        vmstat_value = vmstat('free memory') * 1024
+        psutil_value = psutil.virtual_memory().free
+        assert abs(vmstat_value - psutil_value) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_buffers(self):
+        vmstat_value = vmstat('buffer memory') * 1024
+        psutil_value = psutil.virtual_memory().buffers
+        assert abs(vmstat_value - psutil_value) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_active(self):
+        vmstat_value = vmstat('active memory') * 1024
+        psutil_value = psutil.virtual_memory().active
+        assert abs(vmstat_value - psutil_value) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_inactive(self):
+        vmstat_value = vmstat('inactive memory') * 1024
+        psutil_value = psutil.virtual_memory().inactive
+        assert abs(vmstat_value - psutil_value) < TOLERANCE_SYS_MEM
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemVirtualMemoryMocks(PsutilTestCase):
+    def test_warnings_on_misses(self):
+        # Emulate a case where /proc/meminfo provides few info.
+        # psutil is supposed to set the missing fields to 0 and
+        # raise a warning.
+        content = textwrap.dedent("""\
+            Active(anon):    6145416 kB
+            Active(file):    2950064 kB
+            Inactive(anon):   574764 kB
+            Inactive(file):  1567648 kB
+            MemAvailable:         -1 kB
+            MemFree:         2057400 kB
+            MemTotal:       16325648 kB
+            SReclaimable:     346648 kB
+            """).encode()
+        with mock_open_content({'/proc/meminfo': content}) as m:
+            with warnings.catch_warnings(record=True) as ws:
+                warnings.simplefilter("always")
+                ret = psutil.virtual_memory()
+                assert m.called
+                assert len(ws) == 1
+                w = ws[0]
+                assert "memory stats couldn't be determined" in str(w.message)
+                assert "cached" in str(w.message)
+                assert "shared" in str(w.message)
+                assert "active" in str(w.message)
+                assert "inactive" in str(w.message)
+                assert "buffers" in str(w.message)
+                assert "available" in str(w.message)
+                assert ret.cached == 0
+                assert ret.active == 0
+                assert ret.inactive == 0
+                assert ret.shared == 0
+                assert ret.buffers == 0
+                assert ret.available == 0
+                assert ret.slab == 0
+
+    @retry_on_failure()
+    def test_avail_old_percent(self):
+        # Make sure that our calculation of avail mem for old kernels
+        # is off by max 15%.
+        mems = {}
+        with open_binary('/proc/meminfo') as f:
+            for line in f:
+                fields = line.split()
+                mems[fields[0]] = int(fields[1]) * 1024
+
+        a = calculate_avail_vmem(mems)
+        if b'MemAvailable:' in mems:
+            b = mems[b'MemAvailable:']
+            diff_percent = abs(a - b) / a * 100
+            assert diff_percent < 15
+
+    def test_avail_old_comes_from_kernel(self):
+        # Make sure "MemAvailable:" coluimn is used instead of relying
+        # on our internal algorithm to calculate avail mem.
+        content = textwrap.dedent("""\
+            Active:          9444728 kB
+            Active(anon):    6145416 kB
+            Active(file):    2950064 kB
+            Buffers:          287952 kB
+            Cached:          4818144 kB
+            Inactive(file):  1578132 kB
+            Inactive(anon):   574764 kB
+            Inactive(file):  1567648 kB
+            MemAvailable:    6574984 kB
+            MemFree:         2057400 kB
+            MemTotal:       16325648 kB
+            Shmem:            577588 kB
+            SReclaimable:     346648 kB
+            """).encode()
+        with mock_open_content({'/proc/meminfo': content}) as m:
+            with warnings.catch_warnings(record=True) as ws:
+                ret = psutil.virtual_memory()
+            assert m.called
+            assert ret.available == 6574984 * 1024
+            w = ws[0]
+            assert "inactive memory stats couldn't be determined" in str(
+                w.message
+            )
+
+    def test_avail_old_missing_fields(self):
+        # Remove Active(file), Inactive(file) and SReclaimable
+        # from /proc/meminfo and make sure the fallback is used
+        # (free + cached),
+        content = textwrap.dedent("""\
+            Active:          9444728 kB
+            Active(anon):    6145416 kB
+            Buffers:          287952 kB
+            Cached:          4818144 kB
+            Inactive(file):  1578132 kB
+            Inactive(anon):   574764 kB
+            MemFree:         2057400 kB
+            MemTotal:       16325648 kB
+            Shmem:            577588 kB
+            """).encode()
+        with mock_open_content({"/proc/meminfo": content}) as m:
+            with warnings.catch_warnings(record=True) as ws:
+                ret = psutil.virtual_memory()
+            assert m.called
+            assert ret.available == 2057400 * 1024 + 4818144 * 1024
+            w = ws[0]
+            assert "inactive memory stats couldn't be determined" in str(
+                w.message
+            )
+
+    def test_avail_old_missing_zoneinfo(self):
+        # Remove /proc/zoneinfo file. Make sure fallback is used
+        # (free + cached).
+        content = textwrap.dedent("""\
+            Active:          9444728 kB
+            Active(anon):    6145416 kB
+            Active(file):    2950064 kB
+            Buffers:          287952 kB
+            Cached:          4818144 kB
+            Inactive(file):  1578132 kB
+            Inactive(anon):   574764 kB
+            Inactive(file):  1567648 kB
+            MemFree:         2057400 kB
+            MemTotal:       16325648 kB
+            Shmem:            577588 kB
+            SReclaimable:     346648 kB
+            """).encode()
+        with mock_open_content({"/proc/meminfo": content}):
+            with mock_open_exception("/proc/zoneinfo", FileNotFoundError):
+                with warnings.catch_warnings(record=True) as ws:
+                    ret = psutil.virtual_memory()
+                    assert ret.available == 2057400 * 1024 + 4818144 * 1024
+                    w = ws[0]
+                    assert (
+                        "inactive memory stats couldn't be determined"
+                        in str(w.message)
+                    )
+
+    def test_virtual_memory_mocked(self):
+        # Emulate /proc/meminfo because neither vmstat nor free return slab.
+        content = textwrap.dedent("""\
+            MemTotal:              100 kB
+            MemFree:               2 kB
+            MemAvailable:          3 kB
+            Buffers:               4 kB
+            Cached:                5 kB
+            SwapCached:            6 kB
+            Active:                7 kB
+            Inactive:              8 kB
+            Active(anon):          9 kB
+            Inactive(anon):        10 kB
+            Active(file):          11 kB
+            Inactive(file):        12 kB
+            Unevictable:           13 kB
+            Mlocked:               14 kB
+            SwapTotal:             15 kB
+            SwapFree:              16 kB
+            Dirty:                 17 kB
+            Writeback:             18 kB
+            AnonPages:             19 kB
+            Mapped:                20 kB
+            Shmem:                 21 kB
+            Slab:                  22 kB
+            SReclaimable:          23 kB
+            SUnreclaim:            24 kB
+            KernelStack:           25 kB
+            PageTables:            26 kB
+            NFS_Unstable:          27 kB
+            Bounce:                28 kB
+            WritebackTmp:          29 kB
+            CommitLimit:           30 kB
+            Committed_AS:          31 kB
+            VmallocTotal:          32 kB
+            VmallocUsed:           33 kB
+            VmallocChunk:          34 kB
+            HardwareCorrupted:     35 kB
+            AnonHugePages:         36 kB
+            ShmemHugePages:        37 kB
+            ShmemPmdMapped:        38 kB
+            CmaTotal:              39 kB
+            CmaFree:               40 kB
+            HugePages_Total:       41 kB
+            HugePages_Free:        42 kB
+            HugePages_Rsvd:        43 kB
+            HugePages_Surp:        44 kB
+            Hugepagesize:          45 kB
+            DirectMap46k:          46 kB
+            DirectMap47M:          47 kB
+            DirectMap48G:          48 kB
+            """).encode()
+        with mock_open_content({"/proc/meminfo": content}) as m:
+            mem = psutil.virtual_memory()
+            assert m.called
+            assert mem.total == 100 * 1024
+            assert mem.free == 2 * 1024
+            assert mem.buffers == 4 * 1024
+            # cached mem also includes reclaimable memory
+            assert mem.cached == (5 + 23) * 1024
+            assert mem.shared == 21 * 1024
+            assert mem.active == 7 * 1024
+            assert mem.inactive == 8 * 1024
+            assert mem.slab == 22 * 1024
+            assert mem.available == 3 * 1024
+
+
+# =====================================================================
+# --- system swap memory
+# =====================================================================
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemSwapMemory(PsutilTestCase):
+    @staticmethod
+    def meminfo_has_swap_info():
+        """Return True if /proc/meminfo provides swap metrics."""
+        with open("/proc/meminfo") as f:
+            data = f.read()
+        return 'SwapTotal:' in data and 'SwapFree:' in data
+
+    def test_total(self):
+        free_value = free_swap().total
+        psutil_value = psutil.swap_memory().total
+        assert abs(free_value - psutil_value) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_used(self):
+        free_value = free_swap().used
+        psutil_value = psutil.swap_memory().used
+        assert abs(free_value - psutil_value) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_free(self):
+        free_value = free_swap().free
+        psutil_value = psutil.swap_memory().free
+        assert abs(free_value - psutil_value) < TOLERANCE_SYS_MEM
+
+    def test_missing_sin_sout(self):
+        with mock.patch('psutil._common.open', create=True) as m:
+            with warnings.catch_warnings(record=True) as ws:
+                warnings.simplefilter("always")
+                ret = psutil.swap_memory()
+                assert m.called
+                assert len(ws) == 1
+                w = ws[0]
+                assert (
+                    "'sin' and 'sout' swap memory stats couldn't be determined"
+                    in str(w.message)
+                )
+                assert ret.sin == 0
+                assert ret.sout == 0
+
+    def test_no_vmstat_mocked(self):
+        # see https://github.com/giampaolo/psutil/issues/722
+        with mock_open_exception("/proc/vmstat", FileNotFoundError) as m:
+            with warnings.catch_warnings(record=True) as ws:
+                warnings.simplefilter("always")
+                ret = psutil.swap_memory()
+                assert m.called
+                assert len(ws) == 1
+                w = ws[0]
+                assert (
+                    "'sin' and 'sout' swap memory stats couldn't "
+                    "be determined and were set to 0"
+                    in str(w.message)
+                )
+                assert ret.sin == 0
+                assert ret.sout == 0
+
+    def test_meminfo_against_sysinfo(self):
+        # Make sure the content of /proc/meminfo about swap memory
+        # matches sysinfo() syscall, see:
+        # https://github.com/giampaolo/psutil/issues/1015
+        if not self.meminfo_has_swap_info():
+            raise pytest.skip("/proc/meminfo has no swap metrics")
+        with mock.patch('psutil._pslinux.cext.linux_sysinfo') as m:
+            swap = psutil.swap_memory()
+        assert not m.called
+        import psutil._psutil_linux as cext
+
+        _, _, _, _, total, free, unit_multiplier = cext.linux_sysinfo()
+        total *= unit_multiplier
+        free *= unit_multiplier
+        assert swap.total == total
+        assert abs(swap.free - free) < TOLERANCE_SYS_MEM
+
+    def test_emulate_meminfo_has_no_metrics(self):
+        # Emulate a case where /proc/meminfo provides no swap metrics
+        # in which case sysinfo() syscall is supposed to be used
+        # as a fallback.
+        with mock_open_content({"/proc/meminfo": b""}) as m:
+            psutil.swap_memory()
+            assert m.called
+
+
+# =====================================================================
+# --- system CPU
+# =====================================================================
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemCPUTimes(PsutilTestCase):
+    def test_fields(self):
+        fields = psutil.cpu_times()._fields
+        kernel_ver = re.findall(r'\d+\.\d+\.\d+', os.uname()[2])[0]
+        kernel_ver_info = tuple(map(int, kernel_ver.split('.')))
+        if kernel_ver_info >= (2, 6, 11):
+            assert 'steal' in fields
+        else:
+            assert 'steal' not in fields
+        if kernel_ver_info >= (2, 6, 24):
+            assert 'guest' in fields
+        else:
+            assert 'guest' not in fields
+        if kernel_ver_info >= (3, 2, 0):
+            assert 'guest_nice' in fields
+        else:
+            assert 'guest_nice' not in fields
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemCPUCountLogical(PsutilTestCase):
+    @pytest.mark.skipif(
+        not os.path.exists("/sys/devices/system/cpu/online"),
+        reason="/sys/devices/system/cpu/online does not exist",
+    )
+    def test_against_sysdev_cpu_online(self):
+        with open("/sys/devices/system/cpu/online") as f:
+            value = f.read().strip()
+        if "-" in str(value):
+            value = int(value.split('-')[1]) + 1
+            assert psutil.cpu_count() == value
+
+    @pytest.mark.skipif(
+        not os.path.exists("/sys/devices/system/cpu"),
+        reason="/sys/devices/system/cpu does not exist",
+    )
+    def test_against_sysdev_cpu_num(self):
+        ls = os.listdir("/sys/devices/system/cpu")
+        count = len([x for x in ls if re.search(r"cpu\d+$", x) is not None])
+        assert psutil.cpu_count() == count
+
+    @pytest.mark.skipif(
+        not shutil.which("nproc"), reason="nproc utility not available"
+    )
+    def test_against_nproc(self):
+        num = int(sh("nproc --all"))
+        assert psutil.cpu_count(logical=True) == num
+
+    @pytest.mark.skipif(
+        not shutil.which("lscpu"), reason="lscpu utility not available"
+    )
+    def test_against_lscpu(self):
+        out = sh("lscpu -p")
+        num = len([x for x in out.split('\n') if not x.startswith('#')])
+        assert psutil.cpu_count(logical=True) == num
+
+    def test_emulate_fallbacks(self):
+        import psutil._pslinux
+
+        original = psutil._pslinux.cpu_count_logical()
+        # Here we want to mock os.sysconf("SC_NPROCESSORS_ONLN") in
+        # order to cause the parsing of /proc/cpuinfo and /proc/stat.
+        with mock.patch(
+            'psutil._pslinux.os.sysconf', side_effect=ValueError
+        ) as m:
+            assert psutil._pslinux.cpu_count_logical() == original
+            assert m.called
+
+            # Let's have open() return empty data and make sure None is
+            # returned ('cause we mimic os.cpu_count()).
+            with mock.patch('psutil._common.open', create=True) as m:
+                assert psutil._pslinux.cpu_count_logical() is None
+                assert m.call_count == 2
+                # /proc/stat should be the last one
+                assert m.call_args[0][0] == '/proc/stat'
+
+            # Let's push this a bit further and make sure /proc/cpuinfo
+            # parsing works as expected.
+            with open('/proc/cpuinfo', 'rb') as f:
+                cpuinfo_data = f.read()
+            fake_file = io.BytesIO(cpuinfo_data)
+            with mock.patch(
+                'psutil._common.open', return_value=fake_file, create=True
+            ) as m:
+                assert psutil._pslinux.cpu_count_logical() == original
+
+            # Finally, let's make /proc/cpuinfo return meaningless data;
+            # this way we'll fall back on relying on /proc/stat
+            with mock_open_content({"/proc/cpuinfo": b""}) as m:
+                assert psutil._pslinux.cpu_count_logical() == original
+                assert m.called
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemCPUCountCores(PsutilTestCase):
+    @pytest.mark.skipif(
+        not shutil.which("lscpu"), reason="lscpu utility not available"
+    )
+    def test_against_lscpu(self):
+        out = sh("lscpu -p")
+        core_ids = set()
+        for line in out.split('\n'):
+            if not line.startswith('#'):
+                fields = line.split(',')
+                core_ids.add(fields[1])
+        assert psutil.cpu_count(logical=False) == len(core_ids)
+
+    @pytest.mark.skipif(
+        platform.machine() not in {"x86_64", "i686"}, reason="x86_64/i686 only"
+    )
+    def test_method_2(self):
+        meth_1 = psutil._pslinux.cpu_count_cores()
+        with mock.patch('glob.glob', return_value=[]) as m:
+            meth_2 = psutil._pslinux.cpu_count_cores()
+            assert m.called
+        if meth_1 is not None:
+            assert meth_1 == meth_2
+
+    def test_emulate_none(self):
+        with mock.patch('glob.glob', return_value=[]) as m1:
+            with mock.patch('psutil._common.open', create=True) as m2:
+                assert psutil._pslinux.cpu_count_cores() is None
+        assert m1.called
+        assert m2.called
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemCPUFrequency(PsutilTestCase):
+    @pytest.mark.skipif(not HAS_CPU_FREQ, reason="not supported")
+    @pytest.mark.skipif(
+        AARCH64, reason="aarch64 does not always expose frequency"
+    )
+    def test_emulate_use_second_file(self):
+        # https://github.com/giampaolo/psutil/issues/981
+        def path_exists_mock(path):
+            if path.startswith("/sys/devices/system/cpu/cpufreq/policy"):
+                return False
+            else:
+                return orig_exists(path)
+
+        orig_exists = os.path.exists
+        with mock.patch(
+            "os.path.exists", side_effect=path_exists_mock, create=True
+        ):
+            assert psutil.cpu_freq()
+
+    @pytest.mark.skipif(not HAS_CPU_FREQ, reason="not supported")
+    @pytest.mark.skipif(
+        AARCH64, reason="aarch64 does not report mhz in /proc/cpuinfo"
+    )
+    def test_emulate_use_cpuinfo(self):
+        # Emulate a case where /sys/devices/system/cpu/cpufreq* does not
+        # exist and /proc/cpuinfo is used instead.
+        def path_exists_mock(path):
+            if path.startswith('/sys/devices/system/cpu/'):
+                return False
+            else:
+                return os_path_exists(path)
+
+        os_path_exists = os.path.exists
+        try:
+            with mock.patch("os.path.exists", side_effect=path_exists_mock):
+                reload_module(psutil._pslinux)
+                ret = psutil.cpu_freq()
+                assert ret, ret
+                assert ret.max == 0.0
+                assert ret.min == 0.0
+                for freq in psutil.cpu_freq(percpu=True):
+                    assert freq.max == 0.0
+                    assert freq.min == 0.0
+        finally:
+            reload_module(psutil._pslinux)
+            reload_module(psutil)
+
+    @pytest.mark.skipif(not HAS_CPU_FREQ, reason="not supported")
+    def test_emulate_data(self):
+        def open_mock(name, *args, **kwargs):
+            if name.endswith('/scaling_cur_freq') and name.startswith(
+                "/sys/devices/system/cpu/cpufreq/policy"
+            ):
+                return io.BytesIO(b"500000")
+            elif name.endswith('/scaling_min_freq') and name.startswith(
+                "/sys/devices/system/cpu/cpufreq/policy"
+            ):
+                return io.BytesIO(b"600000")
+            elif name.endswith('/scaling_max_freq') and name.startswith(
+                "/sys/devices/system/cpu/cpufreq/policy"
+            ):
+                return io.BytesIO(b"700000")
+            elif name == '/proc/cpuinfo':
+                return io.BytesIO(b"cpu MHz     : 500")
+            else:
+                return orig_open(name, *args, **kwargs)
+
+        orig_open = open
+        with mock.patch("builtins.open", side_effect=open_mock):
+            with mock.patch('os.path.exists', return_value=True):
+                freq = psutil.cpu_freq()
+                assert freq.current == 500.0
+                # when /proc/cpuinfo is used min and max frequencies are not
+                # available and are set to 0.
+                if freq.min != 0.0:
+                    assert freq.min == 600.0
+                if freq.max != 0.0:
+                    assert freq.max == 700.0
+
+    @pytest.mark.skipif(not HAS_CPU_FREQ, reason="not supported")
+    def test_emulate_multi_cpu(self):
+        def open_mock(name, *args, **kwargs):
+            n = name
+            if n.endswith('/scaling_cur_freq') and n.startswith(
+                "/sys/devices/system/cpu/cpufreq/policy0"
+            ):
+                return io.BytesIO(b"100000")
+            elif n.endswith('/scaling_min_freq') and n.startswith(
+                "/sys/devices/system/cpu/cpufreq/policy0"
+            ):
+                return io.BytesIO(b"200000")
+            elif n.endswith('/scaling_max_freq') and n.startswith(
+                "/sys/devices/system/cpu/cpufreq/policy0"
+            ):
+                return io.BytesIO(b"300000")
+            elif n.endswith('/scaling_cur_freq') and n.startswith(
+                "/sys/devices/system/cpu/cpufreq/policy1"
+            ):
+                return io.BytesIO(b"400000")
+            elif n.endswith('/scaling_min_freq') and n.startswith(
+                "/sys/devices/system/cpu/cpufreq/policy1"
+            ):
+                return io.BytesIO(b"500000")
+            elif n.endswith('/scaling_max_freq') and n.startswith(
+                "/sys/devices/system/cpu/cpufreq/policy1"
+            ):
+                return io.BytesIO(b"600000")
+            elif name == '/proc/cpuinfo':
+                return io.BytesIO(b"cpu MHz     : 100\ncpu MHz     : 400")
+            else:
+                return orig_open(name, *args, **kwargs)
+
+        orig_open = open
+        with mock.patch("builtins.open", side_effect=open_mock):
+            with mock.patch('os.path.exists', return_value=True):
+                with mock.patch(
+                    'psutil._pslinux.cpu_count_logical', return_value=2
+                ):
+                    freq = psutil.cpu_freq(percpu=True)
+                    assert freq[0].current == 100.0
+                    if freq[0].min != 0.0:
+                        assert freq[0].min == 200.0
+                    if freq[0].max != 0.0:
+                        assert freq[0].max == 300.0
+                    assert freq[1].current == 400.0
+                    if freq[1].min != 0.0:
+                        assert freq[1].min == 500.0
+                    if freq[1].max != 0.0:
+                        assert freq[1].max == 600.0
+
+    @pytest.mark.skipif(not HAS_CPU_FREQ, reason="not supported")
+    def test_emulate_no_scaling_cur_freq_file(self):
+        # See: https://github.com/giampaolo/psutil/issues/1071
+        def open_mock(name, *args, **kwargs):
+            if name.endswith('/scaling_cur_freq'):
+                raise FileNotFoundError
+            if name.endswith('/cpuinfo_cur_freq'):
+                return io.BytesIO(b"200000")
+            elif name == '/proc/cpuinfo':
+                return io.BytesIO(b"cpu MHz     : 200")
+            else:
+                return orig_open(name, *args, **kwargs)
+
+        orig_open = open
+        with mock.patch("builtins.open", side_effect=open_mock):
+            with mock.patch('os.path.exists', return_value=True):
+                with mock.patch(
+                    'psutil._pslinux.cpu_count_logical', return_value=1
+                ):
+                    freq = psutil.cpu_freq()
+                    assert freq.current == 200
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemCPUStats(PsutilTestCase):
+
+    # XXX: fails too often.
+    # def test_ctx_switches(self):
+    #     vmstat_value = vmstat("context switches")
+    #     psutil_value = psutil.cpu_stats().ctx_switches
+    #     self.assertAlmostEqual(vmstat_value, psutil_value, delta=500)
+
+    def test_interrupts(self):
+        vmstat_value = vmstat("interrupts")
+        psutil_value = psutil.cpu_stats().interrupts
+        assert abs(vmstat_value - psutil_value) < 500
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestLoadAvg(PsutilTestCase):
+    @pytest.mark.skipif(not HAS_GETLOADAVG, reason="not supported")
+    def test_getloadavg(self):
+        psutil_value = psutil.getloadavg()
+        with open("/proc/loadavg") as f:
+            proc_value = f.read().split()
+
+        assert abs(float(proc_value[0]) - psutil_value[0]) < 1
+        assert abs(float(proc_value[1]) - psutil_value[1]) < 1
+        assert abs(float(proc_value[2]) - psutil_value[2]) < 1
+
+
+# =====================================================================
+# --- system network
+# =====================================================================
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemNetIfAddrs(PsutilTestCase):
+    def test_ips(self):
+        for name, addrs in psutil.net_if_addrs().items():
+            for addr in addrs:
+                if addr.family == psutil.AF_LINK:
+                    assert addr.address == get_mac_address(name)
+                elif addr.family == socket.AF_INET:
+                    assert addr.address == get_ipv4_address(name)
+                    assert addr.netmask == get_ipv4_netmask(name)
+                    if addr.broadcast is not None:
+                        assert addr.broadcast == get_ipv4_broadcast(name)
+                    else:
+                        assert get_ipv4_broadcast(name) == '0.0.0.0'
+                elif addr.family == socket.AF_INET6:
+                    # IPv6 addresses can have a percent symbol at the end.
+                    # E.g. these 2 are equivalent:
+                    # "fe80::1ff:fe23:4567:890a"
+                    # "fe80::1ff:fe23:4567:890a%eth0"
+                    # That is the "zone id" portion, which usually is the name
+                    # of the network interface.
+                    address = addr.address.split('%')[0]
+                    assert address in get_ipv6_addresses(name)
+
+    # XXX - not reliable when having virtual NICs installed by Docker.
+    # @pytest.mark.skipif(not shutil.which("ip"),
+    #                     reason="'ip' utility not available")
+    # def test_net_if_names(self):
+    #     out = sh("ip addr").strip()
+    #     nics = [x for x in psutil.net_if_addrs().keys() if ':' not in x]
+    #     found = 0
+    #     for line in out.split('\n'):
+    #         line = line.strip()
+    #         if re.search(r"^\d+:", line):
+    #             found += 1
+    #             name = line.split(':')[1].strip()
+    #             self.assertIn(name, nics)
+    #     self.assertEqual(len(nics), found, msg="{}\n---\n{}".format(
+    #         pprint.pformat(nics), out))
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemNetIfStats(PsutilTestCase):
+    @pytest.mark.skipif(
+        not shutil.which("ifconfig"), reason="ifconfig utility not available"
+    )
+    def test_against_ifconfig(self):
+        for name, stats in psutil.net_if_stats().items():
+            try:
+                out = sh(f"ifconfig {name}")
+            except RuntimeError:
+                pass
+            else:
+                assert stats.isup == ('RUNNING' in out), out
+                assert stats.mtu == int(
+                    re.findall(r'(?i)MTU[: ](\d+)', out)[0]
+                )
+
+    def test_mtu(self):
+        for name, stats in psutil.net_if_stats().items():
+            with open(f"/sys/class/net/{name}/mtu") as f:
+                assert stats.mtu == int(f.read().strip())
+
+    @pytest.mark.skipif(
+        not shutil.which("ifconfig"), reason="ifconfig utility not available"
+    )
+    def test_flags(self):
+        # first line looks like this:
+        # "eth0: flags=4163<UP,BROADCAST,RUNNING,MULTICAST>  mtu 1500"
+        matches_found = 0
+        for name, stats in psutil.net_if_stats().items():
+            try:
+                out = sh(f"ifconfig {name}")
+            except RuntimeError:
+                pass
+            else:
+                match = re.search(r"flags=(\d+)?<(.*?)>", out)
+                if match and len(match.groups()) >= 2:
+                    matches_found += 1
+                    ifconfig_flags = set(match.group(2).lower().split(","))
+                    psutil_flags = set(stats.flags.split(","))
+                    assert ifconfig_flags == psutil_flags
+                else:
+                    # ifconfig has a different output on CentOS 6
+                    # let's try that
+                    match = re.search(r"(.*)  MTU:(\d+)  Metric:(\d+)", out)
+                    if match and len(match.groups()) >= 3:
+                        matches_found += 1
+                        ifconfig_flags = set(match.group(1).lower().split())
+                        psutil_flags = set(stats.flags.split(","))
+                        assert ifconfig_flags == psutil_flags
+
+        if not matches_found:
+            raise self.fail("no matches were found")
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemNetIOCounters(PsutilTestCase):
+    @pytest.mark.skipif(
+        not shutil.which("ifconfig"), reason="ifconfig utility not available"
+    )
+    @retry_on_failure()
+    def test_against_ifconfig(self):
+        def ifconfig(nic):
+            ret = {}
+            out = sh(f"ifconfig {nic}")
+            ret['packets_recv'] = int(
+                re.findall(r'RX packets[: ](\d+)', out)[0]
+            )
+            ret['packets_sent'] = int(
+                re.findall(r'TX packets[: ](\d+)', out)[0]
+            )
+            ret['errin'] = int(re.findall(r'errors[: ](\d+)', out)[0])
+            ret['errout'] = int(re.findall(r'errors[: ](\d+)', out)[1])
+            ret['dropin'] = int(re.findall(r'dropped[: ](\d+)', out)[0])
+            ret['dropout'] = int(re.findall(r'dropped[: ](\d+)', out)[1])
+            ret['bytes_recv'] = int(
+                re.findall(r'RX (?:packets \d+ +)?bytes[: ](\d+)', out)[0]
+            )
+            ret['bytes_sent'] = int(
+                re.findall(r'TX (?:packets \d+ +)?bytes[: ](\d+)', out)[0]
+            )
+            return ret
+
+        nio = psutil.net_io_counters(pernic=True, nowrap=False)
+        for name, stats in nio.items():
+            try:
+                ifconfig_ret = ifconfig(name)
+            except RuntimeError:
+                continue
+            assert (
+                abs(stats.bytes_recv - ifconfig_ret['bytes_recv']) < 1024 * 10
+            )
+            assert (
+                abs(stats.bytes_sent - ifconfig_ret['bytes_sent']) < 1024 * 10
+            )
+            assert (
+                abs(stats.packets_recv - ifconfig_ret['packets_recv']) < 1024
+            )
+            assert (
+                abs(stats.packets_sent - ifconfig_ret['packets_sent']) < 1024
+            )
+            assert abs(stats.errin - ifconfig_ret['errin']) < 10
+            assert abs(stats.errout - ifconfig_ret['errout']) < 10
+            assert abs(stats.dropin - ifconfig_ret['dropin']) < 10
+            assert abs(stats.dropout - ifconfig_ret['dropout']) < 10
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemNetConnections(PsutilTestCase):
+    @mock.patch('psutil._pslinux.socket.inet_ntop', side_effect=ValueError)
+    @mock.patch('psutil._pslinux.supports_ipv6', return_value=False)
+    def test_emulate_ipv6_unsupported(self, supports_ipv6, inet_ntop):
+        # see: https://github.com/giampaolo/psutil/issues/623
+        try:
+            s = socket.socket(socket.AF_INET6, socket.SOCK_STREAM)
+            self.addCleanup(s.close)
+            s.bind(("::1", 0))
+        except OSError:
+            pass
+        psutil.net_connections(kind='inet6')
+
+    def test_emulate_unix(self):
+        content = textwrap.dedent("""\
+            0: 00000003 000 000 0001 03 462170 @/tmp/dbus-Qw2hMPIU3n
+            0: 00000003 000 000 0001 03 35010 @/tmp/dbus-tB2X8h69BQ
+            0: 00000003 000 000 0001 03 34424 @/tmp/dbus-cHy80Y8O
+            000000000000000000000000000000000000000000000000000000
+            """)
+        with mock_open_content({"/proc/net/unix": content}) as m:
+            psutil.net_connections(kind='unix')
+            assert m.called
+
+
+# =====================================================================
+# --- system disks
+# =====================================================================
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemDiskPartitions(PsutilTestCase):
+    @pytest.mark.skipif(
+        not hasattr(os, 'statvfs'), reason="os.statvfs() not available"
+    )
+    @skip_on_not_implemented()
+    def test_against_df(self):
+        # test psutil.disk_usage() and psutil.disk_partitions()
+        # against "df -a"
+        def df(path):
+            out = sh(f'df -P -B 1 "{path}"').strip()
+            lines = out.split('\n')
+            lines.pop(0)
+            line = lines.pop(0)
+            dev, total, used, free = line.split()[:4]
+            if dev == 'none':
+                dev = ''
+            total, used, free = int(total), int(used), int(free)
+            return dev, total, used, free
+
+        for part in psutil.disk_partitions(all=False):
+            usage = psutil.disk_usage(part.mountpoint)
+            _, total, used, free = df(part.mountpoint)
+            assert usage.total == total
+            assert abs(usage.free - free) < TOLERANCE_DISK_USAGE
+            assert abs(usage.used - used) < TOLERANCE_DISK_USAGE
+
+    def test_zfs_fs(self):
+        # Test that ZFS partitions are returned.
+        with open("/proc/filesystems") as f:
+            data = f.read()
+        if 'zfs' in data:
+            for part in psutil.disk_partitions():
+                if part.fstype == 'zfs':
+                    return
+
+        # No ZFS partitions on this system. Let's fake one.
+        fake_file = io.StringIO("nodev\tzfs\n")
+        with mock.patch(
+            'psutil._common.open', return_value=fake_file, create=True
+        ) as m1:
+            with mock.patch(
+                'psutil._pslinux.cext.disk_partitions',
+                return_value=[('/dev/sdb3', '/', 'zfs', 'rw')],
+            ) as m2:
+                ret = psutil.disk_partitions()
+                assert m1.called
+                assert m2.called
+                assert ret
+                assert ret[0].fstype == 'zfs'
+
+    def test_emulate_realpath_fail(self):
+        # See: https://github.com/giampaolo/psutil/issues/1307
+        try:
+            with mock.patch(
+                'os.path.realpath', return_value='/non/existent'
+            ) as m:
+                with pytest.raises(FileNotFoundError):
+                    psutil.disk_partitions()
+                assert m.called
+        finally:
+            psutil.PROCFS_PATH = "/proc"
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSystemDiskIoCounters(PsutilTestCase):
+    def test_emulate_kernel_2_4(self):
+        # Tests /proc/diskstats parsing format for 2.4 kernels, see:
+        # https://github.com/giampaolo/psutil/issues/767
+        content = "   3     0   1 hda 2 3 4 5 6 7 8 9 10 11 12"
+        with mock_open_content({'/proc/diskstats': content}):
+            with mock.patch(
+                'psutil._pslinux.is_storage_device', return_value=True
+            ):
+                ret = psutil.disk_io_counters(nowrap=False)
+                assert ret.read_count == 1
+                assert ret.read_merged_count == 2
+                assert ret.read_bytes == 3 * SECTOR_SIZE
+                assert ret.read_time == 4
+                assert ret.write_count == 5
+                assert ret.write_merged_count == 6
+                assert ret.write_bytes == 7 * SECTOR_SIZE
+                assert ret.write_time == 8
+                assert ret.busy_time == 10
+
+    def test_emulate_kernel_2_6_full(self):
+        # Tests /proc/diskstats parsing format for 2.6 kernels,
+        # lines reporting all metrics:
+        # https://github.com/giampaolo/psutil/issues/767
+        content = "   3    0   hda 1 2 3 4 5 6 7 8 9 10 11"
+        with mock_open_content({"/proc/diskstats": content}):
+            with mock.patch(
+                'psutil._pslinux.is_storage_device', return_value=True
+            ):
+                ret = psutil.disk_io_counters(nowrap=False)
+                assert ret.read_count == 1
+                assert ret.read_merged_count == 2
+                assert ret.read_bytes == 3 * SECTOR_SIZE
+                assert ret.read_time == 4
+                assert ret.write_count == 5
+                assert ret.write_merged_count == 6
+                assert ret.write_bytes == 7 * SECTOR_SIZE
+                assert ret.write_time == 8
+                assert ret.busy_time == 10
+
+    def test_emulate_kernel_2_6_limited(self):
+        # Tests /proc/diskstats parsing format for 2.6 kernels,
+        # where one line of /proc/partitions return a limited
+        # amount of metrics when it bumps into a partition
+        # (instead of a disk). See:
+        # https://github.com/giampaolo/psutil/issues/767
+        with mock_open_content({"/proc/diskstats": "   3    1   hda 1 2 3 4"}):
+            with mock.patch(
+                'psutil._pslinux.is_storage_device', return_value=True
+            ):
+                ret = psutil.disk_io_counters(nowrap=False)
+                assert ret.read_count == 1
+                assert ret.read_bytes == 2 * SECTOR_SIZE
+                assert ret.write_count == 3
+                assert ret.write_bytes == 4 * SECTOR_SIZE
+
+                assert ret.read_merged_count == 0
+                assert ret.read_time == 0
+                assert ret.write_merged_count == 0
+                assert ret.write_time == 0
+                assert ret.busy_time == 0
+
+    def test_emulate_include_partitions(self):
+        # Make sure that when perdisk=True disk partitions are returned,
+        # see:
+        # https://github.com/giampaolo/psutil/pull/1313#issuecomment-408626842
+        content = textwrap.dedent("""\
+            3    0   nvme0n1 1 2 3 4 5 6 7 8 9 10 11
+            3    0   nvme0n1p1 1 2 3 4 5 6 7 8 9 10 11
+            """)
+        with mock_open_content({"/proc/diskstats": content}):
+            with mock.patch(
+                'psutil._pslinux.is_storage_device', return_value=False
+            ):
+                ret = psutil.disk_io_counters(perdisk=True, nowrap=False)
+                assert len(ret) == 2
+                assert ret['nvme0n1'].read_count == 1
+                assert ret['nvme0n1p1'].read_count == 1
+                assert ret['nvme0n1'].write_count == 5
+                assert ret['nvme0n1p1'].write_count == 5
+
+    def test_emulate_exclude_partitions(self):
+        # Make sure that when perdisk=False partitions (e.g. 'sda1',
+        # 'nvme0n1p1') are skipped and not included in the total count.
+        # https://github.com/giampaolo/psutil/pull/1313#issuecomment-408626842
+        content = textwrap.dedent("""\
+            3    0   nvme0n1 1 2 3 4 5 6 7 8 9 10 11
+            3    0   nvme0n1p1 1 2 3 4 5 6 7 8 9 10 11
+            """)
+        with mock_open_content({"/proc/diskstats": content}):
+            with mock.patch(
+                'psutil._pslinux.is_storage_device', return_value=False
+            ):
+                ret = psutil.disk_io_counters(perdisk=False, nowrap=False)
+                assert ret is None
+
+        def is_storage_device(name):
+            return name == 'nvme0n1'
+
+        content = textwrap.dedent("""\
+            3    0   nvme0n1 1 2 3 4 5 6 7 8 9 10 11
+            3    0   nvme0n1p1 1 2 3 4 5 6 7 8 9 10 11
+            """)
+        with mock_open_content({"/proc/diskstats": content}):
+            with mock.patch(
+                'psutil._pslinux.is_storage_device',
+                create=True,
+                side_effect=is_storage_device,
+            ):
+                ret = psutil.disk_io_counters(perdisk=False, nowrap=False)
+                assert ret.read_count == 1
+                assert ret.write_count == 5
+
+    def test_emulate_use_sysfs(self):
+        def exists(path):
+            return path == '/proc/diskstats'
+
+        wprocfs = psutil.disk_io_counters(perdisk=True)
+        with mock.patch(
+            'psutil._pslinux.os.path.exists', create=True, side_effect=exists
+        ):
+            wsysfs = psutil.disk_io_counters(perdisk=True)
+        assert len(wprocfs) == len(wsysfs)
+
+    def test_emulate_not_impl(self):
+        def exists(path):
+            return False
+
+        with mock.patch(
+            'psutil._pslinux.os.path.exists', create=True, side_effect=exists
+        ):
+            with pytest.raises(NotImplementedError):
+                psutil.disk_io_counters()
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestRootFsDeviceFinder(PsutilTestCase):
+    def setUp(self):
+        dev = os.stat("/").st_dev
+        self.major = os.major(dev)
+        self.minor = os.minor(dev)
+
+    def test_call_methods(self):
+        finder = RootFsDeviceFinder()
+        if os.path.exists("/proc/partitions"):
+            finder.ask_proc_partitions()
+        else:
+            with pytest.raises(FileNotFoundError):
+                finder.ask_proc_partitions()
+        if os.path.exists(f"/sys/dev/block/{self.major}:{self.minor}/uevent"):
+            finder.ask_sys_dev_block()
+        else:
+            with pytest.raises(FileNotFoundError):
+                finder.ask_sys_dev_block()
+        finder.ask_sys_class_block()
+
+    @pytest.mark.skipif(GITHUB_ACTIONS, reason="unsupported on GITHUB_ACTIONS")
+    def test_comparisons(self):
+        finder = RootFsDeviceFinder()
+        assert finder.find() is not None
+
+        a = b = c = None
+        if os.path.exists("/proc/partitions"):
+            a = finder.ask_proc_partitions()
+        if os.path.exists(f"/sys/dev/block/{self.major}:{self.minor}/uevent"):
+            b = finder.ask_sys_class_block()
+        c = finder.ask_sys_dev_block()
+
+        base = a or b or c
+        if base and a:
+            assert base == a
+        if base and b:
+            assert base == b
+        if base and c:
+            assert base == c
+
+    @pytest.mark.skipif(
+        not shutil.which("findmnt"), reason="findmnt utility not available"
+    )
+    @pytest.mark.skipif(GITHUB_ACTIONS, reason="unsupported on GITHUB_ACTIONS")
+    def test_against_findmnt(self):
+        psutil_value = RootFsDeviceFinder().find()
+        findmnt_value = sh("findmnt -o SOURCE -rn /")
+        assert psutil_value == findmnt_value
+
+    def test_disk_partitions_mocked(self):
+        with mock.patch(
+            'psutil._pslinux.cext.disk_partitions',
+            return_value=[('/dev/root', '/', 'ext4', 'rw')],
+        ) as m:
+            part = psutil.disk_partitions()[0]
+            assert m.called
+            if not GITHUB_ACTIONS:
+                assert part.device != "/dev/root"
+                assert part.device == RootFsDeviceFinder().find()
+            else:
+                assert part.device == "/dev/root"
+
+
+# =====================================================================
+# --- misc
+# =====================================================================
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestMisc(PsutilTestCase):
+    def test_boot_time(self):
+        vmstat_value = vmstat('boot time')
+        psutil_value = psutil.boot_time()
+        assert int(vmstat_value) == int(psutil_value)
+
+    def test_no_procfs_on_import(self):
+        my_procfs = self.get_testfn()
+        os.mkdir(my_procfs)
+
+        with open(os.path.join(my_procfs, 'stat'), 'w') as f:
+            f.write('cpu   0 0 0 0 0 0 0 0 0 0\n')
+            f.write('cpu0  0 0 0 0 0 0 0 0 0 0\n')
+            f.write('cpu1  0 0 0 0 0 0 0 0 0 0\n')
+
+        try:
+            orig_open = open
+
+            def open_mock(name, *args, **kwargs):
+                if name.startswith('/proc'):
+                    raise FileNotFoundError
+                return orig_open(name, *args, **kwargs)
+
+            with mock.patch("builtins.open", side_effect=open_mock):
+                reload_module(psutil)
+
+                with pytest.raises(OSError):
+                    psutil.cpu_times()
+                with pytest.raises(OSError):
+                    psutil.cpu_times(percpu=True)
+                with pytest.raises(OSError):
+                    psutil.cpu_percent()
+                with pytest.raises(OSError):
+                    psutil.cpu_percent(percpu=True)
+                with pytest.raises(OSError):
+                    psutil.cpu_times_percent()
+                with pytest.raises(OSError):
+                    psutil.cpu_times_percent(percpu=True)
+
+                psutil.PROCFS_PATH = my_procfs
+
+                assert psutil.cpu_percent() == 0
+                assert sum(psutil.cpu_times_percent()) == 0
+
+                # since we don't know the number of CPUs at import time,
+                # we awkwardly say there are none until the second call
+                per_cpu_percent = psutil.cpu_percent(percpu=True)
+                assert sum(per_cpu_percent) == 0
+
+                # ditto awkward length
+                per_cpu_times_percent = psutil.cpu_times_percent(percpu=True)
+                assert sum(map(sum, per_cpu_times_percent)) == 0
+
+                # much user, very busy
+                with open(os.path.join(my_procfs, 'stat'), 'w') as f:
+                    f.write('cpu   1 0 0 0 0 0 0 0 0 0\n')
+                    f.write('cpu0  1 0 0 0 0 0 0 0 0 0\n')
+                    f.write('cpu1  1 0 0 0 0 0 0 0 0 0\n')
+
+                assert psutil.cpu_percent() != 0
+                assert sum(psutil.cpu_percent(percpu=True)) != 0
+                assert sum(psutil.cpu_times_percent()) != 0
+                assert (
+                    sum(map(sum, psutil.cpu_times_percent(percpu=True))) != 0
+                )
+        finally:
+            shutil.rmtree(my_procfs)
+            reload_module(psutil)
+
+        assert psutil.PROCFS_PATH == '/proc'
+
+    def test_cpu_steal_decrease(self):
+        # Test cumulative cpu stats decrease. We should ignore this.
+        # See issue #1210.
+        content = textwrap.dedent("""\
+            cpu   0 0 0 0 0 0 0 1 0 0
+            cpu0  0 0 0 0 0 0 0 1 0 0
+            cpu1  0 0 0 0 0 0 0 1 0 0
+            """).encode()
+        with mock_open_content({"/proc/stat": content}) as m:
+            # first call to "percent" functions should read the new stat file
+            # and compare to the "real" file read at import time - so the
+            # values are meaningless
+            psutil.cpu_percent()
+            assert m.called
+            psutil.cpu_percent(percpu=True)
+            psutil.cpu_times_percent()
+            psutil.cpu_times_percent(percpu=True)
+
+        content = textwrap.dedent("""\
+            cpu   1 0 0 0 0 0 0 0 0 0
+            cpu0  1 0 0 0 0 0 0 0 0 0
+            cpu1  1 0 0 0 0 0 0 0 0 0
+            """).encode()
+        with mock_open_content({"/proc/stat": content}):
+            # Increase "user" while steal goes "backwards" to zero.
+            cpu_percent = psutil.cpu_percent()
+            assert m.called
+            cpu_percent_percpu = psutil.cpu_percent(percpu=True)
+            cpu_times_percent = psutil.cpu_times_percent()
+            cpu_times_percent_percpu = psutil.cpu_times_percent(percpu=True)
+            assert cpu_percent != 0
+            assert sum(cpu_percent_percpu) != 0
+            assert sum(cpu_times_percent) != 0
+            assert sum(cpu_times_percent) != 100.0
+            assert sum(map(sum, cpu_times_percent_percpu)) != 0
+            assert sum(map(sum, cpu_times_percent_percpu)) != 100.0
+            assert cpu_times_percent.steal == 0
+            assert cpu_times_percent.user != 0
+
+    def test_boot_time_mocked(self):
+        with mock.patch('psutil._common.open', create=True) as m:
+            with pytest.raises(RuntimeError):
+                psutil._pslinux.boot_time()
+            assert m.called
+
+    def test_users(self):
+        # Make sure the C extension converts ':0' and ':0.0' to
+        # 'localhost'.
+        for user in psutil.users():
+            assert user.host not in {":0", ":0.0"}
+
+    def test_procfs_path(self):
+        tdir = self.get_testfn()
+        os.mkdir(tdir)
+        try:
+            psutil.PROCFS_PATH = tdir
+            with pytest.raises(OSError):
+                psutil.virtual_memory()
+            with pytest.raises(OSError):
+                psutil.cpu_times()
+            with pytest.raises(OSError):
+                psutil.cpu_times(percpu=True)
+            with pytest.raises(OSError):
+                psutil.boot_time()
+            # self.assertRaises(OSError, psutil.pids)
+            with pytest.raises(OSError):
+                psutil.net_connections()
+            with pytest.raises(OSError):
+                psutil.net_io_counters()
+            with pytest.raises(OSError):
+                psutil.net_if_stats()
+            # self.assertRaises(OSError, psutil.disk_io_counters)
+            with pytest.raises(OSError):
+                psutil.disk_partitions()
+            with pytest.raises(psutil.NoSuchProcess):
+                psutil.Process()
+        finally:
+            psutil.PROCFS_PATH = "/proc"
+
+    @retry_on_failure()
+    @pytest.mark.skipif(PYTEST_PARALLEL, reason="skip if pytest-parallel")
+    def test_issue_687(self):
+        # In case of thread ID:
+        # - pid_exists() is supposed to return False
+        # - Process(tid) is supposed to work
+        # - pids() should not return the TID
+        # See: https://github.com/giampaolo/psutil/issues/687
+        with ThreadTask():
+            p = psutil.Process()
+            threads = p.threads()
+            assert len(threads) == 2
+            tid = sorted(threads, key=lambda x: x.id)[1].id
+            assert p.pid != tid
+            pt = psutil.Process(tid)
+            pt.as_dict()
+            assert tid not in psutil.pids()
+
+    def test_pid_exists_no_proc_status(self):
+        # Internally pid_exists relies on /proc/{pid}/status.
+        # Emulate a case where this file is empty in which case
+        # psutil is supposed to fall back on using pids().
+        with mock_open_content({"/proc/%s/status": ""}) as m:
+            assert psutil.pid_exists(os.getpid())
+            assert m.called
+
+
+# =====================================================================
+# --- sensors
+# =====================================================================
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+@pytest.mark.skipif(not HAS_BATTERY, reason="no battery")
+class TestSensorsBattery(PsutilTestCase):
+    @pytest.mark.skipif(
+        not shutil.which("acpi"), reason="acpi utility not available"
+    )
+    def test_percent(self):
+        out = sh("acpi -b")
+        acpi_value = int(out.split(",")[1].strip().replace('%', ''))
+        psutil_value = psutil.sensors_battery().percent
+        assert abs(acpi_value - psutil_value) < 1
+
+    def test_emulate_power_plugged(self):
+        # Pretend the AC power cable is connected.
+        def open_mock(name, *args, **kwargs):
+            if name.endswith(('AC0/online', 'AC/online')):
+                return io.BytesIO(b"1")
+            else:
+                return orig_open(name, *args, **kwargs)
+
+        orig_open = open
+        with mock.patch("builtins.open", side_effect=open_mock) as m:
+            assert psutil.sensors_battery().power_plugged is True
+            assert (
+                psutil.sensors_battery().secsleft
+                == psutil.POWER_TIME_UNLIMITED
+            )
+            assert m.called
+
+    def test_emulate_power_plugged_2(self):
+        # Same as above but pretend /AC0/online does not exist in which
+        # case code relies on /status file.
+        def open_mock(name, *args, **kwargs):
+            if name.endswith(('AC0/online', 'AC/online')):
+                raise FileNotFoundError
+            if name.endswith("/status"):
+                return io.StringIO("charging")
+            else:
+                return orig_open(name, *args, **kwargs)
+
+        orig_open = open
+        with mock.patch("builtins.open", side_effect=open_mock) as m:
+            assert psutil.sensors_battery().power_plugged is True
+            assert m.called
+
+    def test_emulate_power_not_plugged(self):
+        # Pretend the AC power cable is not connected.
+        def open_mock(name, *args, **kwargs):
+            if name.endswith(('AC0/online', 'AC/online')):
+                return io.BytesIO(b"0")
+            else:
+                return orig_open(name, *args, **kwargs)
+
+        orig_open = open
+        with mock.patch("builtins.open", side_effect=open_mock) as m:
+            assert psutil.sensors_battery().power_plugged is False
+            assert m.called
+
+    def test_emulate_power_not_plugged_2(self):
+        # Same as above but pretend /AC0/online does not exist in which
+        # case code relies on /status file.
+        def open_mock(name, *args, **kwargs):
+            if name.endswith(('AC0/online', 'AC/online')):
+                raise FileNotFoundError
+            if name.endswith("/status"):
+                return io.StringIO("discharging")
+            else:
+                return orig_open(name, *args, **kwargs)
+
+        orig_open = open
+        with mock.patch("builtins.open", side_effect=open_mock) as m:
+            assert psutil.sensors_battery().power_plugged is False
+            assert m.called
+
+    def test_emulate_power_undetermined(self):
+        # Pretend we can't know whether the AC power cable not
+        # connected (assert fallback to False).
+        def open_mock(name, *args, **kwargs):
+            if name.startswith((
+                '/sys/class/power_supply/AC0/online',
+                '/sys/class/power_supply/AC/online',
+            )):
+                raise FileNotFoundError
+            if name.startswith("/sys/class/power_supply/BAT0/status"):
+                return io.BytesIO(b"???")
+            else:
+                return orig_open(name, *args, **kwargs)
+
+        orig_open = open
+        with mock.patch("builtins.open", side_effect=open_mock) as m:
+            assert psutil.sensors_battery().power_plugged is None
+            assert m.called
+
+    def test_emulate_energy_full_0(self):
+        # Emulate a case where energy_full files returns 0.
+        with mock_open_content(
+            {"/sys/class/power_supply/BAT0/energy_full": b"0"}
+        ) as m:
+            assert psutil.sensors_battery().percent == 0
+            assert m.called
+
+    def test_emulate_energy_full_not_avail(self):
+        # Emulate a case where energy_full file does not exist.
+        # Expected fallback on /capacity.
+        with mock_open_exception(
+            "/sys/class/power_supply/BAT0/energy_full",
+            FileNotFoundError,
+        ):
+            with mock_open_exception(
+                "/sys/class/power_supply/BAT0/charge_full",
+                FileNotFoundError,
+            ):
+                with mock_open_content(
+                    {"/sys/class/power_supply/BAT0/capacity": b"88"}
+                ):
+                    assert psutil.sensors_battery().percent == 88
+
+    def test_emulate_no_power(self):
+        # Emulate a case where /AC0/online file nor /BAT0/status exist.
+        with mock_open_exception(
+            "/sys/class/power_supply/AC/online", FileNotFoundError
+        ):
+            with mock_open_exception(
+                "/sys/class/power_supply/AC0/online", FileNotFoundError
+            ):
+                with mock_open_exception(
+                    "/sys/class/power_supply/BAT0/status",
+                    FileNotFoundError,
+                ):
+                    assert psutil.sensors_battery().power_plugged is None
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSensorsBatteryEmulated(PsutilTestCase):
+    def test_it(self):
+        def open_mock(name, *args, **kwargs):
+            if name.endswith("/energy_now"):
+                return io.StringIO("60000000")
+            elif name.endswith("/power_now"):
+                return io.StringIO("0")
+            elif name.endswith("/energy_full"):
+                return io.StringIO("60000001")
+            else:
+                return orig_open(name, *args, **kwargs)
+
+        orig_open = open
+        with mock.patch('os.listdir', return_value=["BAT0"]) as mlistdir:
+            with mock.patch("builtins.open", side_effect=open_mock) as mopen:
+                assert psutil.sensors_battery() is not None
+        assert mlistdir.called
+        assert mopen.called
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSensorsTemperatures(PsutilTestCase):
+    def test_emulate_class_hwmon(self):
+        def open_mock(name, *args, **kwargs):
+            if name.endswith('/name'):
+                return io.StringIO("name")
+            elif name.endswith('/temp1_label'):
+                return io.StringIO("label")
+            elif name.endswith('/temp1_input'):
+                return io.BytesIO(b"30000")
+            elif name.endswith('/temp1_max'):
+                return io.BytesIO(b"40000")
+            elif name.endswith('/temp1_crit'):
+                return io.BytesIO(b"50000")
+            else:
+                return orig_open(name, *args, **kwargs)
+
+        orig_open = open
+        with mock.patch("builtins.open", side_effect=open_mock):
+            # Test case with /sys/class/hwmon
+            with mock.patch(
+                'glob.glob', return_value=['/sys/class/hwmon/hwmon0/temp1']
+            ):
+                temp = psutil.sensors_temperatures()['name'][0]
+                assert temp.label == 'label'
+                assert temp.current == 30.0
+                assert temp.high == 40.0
+                assert temp.critical == 50.0
+
+    def test_emulate_class_thermal(self):
+        def open_mock(name, *args, **kwargs):
+            if name.endswith('0_temp'):
+                return io.BytesIO(b"50000")
+            elif name.endswith('temp'):
+                return io.BytesIO(b"30000")
+            elif name.endswith('0_type'):
+                return io.StringIO("critical")
+            elif name.endswith('type'):
+                return io.StringIO("name")
+            else:
+                return orig_open(name, *args, **kwargs)
+
+        def glob_mock(path):
+            if path in {
+                '/sys/class/hwmon/hwmon*/temp*_*',
+                '/sys/class/hwmon/hwmon*/device/temp*_*',
+            }:
+                return []
+            elif path == '/sys/class/thermal/thermal_zone*':
+                return ['/sys/class/thermal/thermal_zone0']
+            elif path == '/sys/class/thermal/thermal_zone0/trip_point*':
+                return [
+                    '/sys/class/thermal/thermal_zone1/trip_point_0_type',
+                    '/sys/class/thermal/thermal_zone1/trip_point_0_temp',
+                ]
+            return []
+
+        orig_open = open
+        with mock.patch("builtins.open", side_effect=open_mock):
+            with mock.patch('glob.glob', create=True, side_effect=glob_mock):
+                temp = psutil.sensors_temperatures()['name'][0]
+                assert temp.label == ''
+                assert temp.current == 30.0
+                assert temp.high == 50.0
+                assert temp.critical == 50.0
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestSensorsFans(PsutilTestCase):
+    def test_emulate_data(self):
+        def open_mock(name, *args, **kwargs):
+            if name.endswith('/name'):
+                return io.StringIO("name")
+            elif name.endswith('/fan1_label'):
+                return io.StringIO("label")
+            elif name.endswith('/fan1_input'):
+                return io.StringIO("2000")
+            else:
+                return orig_open(name, *args, **kwargs)
+
+        orig_open = open
+        with mock.patch("builtins.open", side_effect=open_mock):
+            with mock.patch(
+                'glob.glob', return_value=['/sys/class/hwmon/hwmon2/fan1']
+            ):
+                fan = psutil.sensors_fans()['name'][0]
+                assert fan.label == 'label'
+                assert fan.current == 2000
+
+
+# =====================================================================
+# --- test process
+# =====================================================================
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestProcess(PsutilTestCase):
+    @retry_on_failure()
+    def test_parse_smaps_vs_memory_maps(self):
+        sproc = self.spawn_testproc()
+        uss, pss, swap = psutil._pslinux.Process(sproc.pid)._parse_smaps()
+        maps = psutil.Process(sproc.pid).memory_maps(grouped=False)
+        assert (
+            abs(uss - sum(x.private_dirty + x.private_clean for x in maps))
+            < 4096
+        )
+        assert abs(pss - sum(x.pss for x in maps)) < 4096
+        assert abs(swap - sum(x.swap for x in maps)) < 4096
+
+    def test_parse_smaps_mocked(self):
+        # See: https://github.com/giampaolo/psutil/issues/1222
+        content = textwrap.dedent("""\
+            fffff0 r-xp 00000000 00:00 0                  [vsyscall]
+            Size:                  1 kB
+            Rss:                   2 kB
+            Pss:                   3 kB
+            Shared_Clean:          4 kB
+            Shared_Dirty:          5 kB
+            Private_Clean:         6 kB
+            Private_Dirty:         7 kB
+            Referenced:            8 kB
+            Anonymous:             9 kB
+            LazyFree:              10 kB
+            AnonHugePages:         11 kB
+            ShmemPmdMapped:        12 kB
+            Shared_Hugetlb:        13 kB
+            Private_Hugetlb:       14 kB
+            Swap:                  15 kB
+            SwapPss:               16 kB
+            KernelPageSize:        17 kB
+            MMUPageSize:           18 kB
+            Locked:                19 kB
+            VmFlags: rd ex
+            """).encode()
+        with mock_open_content({f"/proc/{os.getpid()}/smaps": content}) as m:
+            p = psutil._pslinux.Process(os.getpid())
+            uss, pss, swap = p._parse_smaps()
+            assert m.called
+            assert uss == (6 + 7 + 14) * 1024
+            assert pss == 3 * 1024
+            assert swap == 15 * 1024
+
+    # On PYPY file descriptors are not closed fast enough.
+    @pytest.mark.skipif(PYPY, reason="unreliable on PYPY")
+    def test_open_files_mode(self):
+        def get_test_file(fname):
+            p = psutil.Process()
+            giveup_at = time.time() + GLOBAL_TIMEOUT
+            while True:
+                for file in p.open_files():
+                    if file.path == os.path.abspath(fname):
+                        return file
+                    elif time.time() > giveup_at:
+                        break
+            raise RuntimeError("timeout looking for test file")
+
+        testfn = self.get_testfn()
+        with open(testfn, "w"):
+            assert get_test_file(testfn).mode == "w"
+        with open(testfn):
+            assert get_test_file(testfn).mode == "r"
+        with open(testfn, "a"):
+            assert get_test_file(testfn).mode == "a"
+        with open(testfn, "r+"):
+            assert get_test_file(testfn).mode == "r+"
+        with open(testfn, "w+"):
+            assert get_test_file(testfn).mode == "r+"
+        with open(testfn, "a+"):
+            assert get_test_file(testfn).mode == "a+"
+
+        safe_rmpath(testfn)
+        with open(testfn, "x"):
+            assert get_test_file(testfn).mode == "w"
+        safe_rmpath(testfn)
+        with open(testfn, "x+"):
+            assert get_test_file(testfn).mode == "r+"
+
+    def test_open_files_file_gone(self):
+        # simulates a file which gets deleted during open_files()
+        # execution
+        p = psutil.Process()
+        files = p.open_files()
+        with open(self.get_testfn(), 'w'):
+            # give the kernel some time to see the new file
+            call_until(lambda: len(p.open_files()) != len(files))
+            with mock.patch(
+                'psutil._pslinux.os.readlink',
+                side_effect=FileNotFoundError,
+            ) as m:
+                assert p.open_files() == []
+                assert m.called
+            # also simulate the case where os.readlink() returns EINVAL
+            # in which case psutil is supposed to 'continue'
+            with mock.patch(
+                'psutil._pslinux.os.readlink',
+                side_effect=OSError(errno.EINVAL, ""),
+            ) as m:
+                assert p.open_files() == []
+                assert m.called
+
+    def test_open_files_fd_gone(self):
+        # Simulate a case where /proc/{pid}/fdinfo/{fd} disappears
+        # while iterating through fds.
+        # https://travis-ci.org/giampaolo/psutil/jobs/225694530
+        p = psutil.Process()
+        files = p.open_files()
+        with open(self.get_testfn(), 'w'):
+            # give the kernel some time to see the new file
+            call_until(lambda: len(p.open_files()) != len(files))
+            with mock.patch(
+                "builtins.open", side_effect=FileNotFoundError
+            ) as m:
+                assert p.open_files() == []
+                assert m.called
+
+    def test_open_files_enametoolong(self):
+        # Simulate a case where /proc/{pid}/fd/{fd} symlink
+        # points to a file with full path longer than PATH_MAX, see:
+        # https://github.com/giampaolo/psutil/issues/1940
+        p = psutil.Process()
+        files = p.open_files()
+        with open(self.get_testfn(), 'w'):
+            # give the kernel some time to see the new file
+            call_until(lambda: len(p.open_files()) != len(files))
+            patch_point = 'psutil._pslinux.os.readlink'
+            with mock.patch(
+                patch_point, side_effect=OSError(errno.ENAMETOOLONG, "")
+            ) as m:
+                with mock.patch("psutil._pslinux.debug"):
+                    assert p.open_files() == []
+                    assert m.called
+
+    # --- mocked tests
+
+    def test_terminal_mocked(self):
+        with mock.patch(
+            'psutil._pslinux._psposix.get_terminal_map', return_value={}
+        ) as m:
+            assert psutil._pslinux.Process(os.getpid()).terminal() is None
+            assert m.called
+
+    # TODO: re-enable this test.
+    # def test_num_ctx_switches_mocked(self):
+    #     with mock.patch('psutil._common.open', create=True) as m:
+    #         self.assertRaises(
+    #             NotImplementedError,
+    #             psutil._pslinux.Process(os.getpid()).num_ctx_switches)
+    #         assert m.called
+
+    def test_cmdline_mocked(self):
+        # see: https://github.com/giampaolo/psutil/issues/639
+        p = psutil.Process()
+        fake_file = io.StringIO('foo\x00bar\x00')
+        with mock.patch(
+            'psutil._common.open', return_value=fake_file, create=True
+        ) as m:
+            assert p.cmdline() == ['foo', 'bar']
+            assert m.called
+        fake_file = io.StringIO('foo\x00bar\x00\x00')
+        with mock.patch(
+            'psutil._common.open', return_value=fake_file, create=True
+        ) as m:
+            assert p.cmdline() == ['foo', 'bar', '']
+            assert m.called
+
+    def test_cmdline_spaces_mocked(self):
+        # see: https://github.com/giampaolo/psutil/issues/1179
+        p = psutil.Process()
+        fake_file = io.StringIO('foo bar ')
+        with mock.patch(
+            'psutil._common.open', return_value=fake_file, create=True
+        ) as m:
+            assert p.cmdline() == ['foo', 'bar']
+            assert m.called
+        fake_file = io.StringIO('foo bar  ')
+        with mock.patch(
+            'psutil._common.open', return_value=fake_file, create=True
+        ) as m:
+            assert p.cmdline() == ['foo', 'bar', '']
+            assert m.called
+
+    def test_cmdline_mixed_separators(self):
+        # https://github.com/giampaolo/psutil/issues/
+        #    1179#issuecomment-552984549
+        p = psutil.Process()
+        fake_file = io.StringIO('foo\x20bar\x00')
+        with mock.patch(
+            'psutil._common.open', return_value=fake_file, create=True
+        ) as m:
+            assert p.cmdline() == ['foo', 'bar']
+            assert m.called
+
+    def test_readlink_path_deleted_mocked(self):
+        with mock.patch(
+            'psutil._pslinux.os.readlink', return_value='/home/foo (deleted)'
+        ):
+            assert psutil.Process().exe() == "/home/foo"
+            assert psutil.Process().cwd() == "/home/foo"
+
+    def test_threads_mocked(self):
+        # Test the case where os.listdir() returns a file (thread)
+        # which no longer exists by the time we open() it (race
+        # condition). threads() is supposed to ignore that instead
+        # of raising NSP.
+        def open_mock_1(name, *args, **kwargs):
+            if name.startswith(f"/proc/{os.getpid()}/task"):
+                raise FileNotFoundError
+            return orig_open(name, *args, **kwargs)
+
+        orig_open = open
+        with mock.patch("builtins.open", side_effect=open_mock_1) as m:
+            ret = psutil.Process().threads()
+            assert m.called
+            assert ret == []
+
+        # ...but if it bumps into something != ENOENT we want an
+        # exception.
+        def open_mock_2(name, *args, **kwargs):
+            if name.startswith(f"/proc/{os.getpid()}/task"):
+                raise PermissionError
+            return orig_open(name, *args, **kwargs)
+
+        with mock.patch("builtins.open", side_effect=open_mock_2):
+            with pytest.raises(psutil.AccessDenied):
+                psutil.Process().threads()
+
+    def test_exe_mocked(self):
+        with mock.patch(
+            'psutil._pslinux.readlink', side_effect=FileNotFoundError
+        ) as m:
+            # de-activate guessing from cmdline()
+            with mock.patch(
+                'psutil._pslinux.Process.cmdline', return_value=[]
+            ):
+                ret = psutil.Process().exe()
+                assert m.called
+                assert ret == ""
+
+    def test_issue_1014(self):
+        # Emulates a case where smaps file does not exist. In this case
+        # wrap_exception decorator should not raise NoSuchProcess.
+        with mock_open_exception(
+            f"/proc/{os.getpid()}/smaps", FileNotFoundError
+        ) as m:
+            p = psutil.Process()
+            with pytest.raises(FileNotFoundError):
+                p.memory_maps()
+            assert m.called
+
+    def test_issue_2418(self):
+        p = psutil.Process()
+        with mock_open_exception(
+            f"/proc/{os.getpid()}/statm", FileNotFoundError
+        ):
+            with mock.patch("os.path.exists", return_value=False):
+                with pytest.raises(psutil.NoSuchProcess):
+                    p.memory_info()
+
+    @pytest.mark.skipif(not HAS_RLIMIT, reason="not supported")
+    def test_rlimit_zombie(self):
+        # Emulate a case where rlimit() raises ENOSYS, which may
+        # happen in case of zombie process:
+        # https://travis-ci.org/giampaolo/psutil/jobs/51368273
+        with mock.patch(
+            "resource.prlimit", side_effect=OSError(errno.ENOSYS, "")
+        ) as m1:
+            with mock.patch(
+                "psutil._pslinux.Process._is_zombie", return_value=True
+            ) as m2:
+                p = psutil.Process()
+                p.name()
+                with pytest.raises(psutil.ZombieProcess) as cm:
+                    p.rlimit(psutil.RLIMIT_NOFILE)
+        assert m1.called
+        assert m2.called
+        assert cm.value.pid == p.pid
+        assert cm.value.name == p.name()
+
+    def test_stat_file_parsing(self):
+        args = [
+            "0",  # pid
+            "(cat)",  # name
+            "Z",  # status
+            "1",  # ppid
+            "0",  # pgrp
+            "0",  # session
+            "0",  # tty
+            "0",  # tpgid
+            "0",  # flags
+            "0",  # minflt
+            "0",  # cminflt
+            "0",  # majflt
+            "0",  # cmajflt
+            "2",  # utime
+            "3",  # stime
+            "4",  # cutime
+            "5",  # cstime
+            "0",  # priority
+            "0",  # nice
+            "0",  # num_threads
+            "0",  # itrealvalue
+            "6",  # starttime
+            "0",  # vsize
+            "0",  # rss
+            "0",  # rsslim
+            "0",  # startcode
+            "0",  # endcode
+            "0",  # startstack
+            "0",  # kstkesp
+            "0",  # kstkeip
+            "0",  # signal
+            "0",  # blocked
+            "0",  # sigignore
+            "0",  # sigcatch
+            "0",  # wchan
+            "0",  # nswap
+            "0",  # cnswap
+            "0",  # exit_signal
+            "6",  # processor
+            "0",  # rt priority
+            "0",  # policy
+            "7",  # delayacct_blkio_ticks
+        ]
+        content = " ".join(args).encode()
+        with mock_open_content({f"/proc/{os.getpid()}/stat": content}):
+            p = psutil.Process()
+            assert p.name() == 'cat'
+            assert p.status() == psutil.STATUS_ZOMBIE
+            assert p.ppid() == 1
+            assert p.create_time() == 6 / CLOCK_TICKS + psutil.boot_time()
+            cpu = p.cpu_times()
+            assert cpu.user == 2 / CLOCK_TICKS
+            assert cpu.system == 3 / CLOCK_TICKS
+            assert cpu.children_user == 4 / CLOCK_TICKS
+            assert cpu.children_system == 5 / CLOCK_TICKS
+            assert cpu.iowait == 7 / CLOCK_TICKS
+            assert p.cpu_num() == 6
+
+    def test_status_file_parsing(self):
+        content = textwrap.dedent("""\
+            Uid:\t1000\t1001\t1002\t1003
+            Gid:\t1004\t1005\t1006\t1007
+            Threads:\t66
+            Cpus_allowed:\tf
+            Cpus_allowed_list:\t0-7
+            voluntary_ctxt_switches:\t12
+            nonvoluntary_ctxt_switches:\t13""").encode()
+        with mock_open_content({f"/proc/{os.getpid()}/status": content}):
+            p = psutil.Process()
+            assert p.num_ctx_switches().voluntary == 12
+            assert p.num_ctx_switches().involuntary == 13
+            assert p.num_threads() == 66
+            uids = p.uids()
+            assert uids.real == 1000
+            assert uids.effective == 1001
+            assert uids.saved == 1002
+            gids = p.gids()
+            assert gids.real == 1004
+            assert gids.effective == 1005
+            assert gids.saved == 1006
+            assert p._proc._get_eligible_cpus() == list(range(8))
+
+    def test_net_connections_enametoolong(self):
+        # Simulate a case where /proc/{pid}/fd/{fd} symlink points to
+        # a file with full path longer than PATH_MAX, see:
+        # https://github.com/giampaolo/psutil/issues/1940
+        with mock.patch(
+            'psutil._pslinux.os.readlink',
+            side_effect=OSError(errno.ENAMETOOLONG, ""),
+        ) as m:
+            p = psutil.Process()
+            with mock.patch("psutil._pslinux.debug"):
+                assert p.net_connections() == []
+                assert m.called
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestProcessAgainstStatus(PsutilTestCase):
+    """/proc/pid/stat and /proc/pid/status have many values in common.
+    Whenever possible, psutil uses /proc/pid/stat (it's faster).
+    For all those cases we check that the value found in
+    /proc/pid/stat (by psutil) matches the one found in
+    /proc/pid/status.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        cls.proc = psutil.Process()
+
+    def read_status_file(self, linestart):
+        with psutil._psplatform.open_text(
+            f"/proc/{self.proc.pid}/status"
+        ) as f:
+            for line in f:
+                line = line.strip()
+                if line.startswith(linestart):
+                    value = line.partition('\t')[2]
+                    try:
+                        return int(value)
+                    except ValueError:
+                        return value
+            raise ValueError(f"can't find {linestart!r}")
+
+    def test_name(self):
+        value = self.read_status_file("Name:")
+        assert self.proc.name() == value
+
+    def test_status(self):
+        value = self.read_status_file("State:")
+        value = value[value.find('(') + 1 : value.rfind(')')]
+        value = value.replace(' ', '-')
+        assert self.proc.status() == value
+
+    def test_ppid(self):
+        value = self.read_status_file("PPid:")
+        assert self.proc.ppid() == value
+
+    def test_num_threads(self):
+        value = self.read_status_file("Threads:")
+        assert self.proc.num_threads() == value
+
+    def test_uids(self):
+        value = self.read_status_file("Uid:")
+        value = tuple(map(int, value.split()[1:4]))
+        assert self.proc.uids() == value
+
+    def test_gids(self):
+        value = self.read_status_file("Gid:")
+        value = tuple(map(int, value.split()[1:4]))
+        assert self.proc.gids() == value
+
+    @retry_on_failure()
+    def test_num_ctx_switches(self):
+        value = self.read_status_file("voluntary_ctxt_switches:")
+        assert self.proc.num_ctx_switches().voluntary == value
+        value = self.read_status_file("nonvoluntary_ctxt_switches:")
+        assert self.proc.num_ctx_switches().involuntary == value
+
+    def test_cpu_affinity(self):
+        value = self.read_status_file("Cpus_allowed_list:")
+        if '-' in str(value):
+            min_, max_ = map(int, value.split('-'))
+            assert self.proc.cpu_affinity() == list(range(min_, max_ + 1))
+
+    def test_cpu_affinity_eligible_cpus(self):
+        value = self.read_status_file("Cpus_allowed_list:")
+        with mock.patch("psutil._pslinux.per_cpu_times") as m:
+            self.proc._proc._get_eligible_cpus()
+        if '-' in str(value):
+            assert not m.called
+        else:
+            assert m.called
+
+
+# =====================================================================
+# --- test utils
+# =====================================================================
+
+
+@pytest.mark.skipif(not LINUX, reason="LINUX only")
+class TestUtils(PsutilTestCase):
+    def test_readlink(self):
+        with mock.patch("os.readlink", return_value="foo (deleted)") as m:
+            assert psutil._psplatform.readlink("bar") == "foo"
+            assert m.called
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_memleaks.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_memleaks.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f78fae67cbce20c4ec31e3703406f5135860a8e
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_memleaks.py
@@ -0,0 +1,487 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Tests for detecting function memory leaks (typically the ones
+implemented in C). It does so by calling a function many times and
+checking whether process memory usage keeps increasing between
+calls or over time.
+Note that this may produce false positives (especially on Windows
+for some reason).
+PyPy appears to be completely unstable for this framework, probably
+because of how its JIT handles memory, so tests are skipped.
+"""
+
+
+import functools
+import os
+import platform
+
+import psutil
+import psutil._common
+from psutil import LINUX
+from psutil import MACOS
+from psutil import OPENBSD
+from psutil import POSIX
+from psutil import SUNOS
+from psutil import WINDOWS
+from psutil.tests import HAS_CPU_AFFINITY
+from psutil.tests import HAS_CPU_FREQ
+from psutil.tests import HAS_ENVIRON
+from psutil.tests import HAS_IONICE
+from psutil.tests import HAS_MEMORY_MAPS
+from psutil.tests import HAS_NET_IO_COUNTERS
+from psutil.tests import HAS_PROC_CPU_NUM
+from psutil.tests import HAS_PROC_IO_COUNTERS
+from psutil.tests import HAS_RLIMIT
+from psutil.tests import HAS_SENSORS_BATTERY
+from psutil.tests import HAS_SENSORS_FANS
+from psutil.tests import HAS_SENSORS_TEMPERATURES
+from psutil.tests import TestMemoryLeak
+from psutil.tests import create_sockets
+from psutil.tests import get_testfn
+from psutil.tests import process_namespace
+from psutil.tests import pytest
+from psutil.tests import skip_on_access_denied
+from psutil.tests import spawn_testproc
+from psutil.tests import system_namespace
+from psutil.tests import terminate
+
+
+cext = psutil._psplatform.cext
+thisproc = psutil.Process()
+FEW_TIMES = 5
+
+
+def fewtimes_if_linux():
+    """Decorator for those Linux functions which are implemented in pure
+    Python, and which we want to run faster.
+    """
+
+    def decorator(fun):
+        @functools.wraps(fun)
+        def wrapper(self, *args, **kwargs):
+            if LINUX:
+                before = self.__class__.times
+                try:
+                    self.__class__.times = FEW_TIMES
+                    return fun(self, *args, **kwargs)
+                finally:
+                    self.__class__.times = before
+            else:
+                return fun(self, *args, **kwargs)
+
+        return wrapper
+
+    return decorator
+
+
+# ===================================================================
+# Process class
+# ===================================================================
+
+
+class TestProcessObjectLeaks(TestMemoryLeak):
+    """Test leaks of Process class methods."""
+
+    proc = thisproc
+
+    def test_coverage(self):
+        ns = process_namespace(None)
+        ns.test_class_coverage(self, ns.getters + ns.setters)
+
+    @fewtimes_if_linux()
+    def test_name(self):
+        self.execute(self.proc.name)
+
+    @fewtimes_if_linux()
+    def test_cmdline(self):
+        self.execute(self.proc.cmdline)
+
+    @fewtimes_if_linux()
+    def test_exe(self):
+        self.execute(self.proc.exe)
+
+    @fewtimes_if_linux()
+    def test_ppid(self):
+        self.execute(self.proc.ppid)
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    @fewtimes_if_linux()
+    def test_uids(self):
+        self.execute(self.proc.uids)
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    @fewtimes_if_linux()
+    def test_gids(self):
+        self.execute(self.proc.gids)
+
+    @fewtimes_if_linux()
+    def test_status(self):
+        self.execute(self.proc.status)
+
+    def test_nice(self):
+        self.execute(self.proc.nice)
+
+    def test_nice_set(self):
+        niceness = thisproc.nice()
+        self.execute(lambda: self.proc.nice(niceness))
+
+    @pytest.mark.skipif(not HAS_IONICE, reason="not supported")
+    def test_ionice(self):
+        self.execute(self.proc.ionice)
+
+    @pytest.mark.skipif(not HAS_IONICE, reason="not supported")
+    def test_ionice_set(self):
+        if WINDOWS:
+            value = thisproc.ionice()
+            self.execute(lambda: self.proc.ionice(value))
+        else:
+            self.execute(lambda: self.proc.ionice(psutil.IOPRIO_CLASS_NONE))
+            fun = functools.partial(cext.proc_ioprio_set, os.getpid(), -1, 0)
+            self.execute_w_exc(OSError, fun)
+
+    @pytest.mark.skipif(not HAS_PROC_IO_COUNTERS, reason="not supported")
+    @fewtimes_if_linux()
+    def test_io_counters(self):
+        self.execute(self.proc.io_counters)
+
+    @pytest.mark.skipif(POSIX, reason="worthless on POSIX")
+    def test_username(self):
+        # always open 1 handle on Windows (only once)
+        psutil.Process().username()
+        self.execute(self.proc.username)
+
+    @fewtimes_if_linux()
+    def test_create_time(self):
+        self.execute(self.proc.create_time)
+
+    @fewtimes_if_linux()
+    @skip_on_access_denied(only_if=OPENBSD)
+    def test_num_threads(self):
+        self.execute(self.proc.num_threads)
+
+    @pytest.mark.skipif(not WINDOWS, reason="WINDOWS only")
+    def test_num_handles(self):
+        self.execute(self.proc.num_handles)
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    @fewtimes_if_linux()
+    def test_num_fds(self):
+        self.execute(self.proc.num_fds)
+
+    @fewtimes_if_linux()
+    def test_num_ctx_switches(self):
+        self.execute(self.proc.num_ctx_switches)
+
+    @fewtimes_if_linux()
+    @skip_on_access_denied(only_if=OPENBSD)
+    def test_threads(self):
+        self.execute(self.proc.threads)
+
+    @fewtimes_if_linux()
+    def test_cpu_times(self):
+        self.execute(self.proc.cpu_times)
+
+    @fewtimes_if_linux()
+    @pytest.mark.skipif(not HAS_PROC_CPU_NUM, reason="not supported")
+    def test_cpu_num(self):
+        self.execute(self.proc.cpu_num)
+
+    @fewtimes_if_linux()
+    def test_memory_info(self):
+        self.execute(self.proc.memory_info)
+
+    @fewtimes_if_linux()
+    def test_memory_full_info(self):
+        self.execute(self.proc.memory_full_info)
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    @fewtimes_if_linux()
+    def test_terminal(self):
+        self.execute(self.proc.terminal)
+
+    def test_resume(self):
+        times = FEW_TIMES if POSIX else self.times
+        self.execute(self.proc.resume, times=times)
+
+    @fewtimes_if_linux()
+    def test_cwd(self):
+        self.execute(self.proc.cwd)
+
+    @pytest.mark.skipif(not HAS_CPU_AFFINITY, reason="not supported")
+    def test_cpu_affinity(self):
+        self.execute(self.proc.cpu_affinity)
+
+    @pytest.mark.skipif(not HAS_CPU_AFFINITY, reason="not supported")
+    def test_cpu_affinity_set(self):
+        affinity = thisproc.cpu_affinity()
+        self.execute(lambda: self.proc.cpu_affinity(affinity))
+        self.execute_w_exc(ValueError, lambda: self.proc.cpu_affinity([-1]))
+
+    @fewtimes_if_linux()
+    def test_open_files(self):
+        with open(get_testfn(), 'w'):
+            self.execute(self.proc.open_files)
+
+    @pytest.mark.skipif(not HAS_MEMORY_MAPS, reason="not supported")
+    @fewtimes_if_linux()
+    def test_memory_maps(self):
+        self.execute(self.proc.memory_maps)
+
+    @pytest.mark.skipif(not LINUX, reason="LINUX only")
+    @pytest.mark.skipif(not HAS_RLIMIT, reason="not supported")
+    def test_rlimit(self):
+        self.execute(lambda: self.proc.rlimit(psutil.RLIMIT_NOFILE))
+
+    @pytest.mark.skipif(not LINUX, reason="LINUX only")
+    @pytest.mark.skipif(not HAS_RLIMIT, reason="not supported")
+    def test_rlimit_set(self):
+        limit = thisproc.rlimit(psutil.RLIMIT_NOFILE)
+        self.execute(lambda: self.proc.rlimit(psutil.RLIMIT_NOFILE, limit))
+        self.execute_w_exc((OSError, ValueError), lambda: self.proc.rlimit(-1))
+
+    @fewtimes_if_linux()
+    # Windows implementation is based on a single system-wide
+    # function (tested later).
+    @pytest.mark.skipif(WINDOWS, reason="worthless on WINDOWS")
+    def test_net_connections(self):
+        # TODO: UNIX sockets are temporarily implemented by parsing
+        # 'pfiles' cmd  output; we don't want that part of the code to
+        # be executed.
+        with create_sockets():
+            kind = 'inet' if SUNOS else 'all'
+            self.execute(lambda: self.proc.net_connections(kind))
+
+    @pytest.mark.skipif(not HAS_ENVIRON, reason="not supported")
+    def test_environ(self):
+        self.execute(self.proc.environ)
+
+    @pytest.mark.skipif(not WINDOWS, reason="WINDOWS only")
+    def test_proc_info(self):
+        self.execute(lambda: cext.proc_info(os.getpid()))
+
+
+class TestTerminatedProcessLeaks(TestProcessObjectLeaks):
+    """Repeat the tests above looking for leaks occurring when dealing
+    with terminated processes raising NoSuchProcess exception.
+    The C functions are still invoked but will follow different code
+    paths. We'll check those code paths.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.subp = spawn_testproc()
+        cls.proc = psutil.Process(cls.subp.pid)
+        cls.proc.kill()
+        cls.proc.wait()
+
+    @classmethod
+    def tearDownClass(cls):
+        super().tearDownClass()
+        terminate(cls.subp)
+
+    def call(self, fun):
+        try:
+            fun()
+        except psutil.NoSuchProcess:
+            pass
+
+    if WINDOWS:
+
+        def test_kill(self):
+            self.execute(self.proc.kill)
+
+        def test_terminate(self):
+            self.execute(self.proc.terminate)
+
+        def test_suspend(self):
+            self.execute(self.proc.suspend)
+
+        def test_resume(self):
+            self.execute(self.proc.resume)
+
+        def test_wait(self):
+            self.execute(self.proc.wait)
+
+        def test_proc_info(self):
+            # test dual implementation
+            def call():
+                try:
+                    return cext.proc_info(self.proc.pid)
+                except ProcessLookupError:
+                    pass
+
+            self.execute(call)
+
+
+@pytest.mark.skipif(not WINDOWS, reason="WINDOWS only")
+class TestProcessDualImplementation(TestMemoryLeak):
+    def test_cmdline_peb_true(self):
+        self.execute(lambda: cext.proc_cmdline(os.getpid(), use_peb=True))
+
+    def test_cmdline_peb_false(self):
+        self.execute(lambda: cext.proc_cmdline(os.getpid(), use_peb=False))
+
+
+# ===================================================================
+# system APIs
+# ===================================================================
+
+
+class TestModuleFunctionsLeaks(TestMemoryLeak):
+    """Test leaks of psutil module functions."""
+
+    def test_coverage(self):
+        ns = system_namespace()
+        ns.test_class_coverage(self, ns.all)
+
+    # --- cpu
+
+    @fewtimes_if_linux()
+    def test_cpu_count(self):  # logical
+        self.execute(lambda: psutil.cpu_count(logical=True))
+
+    @fewtimes_if_linux()
+    def test_cpu_count_cores(self):
+        self.execute(lambda: psutil.cpu_count(logical=False))
+
+    @fewtimes_if_linux()
+    def test_cpu_times(self):
+        self.execute(psutil.cpu_times)
+
+    @fewtimes_if_linux()
+    def test_per_cpu_times(self):
+        self.execute(lambda: psutil.cpu_times(percpu=True))
+
+    @fewtimes_if_linux()
+    def test_cpu_stats(self):
+        self.execute(psutil.cpu_stats)
+
+    @fewtimes_if_linux()
+    # TODO: remove this once 1892 is fixed
+    @pytest.mark.skipif(
+        MACOS and platform.machine() == 'arm64', reason="skipped due to #1892"
+    )
+    @pytest.mark.skipif(not HAS_CPU_FREQ, reason="not supported")
+    def test_cpu_freq(self):
+        self.execute(psutil.cpu_freq)
+
+    @pytest.mark.skipif(not WINDOWS, reason="WINDOWS only")
+    def test_getloadavg(self):
+        psutil.getloadavg()
+        self.execute(psutil.getloadavg)
+
+    # --- mem
+
+    def test_virtual_memory(self):
+        self.execute(psutil.virtual_memory)
+
+    # TODO: remove this skip when this gets fixed
+    @pytest.mark.skipif(SUNOS, reason="worthless on SUNOS (uses a subprocess)")
+    def test_swap_memory(self):
+        self.execute(psutil.swap_memory)
+
+    def test_pid_exists(self):
+        times = FEW_TIMES if POSIX else self.times
+        self.execute(lambda: psutil.pid_exists(os.getpid()), times=times)
+
+    # --- disk
+
+    def test_disk_usage(self):
+        times = FEW_TIMES if POSIX else self.times
+        self.execute(lambda: psutil.disk_usage('.'), times=times)
+
+    def test_disk_partitions(self):
+        self.execute(psutil.disk_partitions)
+
+    @pytest.mark.skipif(
+        LINUX and not os.path.exists('/proc/diskstats'),
+        reason="/proc/diskstats not available on this Linux version",
+    )
+    @fewtimes_if_linux()
+    def test_disk_io_counters(self):
+        self.execute(lambda: psutil.disk_io_counters(nowrap=False))
+
+    # --- proc
+
+    @fewtimes_if_linux()
+    def test_pids(self):
+        self.execute(psutil.pids)
+
+    # --- net
+
+    @fewtimes_if_linux()
+    @pytest.mark.skipif(not HAS_NET_IO_COUNTERS, reason="not supported")
+    def test_net_io_counters(self):
+        self.execute(lambda: psutil.net_io_counters(nowrap=False))
+
+    @fewtimes_if_linux()
+    @pytest.mark.skipif(MACOS and os.getuid() != 0, reason="need root access")
+    def test_net_connections(self):
+        # always opens and handle on Windows() (once)
+        psutil.net_connections(kind='all')
+        with create_sockets():
+            self.execute(lambda: psutil.net_connections(kind='all'))
+
+    def test_net_if_addrs(self):
+        # Note: verified that on Windows this was a false positive.
+        tolerance = 80 * 1024 if WINDOWS else self.tolerance
+        self.execute(psutil.net_if_addrs, tolerance=tolerance)
+
+    def test_net_if_stats(self):
+        self.execute(psutil.net_if_stats)
+
+    # --- sensors
+
+    @fewtimes_if_linux()
+    @pytest.mark.skipif(not HAS_SENSORS_BATTERY, reason="not supported")
+    def test_sensors_battery(self):
+        self.execute(psutil.sensors_battery)
+
+    @fewtimes_if_linux()
+    @pytest.mark.skipif(not HAS_SENSORS_TEMPERATURES, reason="not supported")
+    def test_sensors_temperatures(self):
+        self.execute(psutil.sensors_temperatures)
+
+    @fewtimes_if_linux()
+    @pytest.mark.skipif(not HAS_SENSORS_FANS, reason="not supported")
+    def test_sensors_fans(self):
+        self.execute(psutil.sensors_fans)
+
+    # --- others
+
+    @fewtimes_if_linux()
+    def test_boot_time(self):
+        self.execute(psutil.boot_time)
+
+    def test_users(self):
+        self.execute(psutil.users)
+
+    def test_set_debug(self):
+        self.execute(lambda: psutil._set_debug(False))
+
+    if WINDOWS:
+
+        # --- win services
+
+        def test_win_service_iter(self):
+            self.execute(cext.winservice_enumerate)
+
+        def test_win_service_get(self):
+            pass
+
+        def test_win_service_get_config(self):
+            name = next(psutil.win_service_iter()).name()
+            self.execute(lambda: cext.winservice_query_config(name))
+
+        def test_win_service_get_status(self):
+            name = next(psutil.win_service_iter()).name()
+            self.execute(lambda: cext.winservice_query_status(name))
+
+        def test_win_service_get_description(self):
+            name = next(psutil.win_service_iter()).name()
+            self.execute(lambda: cext.winservice_query_descr(name))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_misc.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..c484264b9190adcf4d7651a88eab53f1c354e845
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_misc.py
@@ -0,0 +1,873 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Miscellaneous tests."""
+
+import collections
+import contextlib
+import io
+import json
+import os
+import pickle
+import socket
+import sys
+from unittest import mock
+
+import psutil
+import psutil.tests
+from psutil import WINDOWS
+from psutil._common import bcat
+from psutil._common import cat
+from psutil._common import debug
+from psutil._common import isfile_strict
+from psutil._common import memoize
+from psutil._common import memoize_when_activated
+from psutil._common import parse_environ_block
+from psutil._common import supports_ipv6
+from psutil._common import wrap_numbers
+from psutil.tests import HAS_NET_IO_COUNTERS
+from psutil.tests import PsutilTestCase
+from psutil.tests import process_namespace
+from psutil.tests import pytest
+from psutil.tests import reload_module
+from psutil.tests import system_namespace
+
+
+# ===================================================================
+# --- Test classes' repr(), str(), ...
+# ===================================================================
+
+
+class TestSpecialMethods(PsutilTestCase):
+    def test_check_pid_range(self):
+        with pytest.raises(OverflowError):
+            psutil._psplatform.cext.check_pid_range(2**128)
+        with pytest.raises(psutil.NoSuchProcess):
+            psutil.Process(2**128)
+
+    def test_process__repr__(self, func=repr):
+        p = psutil.Process(self.spawn_testproc().pid)
+        r = func(p)
+        assert "psutil.Process" in r
+        assert f"pid={p.pid}" in r
+        assert f"name='{p.name()}'" in r.replace("name=u'", "name='")
+        assert "status=" in r
+        assert "exitcode=" not in r
+        p.terminate()
+        p.wait()
+        r = func(p)
+        assert "status='terminated'" in r
+        assert "exitcode=" in r
+
+        with mock.patch.object(
+            psutil.Process,
+            "name",
+            side_effect=psutil.ZombieProcess(os.getpid()),
+        ):
+            p = psutil.Process()
+            r = func(p)
+            assert f"pid={p.pid}" in r
+            assert "status='zombie'" in r
+            assert "name=" not in r
+        with mock.patch.object(
+            psutil.Process,
+            "name",
+            side_effect=psutil.NoSuchProcess(os.getpid()),
+        ):
+            p = psutil.Process()
+            r = func(p)
+            assert f"pid={p.pid}" in r
+            assert "terminated" in r
+            assert "name=" not in r
+        with mock.patch.object(
+            psutil.Process,
+            "name",
+            side_effect=psutil.AccessDenied(os.getpid()),
+        ):
+            p = psutil.Process()
+            r = func(p)
+            assert f"pid={p.pid}" in r
+            assert "name=" not in r
+
+    def test_process__str__(self):
+        self.test_process__repr__(func=str)
+
+    def test_error__repr__(self):
+        assert repr(psutil.Error()) == "psutil.Error()"
+
+    def test_error__str__(self):
+        assert str(psutil.Error()) == ""
+
+    def test_no_such_process__repr__(self):
+        assert (
+            repr(psutil.NoSuchProcess(321))
+            == "psutil.NoSuchProcess(pid=321, msg='process no longer exists')"
+        )
+        assert (
+            repr(psutil.NoSuchProcess(321, name="name", msg="msg"))
+            == "psutil.NoSuchProcess(pid=321, name='name', msg='msg')"
+        )
+
+    def test_no_such_process__str__(self):
+        assert (
+            str(psutil.NoSuchProcess(321))
+            == "process no longer exists (pid=321)"
+        )
+        assert (
+            str(psutil.NoSuchProcess(321, name="name", msg="msg"))
+            == "msg (pid=321, name='name')"
+        )
+
+    def test_zombie_process__repr__(self):
+        assert (
+            repr(psutil.ZombieProcess(321))
+            == 'psutil.ZombieProcess(pid=321, msg="PID still '
+            'exists but it\'s a zombie")'
+        )
+        assert (
+            repr(psutil.ZombieProcess(321, name="name", ppid=320, msg="foo"))
+            == "psutil.ZombieProcess(pid=321, ppid=320, name='name',"
+            " msg='foo')"
+        )
+
+    def test_zombie_process__str__(self):
+        assert (
+            str(psutil.ZombieProcess(321))
+            == "PID still exists but it's a zombie (pid=321)"
+        )
+        assert (
+            str(psutil.ZombieProcess(321, name="name", ppid=320, msg="foo"))
+            == "foo (pid=321, ppid=320, name='name')"
+        )
+
+    def test_access_denied__repr__(self):
+        assert repr(psutil.AccessDenied(321)) == "psutil.AccessDenied(pid=321)"
+        assert (
+            repr(psutil.AccessDenied(321, name="name", msg="msg"))
+            == "psutil.AccessDenied(pid=321, name='name', msg='msg')"
+        )
+
+    def test_access_denied__str__(self):
+        assert str(psutil.AccessDenied(321)) == "(pid=321)"
+        assert (
+            str(psutil.AccessDenied(321, name="name", msg="msg"))
+            == "msg (pid=321, name='name')"
+        )
+
+    def test_timeout_expired__repr__(self):
+        assert (
+            repr(psutil.TimeoutExpired(5))
+            == "psutil.TimeoutExpired(seconds=5, msg='timeout after 5"
+            " seconds')"
+        )
+        assert (
+            repr(psutil.TimeoutExpired(5, pid=321, name="name"))
+            == "psutil.TimeoutExpired(pid=321, name='name', seconds=5, "
+            "msg='timeout after 5 seconds')"
+        )
+
+    def test_timeout_expired__str__(self):
+        assert str(psutil.TimeoutExpired(5)) == "timeout after 5 seconds"
+        assert (
+            str(psutil.TimeoutExpired(5, pid=321, name="name"))
+            == "timeout after 5 seconds (pid=321, name='name')"
+        )
+
+    def test_process__eq__(self):
+        p1 = psutil.Process()
+        p2 = psutil.Process()
+        assert p1 == p2
+        p2._ident = (0, 0)
+        assert p1 != p2
+        assert p1 != 'foo'
+
+    def test_process__hash__(self):
+        s = {psutil.Process(), psutil.Process()}
+        assert len(s) == 1
+
+
+# ===================================================================
+# --- Misc, generic, corner cases
+# ===================================================================
+
+
+class TestMisc(PsutilTestCase):
+    def test__all__(self):
+        dir_psutil = dir(psutil)
+        for name in dir_psutil:
+            if name in {
+                'debug',
+                'tests',
+                'test',
+                'PermissionError',
+                'ProcessLookupError',
+            }:
+                continue
+            if not name.startswith('_'):
+                try:
+                    __import__(name)
+                except ImportError:
+                    if name not in psutil.__all__:
+                        fun = getattr(psutil, name)
+                        if fun is None:
+                            continue
+                        if (
+                            fun.__doc__ is not None
+                            and 'deprecated' not in fun.__doc__.lower()
+                        ):
+                            raise self.fail(f"{name!r} not in psutil.__all__")
+
+        # Import 'star' will break if __all__ is inconsistent, see:
+        # https://github.com/giampaolo/psutil/issues/656
+        # Can't do `from psutil import *` as it won't work
+        # so we simply iterate over __all__.
+        for name in psutil.__all__:
+            assert name in dir_psutil
+
+    def test_version(self):
+        assert (
+            '.'.join([str(x) for x in psutil.version_info])
+            == psutil.__version__
+        )
+
+    def test_process_as_dict_no_new_names(self):
+        # See https://github.com/giampaolo/psutil/issues/813
+        p = psutil.Process()
+        p.foo = '1'
+        assert 'foo' not in p.as_dict()
+
+    def test_serialization(self):
+        def check(ret):
+            json.loads(json.dumps(ret))
+
+            a = pickle.dumps(ret)
+            b = pickle.loads(a)
+            assert ret == b
+
+        # --- process APIs
+
+        proc = psutil.Process()
+        check(psutil.Process().as_dict())
+
+        ns = process_namespace(proc)
+        for fun, name in ns.iter(ns.getters, clear_cache=True):
+            with self.subTest(proc=proc, name=name):
+                try:
+                    ret = fun()
+                except psutil.Error:
+                    pass
+                else:
+                    check(ret)
+
+        # --- system APIs
+
+        ns = system_namespace()
+        for fun, name in ns.iter(ns.getters):
+            if name in {"win_service_iter", "win_service_get"}:
+                continue
+            with self.subTest(name=name):
+                try:
+                    ret = fun()
+                except psutil.AccessDenied:
+                    pass
+                else:
+                    check(ret)
+
+        # --- exception classes
+
+        b = pickle.loads(
+            pickle.dumps(
+                psutil.NoSuchProcess(pid=4567, name='name', msg='msg')
+            )
+        )
+        assert isinstance(b, psutil.NoSuchProcess)
+        assert b.pid == 4567
+        assert b.name == 'name'
+        assert b.msg == 'msg'
+
+        b = pickle.loads(
+            pickle.dumps(
+                psutil.ZombieProcess(pid=4567, name='name', ppid=42, msg='msg')
+            )
+        )
+        assert isinstance(b, psutil.ZombieProcess)
+        assert b.pid == 4567
+        assert b.ppid == 42
+        assert b.name == 'name'
+        assert b.msg == 'msg'
+
+        b = pickle.loads(
+            pickle.dumps(psutil.AccessDenied(pid=123, name='name', msg='msg'))
+        )
+        assert isinstance(b, psutil.AccessDenied)
+        assert b.pid == 123
+        assert b.name == 'name'
+        assert b.msg == 'msg'
+
+        b = pickle.loads(
+            pickle.dumps(
+                psutil.TimeoutExpired(seconds=33, pid=4567, name='name')
+            )
+        )
+        assert isinstance(b, psutil.TimeoutExpired)
+        assert b.seconds == 33
+        assert b.pid == 4567
+        assert b.name == 'name'
+
+    def test_ad_on_process_creation(self):
+        # We are supposed to be able to instantiate Process also in case
+        # of zombie processes or access denied.
+        with mock.patch.object(
+            psutil.Process, '_get_ident', side_effect=psutil.AccessDenied
+        ) as meth:
+            psutil.Process()
+            assert meth.called
+
+        with mock.patch.object(
+            psutil.Process, '_get_ident', side_effect=psutil.ZombieProcess(1)
+        ) as meth:
+            psutil.Process()
+            assert meth.called
+
+        with mock.patch.object(
+            psutil.Process, '_get_ident', side_effect=ValueError
+        ) as meth:
+            with pytest.raises(ValueError):
+                psutil.Process()
+            assert meth.called
+
+        with mock.patch.object(
+            psutil.Process, '_get_ident', side_effect=psutil.NoSuchProcess(1)
+        ) as meth:
+            with self.assertRaises(psutil.NoSuchProcess):
+                psutil.Process()
+            assert meth.called
+
+    def test_sanity_version_check(self):
+        # see: https://github.com/giampaolo/psutil/issues/564
+        with mock.patch(
+            "psutil._psplatform.cext.version", return_value="0.0.0"
+        ):
+            with pytest.raises(ImportError) as cm:
+                reload_module(psutil)
+            assert "version conflict" in str(cm.value).lower()
+
+
+# ===================================================================
+# --- psutil/_common.py utils
+# ===================================================================
+
+
+class TestMemoizeDecorator(PsutilTestCase):
+    def setUp(self):
+        self.calls = []
+
+    tearDown = setUp
+
+    def run_against(self, obj, expected_retval=None):
+        # no args
+        for _ in range(2):
+            ret = obj()
+            assert self.calls == [((), {})]
+            if expected_retval is not None:
+                assert ret == expected_retval
+        # with args
+        for _ in range(2):
+            ret = obj(1)
+            assert self.calls == [((), {}), ((1,), {})]
+            if expected_retval is not None:
+                assert ret == expected_retval
+        # with args + kwargs
+        for _ in range(2):
+            ret = obj(1, bar=2)
+            assert self.calls == [((), {}), ((1,), {}), ((1,), {'bar': 2})]
+            if expected_retval is not None:
+                assert ret == expected_retval
+        # clear cache
+        assert len(self.calls) == 3
+        obj.cache_clear()
+        ret = obj()
+        if expected_retval is not None:
+            assert ret == expected_retval
+        assert len(self.calls) == 4
+        # docstring
+        assert obj.__doc__ == "My docstring."
+
+    def test_function(self):
+        @memoize
+        def foo(*args, **kwargs):
+            """My docstring."""
+            baseclass.calls.append((args, kwargs))
+            return 22
+
+        baseclass = self
+        self.run_against(foo, expected_retval=22)
+
+    def test_class(self):
+        @memoize
+        class Foo:
+            """My docstring."""
+
+            def __init__(self, *args, **kwargs):
+                baseclass.calls.append((args, kwargs))
+
+            def bar(self):
+                return 22
+
+        baseclass = self
+        self.run_against(Foo, expected_retval=None)
+        assert Foo().bar() == 22
+
+    def test_class_singleton(self):
+        # @memoize can be used against classes to create singletons
+        @memoize
+        class Bar:
+            def __init__(self, *args, **kwargs):
+                pass
+
+        assert Bar() is Bar()
+        assert id(Bar()) == id(Bar())
+        assert id(Bar(1)) == id(Bar(1))
+        assert id(Bar(1, foo=3)) == id(Bar(1, foo=3))
+        assert id(Bar(1)) != id(Bar(2))
+
+    def test_staticmethod(self):
+        class Foo:
+            @staticmethod
+            @memoize
+            def bar(*args, **kwargs):
+                """My docstring."""
+                baseclass.calls.append((args, kwargs))
+                return 22
+
+        baseclass = self
+        self.run_against(Foo().bar, expected_retval=22)
+
+    def test_classmethod(self):
+        class Foo:
+            @classmethod
+            @memoize
+            def bar(cls, *args, **kwargs):
+                """My docstring."""
+                baseclass.calls.append((args, kwargs))
+                return 22
+
+        baseclass = self
+        self.run_against(Foo().bar, expected_retval=22)
+
+    def test_original(self):
+        # This was the original test before I made it dynamic to test it
+        # against different types. Keeping it anyway.
+        @memoize
+        def foo(*args, **kwargs):
+            """Foo docstring."""
+            calls.append(None)
+            return (args, kwargs)
+
+        calls = []
+        # no args
+        for _ in range(2):
+            ret = foo()
+            expected = ((), {})
+            assert ret == expected
+            assert len(calls) == 1
+        # with args
+        for _ in range(2):
+            ret = foo(1)
+            expected = ((1,), {})
+            assert ret == expected
+            assert len(calls) == 2
+        # with args + kwargs
+        for _ in range(2):
+            ret = foo(1, bar=2)
+            expected = ((1,), {'bar': 2})
+            assert ret == expected
+            assert len(calls) == 3
+        # clear cache
+        foo.cache_clear()
+        ret = foo()
+        expected = ((), {})
+        assert ret == expected
+        assert len(calls) == 4
+        # docstring
+        assert foo.__doc__ == "Foo docstring."
+
+
+class TestCommonModule(PsutilTestCase):
+    def test_memoize_when_activated(self):
+        class Foo:
+            @memoize_when_activated
+            def foo(self):
+                calls.append(None)
+
+        f = Foo()
+        calls = []
+        f.foo()
+        f.foo()
+        assert len(calls) == 2
+
+        # activate
+        calls = []
+        f.foo.cache_activate(f)
+        f.foo()
+        f.foo()
+        assert len(calls) == 1
+
+        # deactivate
+        calls = []
+        f.foo.cache_deactivate(f)
+        f.foo()
+        f.foo()
+        assert len(calls) == 2
+
+    def test_parse_environ_block(self):
+        def k(s):
+            return s.upper() if WINDOWS else s
+
+        assert parse_environ_block("a=1\0") == {k("a"): "1"}
+        assert parse_environ_block("a=1\0b=2\0\0") == {
+            k("a"): "1",
+            k("b"): "2",
+        }
+        assert parse_environ_block("a=1\0b=\0\0") == {k("a"): "1", k("b"): ""}
+        # ignore everything after \0\0
+        assert parse_environ_block("a=1\0b=2\0\0c=3\0") == {
+            k("a"): "1",
+            k("b"): "2",
+        }
+        # ignore everything that is not an assignment
+        assert parse_environ_block("xxx\0a=1\0") == {k("a"): "1"}
+        assert parse_environ_block("a=1\0=b=2\0") == {k("a"): "1"}
+        # do not fail if the block is incomplete
+        assert parse_environ_block("a=1\0b=2") == {k("a"): "1"}
+
+    def test_supports_ipv6(self):
+        self.addCleanup(supports_ipv6.cache_clear)
+        if supports_ipv6():
+            with mock.patch('psutil._common.socket') as s:
+                s.has_ipv6 = False
+                supports_ipv6.cache_clear()
+                assert not supports_ipv6()
+
+            supports_ipv6.cache_clear()
+            with mock.patch(
+                'psutil._common.socket.socket', side_effect=OSError
+            ) as s:
+                assert not supports_ipv6()
+                assert s.called
+
+            supports_ipv6.cache_clear()
+            with mock.patch(
+                'psutil._common.socket.socket', side_effect=socket.gaierror
+            ) as s:
+                assert not supports_ipv6()
+                supports_ipv6.cache_clear()
+                assert s.called
+
+            supports_ipv6.cache_clear()
+            with mock.patch(
+                'psutil._common.socket.socket.bind',
+                side_effect=socket.gaierror,
+            ) as s:
+                assert not supports_ipv6()
+                supports_ipv6.cache_clear()
+                assert s.called
+        else:
+            with pytest.raises(OSError):
+                sock = socket.socket(socket.AF_INET6, socket.SOCK_STREAM)
+                try:
+                    sock.bind(("::1", 0))
+                finally:
+                    sock.close()
+
+    def test_isfile_strict(self):
+        this_file = os.path.abspath(__file__)
+        assert isfile_strict(this_file)
+        assert not isfile_strict(os.path.dirname(this_file))
+        with mock.patch('psutil._common.os.stat', side_effect=PermissionError):
+            with pytest.raises(OSError):
+                isfile_strict(this_file)
+        with mock.patch(
+            'psutil._common.os.stat', side_effect=FileNotFoundError
+        ):
+            assert not isfile_strict(this_file)
+        with mock.patch('psutil._common.stat.S_ISREG', return_value=False):
+            assert not isfile_strict(this_file)
+
+    def test_debug(self):
+        with mock.patch.object(psutil._common, "PSUTIL_DEBUG", True):
+            with contextlib.redirect_stderr(io.StringIO()) as f:
+                debug("hello")
+                sys.stderr.flush()
+        msg = f.getvalue()
+        assert msg.startswith("psutil-debug"), msg
+        assert "hello" in msg
+        assert __file__.replace('.pyc', '.py') in msg
+
+        # supposed to use repr(exc)
+        with mock.patch.object(psutil._common, "PSUTIL_DEBUG", True):
+            with contextlib.redirect_stderr(io.StringIO()) as f:
+                debug(ValueError("this is an error"))
+        msg = f.getvalue()
+        assert "ignoring ValueError" in msg
+        assert "'this is an error'" in msg
+
+        # supposed to use str(exc), because of extra info about file name
+        with mock.patch.object(psutil._common, "PSUTIL_DEBUG", True):
+            with contextlib.redirect_stderr(io.StringIO()) as f:
+                exc = OSError(2, "no such file")
+                exc.filename = "/foo"
+                debug(exc)
+        msg = f.getvalue()
+        assert "no such file" in msg
+        assert "/foo" in msg
+
+    def test_cat_bcat(self):
+        testfn = self.get_testfn()
+        with open(testfn, "w") as f:
+            f.write("foo")
+        assert cat(testfn) == "foo"
+        assert bcat(testfn) == b"foo"
+        with pytest.raises(FileNotFoundError):
+            cat(testfn + '-invalid')
+        with pytest.raises(FileNotFoundError):
+            bcat(testfn + '-invalid')
+        assert cat(testfn + '-invalid', fallback="bar") == "bar"
+        assert bcat(testfn + '-invalid', fallback="bar") == "bar"
+
+
+# ===================================================================
+# --- Tests for wrap_numbers() function.
+# ===================================================================
+
+
+nt = collections.namedtuple('foo', 'a b c')
+
+
+class TestWrapNumbers(PsutilTestCase):
+    def setUp(self):
+        wrap_numbers.cache_clear()
+
+    tearDown = setUp
+
+    def test_first_call(self):
+        input = {'disk1': nt(5, 5, 5)}
+        assert wrap_numbers(input, 'disk_io') == input
+
+    def test_input_hasnt_changed(self):
+        input = {'disk1': nt(5, 5, 5)}
+        assert wrap_numbers(input, 'disk_io') == input
+        assert wrap_numbers(input, 'disk_io') == input
+
+    def test_increase_but_no_wrap(self):
+        input = {'disk1': nt(5, 5, 5)}
+        assert wrap_numbers(input, 'disk_io') == input
+        input = {'disk1': nt(10, 15, 20)}
+        assert wrap_numbers(input, 'disk_io') == input
+        input = {'disk1': nt(20, 25, 30)}
+        assert wrap_numbers(input, 'disk_io') == input
+        input = {'disk1': nt(20, 25, 30)}
+        assert wrap_numbers(input, 'disk_io') == input
+
+    def test_wrap(self):
+        # let's say 100 is the threshold
+        input = {'disk1': nt(100, 100, 100)}
+        assert wrap_numbers(input, 'disk_io') == input
+        # first wrap restarts from 10
+        input = {'disk1': nt(100, 100, 10)}
+        assert wrap_numbers(input, 'disk_io') == {'disk1': nt(100, 100, 110)}
+        # then it remains the same
+        input = {'disk1': nt(100, 100, 10)}
+        assert wrap_numbers(input, 'disk_io') == {'disk1': nt(100, 100, 110)}
+        # then it goes up
+        input = {'disk1': nt(100, 100, 90)}
+        assert wrap_numbers(input, 'disk_io') == {'disk1': nt(100, 100, 190)}
+        # then it wraps again
+        input = {'disk1': nt(100, 100, 20)}
+        assert wrap_numbers(input, 'disk_io') == {'disk1': nt(100, 100, 210)}
+        # and remains the same
+        input = {'disk1': nt(100, 100, 20)}
+        assert wrap_numbers(input, 'disk_io') == {'disk1': nt(100, 100, 210)}
+        # now wrap another num
+        input = {'disk1': nt(50, 100, 20)}
+        assert wrap_numbers(input, 'disk_io') == {'disk1': nt(150, 100, 210)}
+        # and again
+        input = {'disk1': nt(40, 100, 20)}
+        assert wrap_numbers(input, 'disk_io') == {'disk1': nt(190, 100, 210)}
+        # keep it the same
+        input = {'disk1': nt(40, 100, 20)}
+        assert wrap_numbers(input, 'disk_io') == {'disk1': nt(190, 100, 210)}
+
+    def test_changing_keys(self):
+        # Emulate a case where the second call to disk_io()
+        # (or whatever) provides a new disk, then the new disk
+        # disappears on the third call.
+        input = {'disk1': nt(5, 5, 5)}
+        assert wrap_numbers(input, 'disk_io') == input
+        input = {'disk1': nt(5, 5, 5), 'disk2': nt(7, 7, 7)}
+        assert wrap_numbers(input, 'disk_io') == input
+        input = {'disk1': nt(8, 8, 8)}
+        assert wrap_numbers(input, 'disk_io') == input
+
+    def test_changing_keys_w_wrap(self):
+        input = {'disk1': nt(50, 50, 50), 'disk2': nt(100, 100, 100)}
+        assert wrap_numbers(input, 'disk_io') == input
+        # disk 2 wraps
+        input = {'disk1': nt(50, 50, 50), 'disk2': nt(100, 100, 10)}
+        assert wrap_numbers(input, 'disk_io') == {
+            'disk1': nt(50, 50, 50),
+            'disk2': nt(100, 100, 110),
+        }
+        # disk 2 disappears
+        input = {'disk1': nt(50, 50, 50)}
+        assert wrap_numbers(input, 'disk_io') == input
+
+        # then it appears again; the old wrap is supposed to be
+        # gone.
+        input = {'disk1': nt(50, 50, 50), 'disk2': nt(100, 100, 100)}
+        assert wrap_numbers(input, 'disk_io') == input
+        # remains the same
+        input = {'disk1': nt(50, 50, 50), 'disk2': nt(100, 100, 100)}
+        assert wrap_numbers(input, 'disk_io') == input
+        # and then wraps again
+        input = {'disk1': nt(50, 50, 50), 'disk2': nt(100, 100, 10)}
+        assert wrap_numbers(input, 'disk_io') == {
+            'disk1': nt(50, 50, 50),
+            'disk2': nt(100, 100, 110),
+        }
+
+    def test_real_data(self):
+        d = {
+            'nvme0n1': (300, 508, 640, 1571, 5970, 1987, 2049, 451751, 47048),
+            'nvme0n1p1': (1171, 2, 5600256, 1024, 516, 0, 0, 0, 8),
+            'nvme0n1p2': (54, 54, 2396160, 5165056, 4, 24, 30, 1207, 28),
+            'nvme0n1p3': (2389, 4539, 5154, 150, 4828, 1844, 2019, 398, 348),
+        }
+        assert wrap_numbers(d, 'disk_io') == d
+        assert wrap_numbers(d, 'disk_io') == d
+        # decrease this   ↓
+        d = {
+            'nvme0n1': (100, 508, 640, 1571, 5970, 1987, 2049, 451751, 47048),
+            'nvme0n1p1': (1171, 2, 5600256, 1024, 516, 0, 0, 0, 8),
+            'nvme0n1p2': (54, 54, 2396160, 5165056, 4, 24, 30, 1207, 28),
+            'nvme0n1p3': (2389, 4539, 5154, 150, 4828, 1844, 2019, 398, 348),
+        }
+        out = wrap_numbers(d, 'disk_io')
+        assert out['nvme0n1'][0] == 400
+
+    # --- cache tests
+
+    def test_cache_first_call(self):
+        input = {'disk1': nt(5, 5, 5)}
+        wrap_numbers(input, 'disk_io')
+        cache = wrap_numbers.cache_info()
+        assert cache[0] == {'disk_io': input}
+        assert cache[1] == {'disk_io': {}}
+        assert cache[2] == {'disk_io': {}}
+
+    def test_cache_call_twice(self):
+        input = {'disk1': nt(5, 5, 5)}
+        wrap_numbers(input, 'disk_io')
+        input = {'disk1': nt(10, 10, 10)}
+        wrap_numbers(input, 'disk_io')
+        cache = wrap_numbers.cache_info()
+        assert cache[0] == {'disk_io': input}
+        assert cache[1] == {
+            'disk_io': {('disk1', 0): 0, ('disk1', 1): 0, ('disk1', 2): 0}
+        }
+        assert cache[2] == {'disk_io': {}}
+
+    def test_cache_wrap(self):
+        # let's say 100 is the threshold
+        input = {'disk1': nt(100, 100, 100)}
+        wrap_numbers(input, 'disk_io')
+
+        # first wrap restarts from 10
+        input = {'disk1': nt(100, 100, 10)}
+        wrap_numbers(input, 'disk_io')
+        cache = wrap_numbers.cache_info()
+        assert cache[0] == {'disk_io': input}
+        assert cache[1] == {
+            'disk_io': {('disk1', 0): 0, ('disk1', 1): 0, ('disk1', 2): 100}
+        }
+        assert cache[2] == {'disk_io': {'disk1': {('disk1', 2)}}}
+
+        def check_cache_info():
+            cache = wrap_numbers.cache_info()
+            assert cache[1] == {
+                'disk_io': {
+                    ('disk1', 0): 0,
+                    ('disk1', 1): 0,
+                    ('disk1', 2): 100,
+                }
+            }
+            assert cache[2] == {'disk_io': {'disk1': {('disk1', 2)}}}
+
+        # then it remains the same
+        input = {'disk1': nt(100, 100, 10)}
+        wrap_numbers(input, 'disk_io')
+        cache = wrap_numbers.cache_info()
+        assert cache[0] == {'disk_io': input}
+        check_cache_info()
+
+        # then it goes up
+        input = {'disk1': nt(100, 100, 90)}
+        wrap_numbers(input, 'disk_io')
+        cache = wrap_numbers.cache_info()
+        assert cache[0] == {'disk_io': input}
+        check_cache_info()
+
+        # then it wraps again
+        input = {'disk1': nt(100, 100, 20)}
+        wrap_numbers(input, 'disk_io')
+        cache = wrap_numbers.cache_info()
+        assert cache[0] == {'disk_io': input}
+        assert cache[1] == {
+            'disk_io': {('disk1', 0): 0, ('disk1', 1): 0, ('disk1', 2): 190}
+        }
+        assert cache[2] == {'disk_io': {'disk1': {('disk1', 2)}}}
+
+    def test_cache_changing_keys(self):
+        input = {'disk1': nt(5, 5, 5)}
+        wrap_numbers(input, 'disk_io')
+        input = {'disk1': nt(5, 5, 5), 'disk2': nt(7, 7, 7)}
+        wrap_numbers(input, 'disk_io')
+        cache = wrap_numbers.cache_info()
+        assert cache[0] == {'disk_io': input}
+        assert cache[1] == {
+            'disk_io': {('disk1', 0): 0, ('disk1', 1): 0, ('disk1', 2): 0}
+        }
+        assert cache[2] == {'disk_io': {}}
+
+    def test_cache_clear(self):
+        input = {'disk1': nt(5, 5, 5)}
+        wrap_numbers(input, 'disk_io')
+        wrap_numbers(input, 'disk_io')
+        wrap_numbers.cache_clear('disk_io')
+        assert wrap_numbers.cache_info() == ({}, {}, {})
+        wrap_numbers.cache_clear('disk_io')
+        wrap_numbers.cache_clear('?!?')
+
+    @pytest.mark.skipif(not HAS_NET_IO_COUNTERS, reason="not supported")
+    def test_cache_clear_public_apis(self):
+        if not psutil.disk_io_counters() or not psutil.net_io_counters():
+            raise pytest.skip("no disks or NICs available")
+        psutil.disk_io_counters()
+        psutil.net_io_counters()
+        caches = wrap_numbers.cache_info()
+        for cache in caches:
+            assert 'psutil.disk_io_counters' in cache
+            assert 'psutil.net_io_counters' in cache
+
+        psutil.disk_io_counters.cache_clear()
+        caches = wrap_numbers.cache_info()
+        for cache in caches:
+            assert 'psutil.net_io_counters' in cache
+            assert 'psutil.disk_io_counters' not in cache
+
+        psutil.net_io_counters.cache_clear()
+        caches = wrap_numbers.cache_info()
+        assert caches == ({}, {}, {})
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_osx.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_osx.py
new file mode 100644
index 0000000000000000000000000000000000000000..050418c5f9f107243cef4403d05cc27f9406b339
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_osx.py
@@ -0,0 +1,197 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""macOS specific tests."""
+
+import platform
+import re
+import time
+
+import psutil
+from psutil import MACOS
+from psutil import POSIX
+from psutil.tests import CI_TESTING
+from psutil.tests import HAS_BATTERY
+from psutil.tests import TOLERANCE_DISK_USAGE
+from psutil.tests import TOLERANCE_SYS_MEM
+from psutil.tests import PsutilTestCase
+from psutil.tests import pytest
+from psutil.tests import retry_on_failure
+from psutil.tests import sh
+from psutil.tests import spawn_testproc
+from psutil.tests import terminate
+
+
+if POSIX:
+    from psutil._psutil_posix import getpagesize
+
+
+def sysctl(cmdline):
+    """Expects a sysctl command with an argument and parse the result
+    returning only the value of interest.
+    """
+    out = sh(cmdline)
+    result = out.split()[1]
+    try:
+        return int(result)
+    except ValueError:
+        return result
+
+
+def vm_stat(field):
+    """Wrapper around 'vm_stat' cmdline utility."""
+    out = sh('vm_stat')
+    for line in out.split('\n'):
+        if field in line:
+            break
+    else:
+        raise ValueError("line not found")
+    return int(re.search(r'\d+', line).group(0)) * getpagesize()
+
+
+@pytest.mark.skipif(not MACOS, reason="MACOS only")
+class TestProcess(PsutilTestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.pid = spawn_testproc().pid
+
+    @classmethod
+    def tearDownClass(cls):
+        terminate(cls.pid)
+
+    def test_process_create_time(self):
+        output = sh(f"ps -o lstart -p {self.pid}")
+        start_ps = output.replace('STARTED', '').strip()
+        hhmmss = start_ps.split(' ')[-2]
+        year = start_ps.split(' ')[-1]
+        start_psutil = psutil.Process(self.pid).create_time()
+        assert hhmmss == time.strftime(
+            "%H:%M:%S", time.localtime(start_psutil)
+        )
+        assert year == time.strftime("%Y", time.localtime(start_psutil))
+
+
+@pytest.mark.skipif(not MACOS, reason="MACOS only")
+class TestSystemAPIs(PsutilTestCase):
+
+    # --- disk
+
+    @retry_on_failure()
+    def test_disks(self):
+        # test psutil.disk_usage() and psutil.disk_partitions()
+        # against "df -a"
+        def df(path):
+            out = sh(f'df -k "{path}"').strip()
+            lines = out.split('\n')
+            lines.pop(0)
+            line = lines.pop(0)
+            dev, total, used, free = line.split()[:4]
+            if dev == 'none':
+                dev = ''
+            total = int(total) * 1024
+            used = int(used) * 1024
+            free = int(free) * 1024
+            return dev, total, used, free
+
+        for part in psutil.disk_partitions(all=False):
+            usage = psutil.disk_usage(part.mountpoint)
+            dev, total, used, free = df(part.mountpoint)
+            assert part.device == dev
+            assert usage.total == total
+            assert abs(usage.free - free) < TOLERANCE_DISK_USAGE
+            assert abs(usage.used - used) < TOLERANCE_DISK_USAGE
+
+    # --- cpu
+
+    def test_cpu_count_logical(self):
+        num = sysctl("sysctl hw.logicalcpu")
+        assert num == psutil.cpu_count(logical=True)
+
+    def test_cpu_count_cores(self):
+        num = sysctl("sysctl hw.physicalcpu")
+        assert num == psutil.cpu_count(logical=False)
+
+    # TODO: remove this once 1892 is fixed
+    @pytest.mark.skipif(
+        MACOS and platform.machine() == 'arm64', reason="skipped due to #1892"
+    )
+    def test_cpu_freq(self):
+        freq = psutil.cpu_freq()
+        assert freq.current * 1000 * 1000 == sysctl("sysctl hw.cpufrequency")
+        assert freq.min * 1000 * 1000 == sysctl("sysctl hw.cpufrequency_min")
+        assert freq.max * 1000 * 1000 == sysctl("sysctl hw.cpufrequency_max")
+
+    # --- virtual mem
+
+    def test_vmem_total(self):
+        sysctl_hwphymem = sysctl('sysctl hw.memsize')
+        assert sysctl_hwphymem == psutil.virtual_memory().total
+
+    @pytest.mark.skipif(
+        CI_TESTING and MACOS and platform.machine() == 'arm64',
+        reason="skipped on MACOS + ARM64 + CI_TESTING",
+    )
+    @retry_on_failure()
+    def test_vmem_free(self):
+        vmstat_val = vm_stat("free")
+        psutil_val = psutil.virtual_memory().free
+        assert abs(psutil_val - vmstat_val) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_vmem_active(self):
+        vmstat_val = vm_stat("active")
+        psutil_val = psutil.virtual_memory().active
+        assert abs(psutil_val - vmstat_val) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_vmem_inactive(self):
+        vmstat_val = vm_stat("inactive")
+        psutil_val = psutil.virtual_memory().inactive
+        assert abs(psutil_val - vmstat_val) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_vmem_wired(self):
+        vmstat_val = vm_stat("wired")
+        psutil_val = psutil.virtual_memory().wired
+        assert abs(psutil_val - vmstat_val) < TOLERANCE_SYS_MEM
+
+    # --- swap mem
+
+    @retry_on_failure()
+    def test_swapmem_sin(self):
+        vmstat_val = vm_stat("Pageins")
+        psutil_val = psutil.swap_memory().sin
+        assert abs(psutil_val - vmstat_val) < TOLERANCE_SYS_MEM
+
+    @retry_on_failure()
+    def test_swapmem_sout(self):
+        vmstat_val = vm_stat("Pageout")
+        psutil_val = psutil.swap_memory().sout
+        assert abs(psutil_val - vmstat_val) < TOLERANCE_SYS_MEM
+
+    # --- network
+
+    def test_net_if_stats(self):
+        for name, stats in psutil.net_if_stats().items():
+            try:
+                out = sh(f"ifconfig {name}")
+            except RuntimeError:
+                pass
+            else:
+                assert stats.isup == ('RUNNING' in out), out
+                assert stats.mtu == int(re.findall(r'mtu (\d+)', out)[0])
+
+    # --- sensors_battery
+
+    @pytest.mark.skipif(not HAS_BATTERY, reason="no battery")
+    def test_sensors_battery(self):
+        out = sh("pmset -g batt")
+        percent = re.search(r"(\d+)%", out).group(1)
+        drawing_from = re.search(r"Now drawing from '([^']+)'", out).group(1)
+        power_plugged = drawing_from == "AC Power"
+        psutil_result = psutil.sensors_battery()
+        assert psutil_result.power_plugged == power_plugged
+        assert psutil_result.percent == int(percent)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_posix.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_posix.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7844929e767638086da0ebd85c27ab393f5c7ba
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_posix.py
@@ -0,0 +1,488 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""POSIX specific tests."""
+
+import datetime
+import errno
+import os
+import re
+import shutil
+import subprocess
+import time
+from unittest import mock
+
+import psutil
+from psutil import AIX
+from psutil import BSD
+from psutil import LINUX
+from psutil import MACOS
+from psutil import OPENBSD
+from psutil import POSIX
+from psutil import SUNOS
+from psutil.tests import AARCH64
+from psutil.tests import HAS_NET_IO_COUNTERS
+from psutil.tests import PYTHON_EXE
+from psutil.tests import PsutilTestCase
+from psutil.tests import pytest
+from psutil.tests import retry_on_failure
+from psutil.tests import sh
+from psutil.tests import skip_on_access_denied
+from psutil.tests import spawn_testproc
+from psutil.tests import terminate
+
+
+if POSIX:
+    import mmap
+    import resource
+
+    from psutil._psutil_posix import getpagesize
+
+
+def ps(fmt, pid=None):
+    """Wrapper for calling the ps command with a little bit of cross-platform
+    support for a narrow range of features.
+    """
+
+    cmd = ['ps']
+
+    if LINUX:
+        cmd.append('--no-headers')
+
+    if pid is not None:
+        cmd.extend(['-p', str(pid)])
+    elif SUNOS or AIX:
+        cmd.append('-A')
+    else:
+        cmd.append('ax')
+
+    if SUNOS:
+        fmt = fmt.replace("start", "stime")
+
+    cmd.extend(['-o', fmt])
+
+    output = sh(cmd)
+
+    output = output.splitlines() if LINUX else output.splitlines()[1:]
+
+    all_output = []
+    for line in output:
+        line = line.strip()
+
+        try:
+            line = int(line)
+        except ValueError:
+            pass
+
+        all_output.append(line)
+
+    if pid is None:
+        return all_output
+    else:
+        return all_output[0]
+
+
+# ps "-o" field names differ wildly between platforms.
+# "comm" means "only executable name" but is not available on BSD platforms.
+# "args" means "command with all its arguments", and is also not available
+# on BSD platforms.
+# "command" is like "args" on most platforms, but like "comm" on AIX,
+# and not available on SUNOS.
+# so for the executable name we can use "comm" on Solaris and split "command"
+# on other platforms.
+# to get the cmdline (with args) we have to use "args" on AIX and
+# Solaris, and can use "command" on all others.
+
+
+def ps_name(pid):
+    field = "command"
+    if SUNOS:
+        field = "comm"
+    command = ps(field, pid).split()
+    return command[0]
+
+
+def ps_args(pid):
+    field = "command"
+    if AIX or SUNOS:
+        field = "args"
+    out = ps(field, pid)
+    # observed on BSD + Github CI: '/usr/local/bin/python3 -E -O (python3.9)'
+    out = re.sub(r"\(python.*?\)$", "", out)
+    return out.strip()
+
+
+def ps_rss(pid):
+    field = "rss"
+    if AIX:
+        field = "rssize"
+    return ps(field, pid)
+
+
+def ps_vsz(pid):
+    field = "vsz"
+    if AIX:
+        field = "vsize"
+    return ps(field, pid)
+
+
+def df(device):
+    try:
+        out = sh(f"df -k {device}").strip()
+    except RuntimeError as err:
+        if "device busy" in str(err).lower():
+            raise pytest.skip("df returned EBUSY")
+        raise
+    line = out.split('\n')[1]
+    fields = line.split()
+    sys_total = int(fields[1]) * 1024
+    sys_used = int(fields[2]) * 1024
+    sys_free = int(fields[3]) * 1024
+    sys_percent = float(fields[4].replace('%', ''))
+    return (sys_total, sys_used, sys_free, sys_percent)
+
+
+@pytest.mark.skipif(not POSIX, reason="POSIX only")
+class TestProcess(PsutilTestCase):
+    """Compare psutil results against 'ps' command line utility (mainly)."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.pid = spawn_testproc(
+            [PYTHON_EXE, "-E", "-O"], stdin=subprocess.PIPE
+        ).pid
+
+    @classmethod
+    def tearDownClass(cls):
+        terminate(cls.pid)
+
+    def test_ppid(self):
+        ppid_ps = ps('ppid', self.pid)
+        ppid_psutil = psutil.Process(self.pid).ppid()
+        assert ppid_ps == ppid_psutil
+
+    def test_uid(self):
+        uid_ps = ps('uid', self.pid)
+        uid_psutil = psutil.Process(self.pid).uids().real
+        assert uid_ps == uid_psutil
+
+    def test_gid(self):
+        gid_ps = ps('rgid', self.pid)
+        gid_psutil = psutil.Process(self.pid).gids().real
+        assert gid_ps == gid_psutil
+
+    def test_username(self):
+        username_ps = ps('user', self.pid)
+        username_psutil = psutil.Process(self.pid).username()
+        assert username_ps == username_psutil
+
+    def test_username_no_resolution(self):
+        # Emulate a case where the system can't resolve the uid to
+        # a username in which case psutil is supposed to return
+        # the stringified uid.
+        p = psutil.Process()
+        with mock.patch("psutil.pwd.getpwuid", side_effect=KeyError) as fun:
+            assert p.username() == str(p.uids().real)
+            assert fun.called
+
+    @skip_on_access_denied()
+    @retry_on_failure()
+    def test_rss_memory(self):
+        # give python interpreter some time to properly initialize
+        # so that the results are the same
+        time.sleep(0.1)
+        rss_ps = ps_rss(self.pid)
+        rss_psutil = psutil.Process(self.pid).memory_info()[0] / 1024
+        assert rss_ps == rss_psutil
+
+    @skip_on_access_denied()
+    @retry_on_failure()
+    def test_vsz_memory(self):
+        # give python interpreter some time to properly initialize
+        # so that the results are the same
+        time.sleep(0.1)
+        vsz_ps = ps_vsz(self.pid)
+        vsz_psutil = psutil.Process(self.pid).memory_info()[1] / 1024
+        assert vsz_ps == vsz_psutil
+
+    def test_name(self):
+        name_ps = ps_name(self.pid)
+        # remove path if there is any, from the command
+        name_ps = os.path.basename(name_ps).lower()
+        name_psutil = psutil.Process(self.pid).name().lower()
+        # ...because of how we calculate PYTHON_EXE; on MACOS this may
+        # be "pythonX.Y".
+        name_ps = re.sub(r"\d.\d", "", name_ps)
+        name_psutil = re.sub(r"\d.\d", "", name_psutil)
+        # ...may also be "python.X"
+        name_ps = re.sub(r"\d", "", name_ps)
+        name_psutil = re.sub(r"\d", "", name_psutil)
+        assert name_ps == name_psutil
+
+    def test_name_long(self):
+        # On UNIX the kernel truncates the name to the first 15
+        # characters. In such a case psutil tries to determine the
+        # full name from the cmdline.
+        name = "long-program-name"
+        cmdline = ["long-program-name-extended", "foo", "bar"]
+        with mock.patch("psutil._psplatform.Process.name", return_value=name):
+            with mock.patch(
+                "psutil._psplatform.Process.cmdline", return_value=cmdline
+            ):
+                p = psutil.Process()
+                assert p.name() == "long-program-name-extended"
+
+    def test_name_long_cmdline_ad_exc(self):
+        # Same as above but emulates a case where cmdline() raises
+        # AccessDenied in which case psutil is supposed to return
+        # the truncated name instead of crashing.
+        name = "long-program-name"
+        with mock.patch("psutil._psplatform.Process.name", return_value=name):
+            with mock.patch(
+                "psutil._psplatform.Process.cmdline",
+                side_effect=psutil.AccessDenied(0, ""),
+            ):
+                p = psutil.Process()
+                assert p.name() == "long-program-name"
+
+    def test_name_long_cmdline_nsp_exc(self):
+        # Same as above but emulates a case where cmdline() raises NSP
+        # which is supposed to propagate.
+        name = "long-program-name"
+        with mock.patch("psutil._psplatform.Process.name", return_value=name):
+            with mock.patch(
+                "psutil._psplatform.Process.cmdline",
+                side_effect=psutil.NoSuchProcess(0, ""),
+            ):
+                p = psutil.Process()
+                with pytest.raises(psutil.NoSuchProcess):
+                    p.name()
+
+    @pytest.mark.skipif(MACOS or BSD, reason="ps -o start not available")
+    def test_create_time(self):
+        time_ps = ps('start', self.pid)
+        time_psutil = psutil.Process(self.pid).create_time()
+        time_psutil_tstamp = datetime.datetime.fromtimestamp(
+            time_psutil
+        ).strftime("%H:%M:%S")
+        # sometimes ps shows the time rounded up instead of down, so we check
+        # for both possible values
+        round_time_psutil = round(time_psutil)
+        round_time_psutil_tstamp = datetime.datetime.fromtimestamp(
+            round_time_psutil
+        ).strftime("%H:%M:%S")
+        assert time_ps in {time_psutil_tstamp, round_time_psutil_tstamp}
+
+    def test_exe(self):
+        ps_pathname = ps_name(self.pid)
+        psutil_pathname = psutil.Process(self.pid).exe()
+        try:
+            assert ps_pathname == psutil_pathname
+        except AssertionError:
+            # certain platforms such as BSD are more accurate returning:
+            # "/usr/local/bin/python3.7"
+            # ...instead of:
+            # "/usr/local/bin/python"
+            # We do not want to consider this difference in accuracy
+            # an error.
+            adjusted_ps_pathname = ps_pathname[: len(ps_pathname)]
+            assert ps_pathname == adjusted_ps_pathname
+
+    # On macOS the official python installer exposes a python wrapper that
+    # executes a python executable hidden inside an application bundle inside
+    # the Python framework.
+    # There's a race condition between the ps call & the psutil call below
+    # depending on the completion of the execve call so let's retry on failure
+    @retry_on_failure()
+    def test_cmdline(self):
+        ps_cmdline = ps_args(self.pid)
+        psutil_cmdline = " ".join(psutil.Process(self.pid).cmdline())
+        if AARCH64 and len(ps_cmdline) < len(psutil_cmdline):
+            assert psutil_cmdline.startswith(ps_cmdline)
+        else:
+            assert ps_cmdline == psutil_cmdline
+
+    # On SUNOS "ps" reads niceness /proc/pid/psinfo which returns an
+    # incorrect value (20); the real deal is getpriority(2) which
+    # returns 0; psutil relies on it, see:
+    # https://github.com/giampaolo/psutil/issues/1082
+    # AIX has the same issue
+    @pytest.mark.skipif(SUNOS, reason="not reliable on SUNOS")
+    @pytest.mark.skipif(AIX, reason="not reliable on AIX")
+    def test_nice(self):
+        ps_nice = ps('nice', self.pid)
+        psutil_nice = psutil.Process().nice()
+        assert ps_nice == psutil_nice
+
+
+@pytest.mark.skipif(not POSIX, reason="POSIX only")
+class TestSystemAPIs(PsutilTestCase):
+    """Test some system APIs."""
+
+    @retry_on_failure()
+    def test_pids(self):
+        # Note: this test might fail if the OS is starting/killing
+        # other processes in the meantime
+        pids_ps = sorted(ps("pid"))
+        pids_psutil = psutil.pids()
+
+        # on MACOS and OPENBSD ps doesn't show pid 0
+        if MACOS or (OPENBSD and 0 not in pids_ps):
+            pids_ps.insert(0, 0)
+
+        # There will often be one more process in pids_ps for ps itself
+        if len(pids_ps) - len(pids_psutil) > 1:
+            difference = [x for x in pids_psutil if x not in pids_ps] + [
+                x for x in pids_ps if x not in pids_psutil
+            ]
+            raise self.fail("difference: " + str(difference))
+
+    # for some reason ifconfig -a does not report all interfaces
+    # returned by psutil
+    @pytest.mark.skipif(SUNOS, reason="unreliable on SUNOS")
+    @pytest.mark.skipif(not shutil.which("ifconfig"), reason="no ifconfig cmd")
+    @pytest.mark.skipif(not HAS_NET_IO_COUNTERS, reason="not supported")
+    def test_nic_names(self):
+        output = sh("ifconfig -a")
+        for nic in psutil.net_io_counters(pernic=True):
+            for line in output.split():
+                if line.startswith(nic):
+                    break
+            else:
+                raise self.fail(
+                    f"couldn't find {nic} nic in 'ifconfig -a'"
+                    f" output\n{output}"
+                )
+
+    # @pytest.mark.skipif(CI_TESTING and not psutil.users(),
+    #                     reason="unreliable on CI")
+    @retry_on_failure()
+    def test_users(self):
+        out = sh("who -u")
+        if not out.strip():
+            raise pytest.skip("no users on this system")
+        lines = out.split('\n')
+        users = [x.split()[0] for x in lines]
+        terminals = [x.split()[1] for x in lines]
+        assert len(users) == len(psutil.users())
+        with self.subTest(psutil=psutil.users(), who=out):
+            for idx, u in enumerate(psutil.users()):
+                assert u.name == users[idx]
+                assert u.terminal == terminals[idx]
+                if u.pid is not None:  # None on OpenBSD
+                    psutil.Process(u.pid)
+
+    @retry_on_failure()
+    def test_users_started(self):
+        out = sh("who -u")
+        if not out.strip():
+            raise pytest.skip("no users on this system")
+        tstamp = None
+        # '2023-04-11 09:31' (Linux)
+        started = re.findall(r"\d\d\d\d-\d\d-\d\d \d\d:\d\d", out)
+        if started:
+            tstamp = "%Y-%m-%d %H:%M"
+        else:
+            # 'Apr 10 22:27' (macOS)
+            started = re.findall(r"[A-Z][a-z][a-z] \d\d \d\d:\d\d", out)
+            if started:
+                tstamp = "%b %d %H:%M"
+            else:
+                # 'Apr 10'
+                started = re.findall(r"[A-Z][a-z][a-z] \d\d", out)
+                if started:
+                    tstamp = "%b %d"
+                else:
+                    # 'apr 10' (sunOS)
+                    started = re.findall(r"[a-z][a-z][a-z] \d\d", out)
+                    if started:
+                        tstamp = "%b %d"
+                        started = [x.capitalize() for x in started]
+
+        if not tstamp:
+            raise pytest.skip(f"cannot interpret tstamp in who output\n{out}")
+
+        with self.subTest(psutil=psutil.users(), who=out):
+            for idx, u in enumerate(psutil.users()):
+                psutil_value = datetime.datetime.fromtimestamp(
+                    u.started
+                ).strftime(tstamp)
+                assert psutil_value == started[idx]
+
+    def test_pid_exists_let_raise(self):
+        # According to "man 2 kill" possible error values for kill
+        # are (EINVAL, EPERM, ESRCH). Test that any other errno
+        # results in an exception.
+        with mock.patch(
+            "psutil._psposix.os.kill", side_effect=OSError(errno.EBADF, "")
+        ) as m:
+            with pytest.raises(OSError):
+                psutil._psposix.pid_exists(os.getpid())
+            assert m.called
+
+    def test_os_waitpid_let_raise(self):
+        # os.waitpid() is supposed to catch EINTR and ECHILD only.
+        # Test that any other errno results in an exception.
+        with mock.patch(
+            "psutil._psposix.os.waitpid", side_effect=OSError(errno.EBADF, "")
+        ) as m:
+            with pytest.raises(OSError):
+                psutil._psposix.wait_pid(os.getpid())
+            assert m.called
+
+    def test_os_waitpid_eintr(self):
+        # os.waitpid() is supposed to "retry" on EINTR.
+        with mock.patch(
+            "psutil._psposix.os.waitpid", side_effect=OSError(errno.EINTR, "")
+        ) as m:
+            with pytest.raises(psutil._psposix.TimeoutExpired):
+                psutil._psposix.wait_pid(os.getpid(), timeout=0.01)
+            assert m.called
+
+    def test_os_waitpid_bad_ret_status(self):
+        # Simulate os.waitpid() returning a bad status.
+        with mock.patch(
+            "psutil._psposix.os.waitpid", return_value=(1, -1)
+        ) as m:
+            with pytest.raises(ValueError):
+                psutil._psposix.wait_pid(os.getpid())
+            assert m.called
+
+    # AIX can return '-' in df output instead of numbers, e.g. for /proc
+    @pytest.mark.skipif(AIX, reason="unreliable on AIX")
+    @retry_on_failure()
+    def test_disk_usage(self):
+        tolerance = 4 * 1024 * 1024  # 4MB
+        for part in psutil.disk_partitions(all=False):
+            usage = psutil.disk_usage(part.mountpoint)
+            try:
+                sys_total, sys_used, sys_free, sys_percent = df(part.device)
+            except RuntimeError as err:
+                # see:
+                # https://travis-ci.org/giampaolo/psutil/jobs/138338464
+                # https://travis-ci.org/giampaolo/psutil/jobs/138343361
+                err = str(err).lower()
+                if (
+                    "no such file or directory" in err
+                    or "raw devices not supported" in err
+                    or "permission denied" in err
+                ):
+                    continue
+                raise
+            else:
+                assert abs(usage.total - sys_total) < tolerance
+                assert abs(usage.used - sys_used) < tolerance
+                assert abs(usage.free - sys_free) < tolerance
+                assert abs(usage.percent - sys_percent) <= 1
+
+
+@pytest.mark.skipif(not POSIX, reason="POSIX only")
+class TestMisc(PsutilTestCase):
+    def test_getpagesize(self):
+        pagesize = getpagesize()
+        assert pagesize > 0
+        assert pagesize == resource.getpagesize()
+        assert pagesize == mmap.PAGESIZE
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_process.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_process.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ba1ba0e3bc7934a62011fcb89f880020ff984a0
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_process.py
@@ -0,0 +1,1667 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Tests for psutil.Process class."""
+
+import collections
+import contextlib
+import errno
+import getpass
+import io
+import itertools
+import os
+import signal
+import socket
+import stat
+import string
+import subprocess
+import sys
+import textwrap
+import time
+from unittest import mock
+
+import psutil
+from psutil import AIX
+from psutil import BSD
+from psutil import LINUX
+from psutil import MACOS
+from psutil import NETBSD
+from psutil import OPENBSD
+from psutil import OSX
+from psutil import POSIX
+from psutil import WINDOWS
+from psutil._common import open_text
+from psutil.tests import CI_TESTING
+from psutil.tests import GITHUB_ACTIONS
+from psutil.tests import GLOBAL_TIMEOUT
+from psutil.tests import HAS_CPU_AFFINITY
+from psutil.tests import HAS_ENVIRON
+from psutil.tests import HAS_IONICE
+from psutil.tests import HAS_MEMORY_MAPS
+from psutil.tests import HAS_PROC_CPU_NUM
+from psutil.tests import HAS_PROC_IO_COUNTERS
+from psutil.tests import HAS_RLIMIT
+from psutil.tests import HAS_THREADS
+from psutil.tests import MACOS_11PLUS
+from psutil.tests import PYPY
+from psutil.tests import PYTHON_EXE
+from psutil.tests import PYTHON_EXE_ENV
+from psutil.tests import PsutilTestCase
+from psutil.tests import ThreadTask
+from psutil.tests import call_until
+from psutil.tests import copyload_shared_lib
+from psutil.tests import create_c_exe
+from psutil.tests import create_py_exe
+from psutil.tests import process_namespace
+from psutil.tests import pytest
+from psutil.tests import reap_children
+from psutil.tests import retry_on_failure
+from psutil.tests import sh
+from psutil.tests import skip_on_access_denied
+from psutil.tests import skip_on_not_implemented
+from psutil.tests import wait_for_pid
+
+
+# ===================================================================
+# --- psutil.Process class tests
+# ===================================================================
+
+
+class TestProcess(PsutilTestCase):
+    """Tests for psutil.Process class."""
+
+    def spawn_psproc(self, *args, **kwargs):
+        sproc = self.spawn_testproc(*args, **kwargs)
+        try:
+            return psutil.Process(sproc.pid)
+        except psutil.NoSuchProcess:
+            self.assertPidGone(sproc.pid)
+            raise
+
+    # ---
+
+    def test_pid(self):
+        p = psutil.Process()
+        assert p.pid == os.getpid()
+        with pytest.raises(AttributeError):
+            p.pid = 33
+
+    def test_kill(self):
+        p = self.spawn_psproc()
+        p.kill()
+        code = p.wait()
+        if WINDOWS:
+            assert code == signal.SIGTERM
+        else:
+            assert code == -signal.SIGKILL
+        self.assertProcessGone(p)
+
+    def test_terminate(self):
+        p = self.spawn_psproc()
+        p.terminate()
+        code = p.wait()
+        if WINDOWS:
+            assert code == signal.SIGTERM
+        else:
+            assert code == -signal.SIGTERM
+        self.assertProcessGone(p)
+
+    def test_send_signal(self):
+        sig = signal.SIGKILL if POSIX else signal.SIGTERM
+        p = self.spawn_psproc()
+        p.send_signal(sig)
+        code = p.wait()
+        if WINDOWS:
+            assert code == sig
+        else:
+            assert code == -sig
+        self.assertProcessGone(p)
+
+    @pytest.mark.skipif(not POSIX, reason="not POSIX")
+    def test_send_signal_mocked(self):
+        sig = signal.SIGTERM
+        p = self.spawn_psproc()
+        with mock.patch('psutil.os.kill', side_effect=ProcessLookupError):
+            with pytest.raises(psutil.NoSuchProcess):
+                p.send_signal(sig)
+
+        p = self.spawn_psproc()
+        with mock.patch('psutil.os.kill', side_effect=PermissionError):
+            with pytest.raises(psutil.AccessDenied):
+                p.send_signal(sig)
+
+    def test_wait_exited(self):
+        # Test waitpid() + WIFEXITED -> WEXITSTATUS.
+        # normal return, same as exit(0)
+        cmd = [PYTHON_EXE, "-c", "pass"]
+        p = self.spawn_psproc(cmd)
+        code = p.wait()
+        assert code == 0
+        self.assertProcessGone(p)
+        # exit(1), implicit in case of error
+        cmd = [PYTHON_EXE, "-c", "1 / 0"]
+        p = self.spawn_psproc(cmd, stderr=subprocess.PIPE)
+        code = p.wait()
+        assert code == 1
+        self.assertProcessGone(p)
+        # via sys.exit()
+        cmd = [PYTHON_EXE, "-c", "import sys; sys.exit(5);"]
+        p = self.spawn_psproc(cmd)
+        code = p.wait()
+        assert code == 5
+        self.assertProcessGone(p)
+        # via os._exit()
+        cmd = [PYTHON_EXE, "-c", "import os; os._exit(5);"]
+        p = self.spawn_psproc(cmd)
+        code = p.wait()
+        assert code == 5
+        self.assertProcessGone(p)
+
+    @pytest.mark.skipif(NETBSD, reason="fails on NETBSD")
+    def test_wait_stopped(self):
+        p = self.spawn_psproc()
+        if POSIX:
+            # Test waitpid() + WIFSTOPPED and WIFCONTINUED.
+            # Note: if a process is stopped it ignores SIGTERM.
+            p.send_signal(signal.SIGSTOP)
+            with pytest.raises(psutil.TimeoutExpired):
+                p.wait(timeout=0.001)
+            p.send_signal(signal.SIGCONT)
+            with pytest.raises(psutil.TimeoutExpired):
+                p.wait(timeout=0.001)
+            p.send_signal(signal.SIGTERM)
+            assert p.wait() == -signal.SIGTERM
+            assert p.wait() == -signal.SIGTERM
+        else:
+            p.suspend()
+            with pytest.raises(psutil.TimeoutExpired):
+                p.wait(timeout=0.001)
+            p.resume()
+            with pytest.raises(psutil.TimeoutExpired):
+                p.wait(timeout=0.001)
+            p.terminate()
+            assert p.wait() == signal.SIGTERM
+            assert p.wait() == signal.SIGTERM
+
+    def test_wait_non_children(self):
+        # Test wait() against a process which is not our direct
+        # child.
+        child, grandchild = self.spawn_children_pair()
+        with pytest.raises(psutil.TimeoutExpired):
+            child.wait(0.01)
+        with pytest.raises(psutil.TimeoutExpired):
+            grandchild.wait(0.01)
+        # We also terminate the direct child otherwise the
+        # grandchild will hang until the parent is gone.
+        child.terminate()
+        grandchild.terminate()
+        child_ret = child.wait()
+        grandchild_ret = grandchild.wait()
+        if POSIX:
+            assert child_ret == -signal.SIGTERM
+            # For processes which are not our children we're supposed
+            # to get None.
+            assert grandchild_ret is None
+        else:
+            assert child_ret == signal.SIGTERM
+            assert child_ret == signal.SIGTERM
+
+    def test_wait_timeout(self):
+        p = self.spawn_psproc()
+        p.name()
+        with pytest.raises(psutil.TimeoutExpired):
+            p.wait(0.01)
+        with pytest.raises(psutil.TimeoutExpired):
+            p.wait(0)
+        with pytest.raises(ValueError):
+            p.wait(-1)
+
+    def test_wait_timeout_nonblocking(self):
+        p = self.spawn_psproc()
+        with pytest.raises(psutil.TimeoutExpired):
+            p.wait(0)
+        p.kill()
+        stop_at = time.time() + GLOBAL_TIMEOUT
+        while time.time() < stop_at:
+            try:
+                code = p.wait(0)
+                break
+            except psutil.TimeoutExpired:
+                pass
+        else:
+            raise self.fail('timeout')
+        if POSIX:
+            assert code == -signal.SIGKILL
+        else:
+            assert code == signal.SIGTERM
+        self.assertProcessGone(p)
+
+    def test_cpu_percent(self):
+        p = psutil.Process()
+        p.cpu_percent(interval=0.001)
+        p.cpu_percent(interval=0.001)
+        for _ in range(100):
+            percent = p.cpu_percent(interval=None)
+            assert isinstance(percent, float)
+            assert percent >= 0.0
+        with pytest.raises(ValueError):
+            p.cpu_percent(interval=-1)
+
+    def test_cpu_percent_numcpus_none(self):
+        # See: https://github.com/giampaolo/psutil/issues/1087
+        with mock.patch('psutil.cpu_count', return_value=None) as m:
+            psutil.Process().cpu_percent()
+            assert m.called
+
+    def test_cpu_times(self):
+        times = psutil.Process().cpu_times()
+        assert times.user >= 0.0, times
+        assert times.system >= 0.0, times
+        assert times.children_user >= 0.0, times
+        assert times.children_system >= 0.0, times
+        if LINUX:
+            assert times.iowait >= 0.0, times
+        # make sure returned values can be pretty printed with strftime
+        for name in times._fields:
+            time.strftime("%H:%M:%S", time.localtime(getattr(times, name)))
+
+    def test_cpu_times_2(self):
+        def waste_cpu():
+            stop_at = os.times().user + 0.2
+            while os.times().user < stop_at:
+                for x in range(100000):
+                    x **= 2
+
+        waste_cpu()
+        a = psutil.Process().cpu_times()
+        b = os.times()
+        self.assertAlmostEqual(a.user, b.user, delta=0.1)
+        self.assertAlmostEqual(a.system, b.system, delta=0.1)
+
+    @pytest.mark.skipif(not HAS_PROC_CPU_NUM, reason="not supported")
+    def test_cpu_num(self):
+        p = psutil.Process()
+        num = p.cpu_num()
+        assert num >= 0
+        if psutil.cpu_count() == 1:
+            assert num == 0
+        assert p.cpu_num() in range(psutil.cpu_count())
+
+    def test_create_time(self):
+        p = self.spawn_psproc()
+        now = time.time()
+        create_time = p.create_time()
+
+        # Use time.time() as base value to compare our result using a
+        # tolerance of +/- 1 second.
+        # It will fail if the difference between the values is > 2s.
+        difference = abs(create_time - now)
+        if difference > 2:
+            raise self.fail(
+                f"expected: {now}, found: {create_time}, difference:"
+                f" {difference}"
+            )
+
+        # make sure returned value can be pretty printed with strftime
+        time.strftime("%Y %m %d %H:%M:%S", time.localtime(p.create_time()))
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_terminal(self):
+        terminal = psutil.Process().terminal()
+        if terminal is not None:
+            try:
+                tty = os.path.realpath(sh('tty'))
+            except RuntimeError:
+                # Note: happens if pytest is run without the `-s` opt.
+                raise pytest.skip("can't rely on `tty` CLI")
+            else:
+                assert terminal == tty
+
+    @pytest.mark.skipif(not HAS_PROC_IO_COUNTERS, reason="not supported")
+    @skip_on_not_implemented(only_if=LINUX)
+    def test_io_counters(self):
+        p = psutil.Process()
+        # test reads
+        io1 = p.io_counters()
+        with open(PYTHON_EXE, 'rb') as f:
+            f.read()
+        io2 = p.io_counters()
+        if not BSD and not AIX:
+            assert io2.read_count > io1.read_count
+            assert io2.write_count == io1.write_count
+            if LINUX:
+                assert io2.read_chars > io1.read_chars
+                assert io2.write_chars == io1.write_chars
+        else:
+            assert io2.read_bytes >= io1.read_bytes
+            assert io2.write_bytes >= io1.write_bytes
+
+        # test writes
+        io1 = p.io_counters()
+        with open(self.get_testfn(), 'wb') as f:
+            f.write(bytes("x" * 1000000, 'ascii'))
+        io2 = p.io_counters()
+        assert io2.write_count >= io1.write_count
+        assert io2.write_bytes >= io1.write_bytes
+        assert io2.read_count >= io1.read_count
+        assert io2.read_bytes >= io1.read_bytes
+        if LINUX:
+            assert io2.write_chars > io1.write_chars
+            assert io2.read_chars >= io1.read_chars
+
+        # sanity check
+        for i in range(len(io2)):
+            if BSD and i >= 2:
+                # On BSD read_bytes and write_bytes are always set to -1.
+                continue
+            assert io2[i] >= 0
+            assert io2[i] >= 0
+
+    @pytest.mark.skipif(not HAS_IONICE, reason="not supported")
+    @pytest.mark.skipif(not LINUX, reason="linux only")
+    def test_ionice_linux(self):
+        def cleanup(init):
+            ioclass, value = init
+            if ioclass == psutil.IOPRIO_CLASS_NONE:
+                value = 0
+            p.ionice(ioclass, value)
+
+        p = psutil.Process()
+        if not CI_TESTING:
+            assert p.ionice()[0] == psutil.IOPRIO_CLASS_NONE
+        assert psutil.IOPRIO_CLASS_NONE == 0
+        assert psutil.IOPRIO_CLASS_RT == 1  # high
+        assert psutil.IOPRIO_CLASS_BE == 2  # normal
+        assert psutil.IOPRIO_CLASS_IDLE == 3  # low
+        init = p.ionice()
+        self.addCleanup(cleanup, init)
+
+        # low
+        p.ionice(psutil.IOPRIO_CLASS_IDLE)
+        assert tuple(p.ionice()) == (psutil.IOPRIO_CLASS_IDLE, 0)
+        with pytest.raises(ValueError):  # accepts no value
+            p.ionice(psutil.IOPRIO_CLASS_IDLE, value=7)
+        # normal
+        p.ionice(psutil.IOPRIO_CLASS_BE)
+        assert tuple(p.ionice()) == (psutil.IOPRIO_CLASS_BE, 0)
+        p.ionice(psutil.IOPRIO_CLASS_BE, value=7)
+        assert tuple(p.ionice()) == (psutil.IOPRIO_CLASS_BE, 7)
+        with pytest.raises(ValueError):
+            p.ionice(psutil.IOPRIO_CLASS_BE, value=8)
+        try:
+            p.ionice(psutil.IOPRIO_CLASS_RT, value=7)
+        except psutil.AccessDenied:
+            pass
+        # errs
+        with pytest.raises(ValueError, match="ioclass accepts no value"):
+            p.ionice(psutil.IOPRIO_CLASS_NONE, 1)
+        with pytest.raises(ValueError, match="ioclass accepts no value"):
+            p.ionice(psutil.IOPRIO_CLASS_IDLE, 1)
+        with pytest.raises(
+            ValueError, match="'ioclass' argument must be specified"
+        ):
+            p.ionice(value=1)
+
+    @pytest.mark.skipif(not HAS_IONICE, reason="not supported")
+    @pytest.mark.skipif(
+        not WINDOWS, reason="not supported on this win version"
+    )
+    def test_ionice_win(self):
+        p = psutil.Process()
+        if not CI_TESTING:
+            assert p.ionice() == psutil.IOPRIO_NORMAL
+        init = p.ionice()
+        self.addCleanup(p.ionice, init)
+
+        # base
+        p.ionice(psutil.IOPRIO_VERYLOW)
+        assert p.ionice() == psutil.IOPRIO_VERYLOW
+        p.ionice(psutil.IOPRIO_LOW)
+        assert p.ionice() == psutil.IOPRIO_LOW
+        try:
+            p.ionice(psutil.IOPRIO_HIGH)
+        except psutil.AccessDenied:
+            pass
+        else:
+            assert p.ionice() == psutil.IOPRIO_HIGH
+        # errs
+        with pytest.raises(
+            TypeError, match="value argument not accepted on Windows"
+        ):
+            p.ionice(psutil.IOPRIO_NORMAL, value=1)
+        with pytest.raises(ValueError, match="is not a valid priority"):
+            p.ionice(psutil.IOPRIO_HIGH + 1)
+
+    @pytest.mark.skipif(not HAS_RLIMIT, reason="not supported")
+    def test_rlimit_get(self):
+        import resource
+
+        p = psutil.Process(os.getpid())
+        names = [x for x in dir(psutil) if x.startswith('RLIMIT')]
+        assert names, names
+        for name in names:
+            value = getattr(psutil, name)
+            assert value >= 0
+            if name in dir(resource):
+                assert value == getattr(resource, name)
+                # XXX - On PyPy RLIMIT_INFINITY returned by
+                # resource.getrlimit() is reported as a very big long
+                # number instead of -1. It looks like a bug with PyPy.
+                if PYPY:
+                    continue
+                assert p.rlimit(value) == resource.getrlimit(value)
+            else:
+                ret = p.rlimit(value)
+                assert len(ret) == 2
+                assert ret[0] >= -1
+                assert ret[1] >= -1
+
+    @pytest.mark.skipif(not HAS_RLIMIT, reason="not supported")
+    def test_rlimit_set(self):
+        p = self.spawn_psproc()
+        p.rlimit(psutil.RLIMIT_NOFILE, (5, 5))
+        assert p.rlimit(psutil.RLIMIT_NOFILE) == (5, 5)
+        # If pid is 0 prlimit() applies to the calling process and
+        # we don't want that.
+        if LINUX:
+            with pytest.raises(ValueError, match="can't use prlimit"):
+                psutil._psplatform.Process(0).rlimit(0)
+        with pytest.raises(ValueError):
+            p.rlimit(psutil.RLIMIT_NOFILE, (5, 5, 5))
+
+    @pytest.mark.skipif(not HAS_RLIMIT, reason="not supported")
+    def test_rlimit(self):
+        p = psutil.Process()
+        testfn = self.get_testfn()
+        soft, hard = p.rlimit(psutil.RLIMIT_FSIZE)
+        try:
+            p.rlimit(psutil.RLIMIT_FSIZE, (1024, hard))
+            with open(testfn, "wb") as f:
+                f.write(b"X" * 1024)
+            # write() or flush() doesn't always cause the exception
+            # but close() will.
+            with pytest.raises(OSError) as exc:
+                with open(testfn, "wb") as f:
+                    f.write(b"X" * 1025)
+            assert exc.value.errno == errno.EFBIG
+        finally:
+            p.rlimit(psutil.RLIMIT_FSIZE, (soft, hard))
+            assert p.rlimit(psutil.RLIMIT_FSIZE) == (soft, hard)
+
+    @pytest.mark.skipif(not HAS_RLIMIT, reason="not supported")
+    def test_rlimit_infinity(self):
+        # First set a limit, then re-set it by specifying INFINITY
+        # and assume we overridden the previous limit.
+        p = psutil.Process()
+        soft, hard = p.rlimit(psutil.RLIMIT_FSIZE)
+        try:
+            p.rlimit(psutil.RLIMIT_FSIZE, (1024, hard))
+            p.rlimit(psutil.RLIMIT_FSIZE, (psutil.RLIM_INFINITY, hard))
+            with open(self.get_testfn(), "wb") as f:
+                f.write(b"X" * 2048)
+        finally:
+            p.rlimit(psutil.RLIMIT_FSIZE, (soft, hard))
+            assert p.rlimit(psutil.RLIMIT_FSIZE) == (soft, hard)
+
+    @pytest.mark.skipif(not HAS_RLIMIT, reason="not supported")
+    def test_rlimit_infinity_value(self):
+        # RLIMIT_FSIZE should be RLIM_INFINITY, which will be a really
+        # big number on a platform with large file support.  On these
+        # platforms we need to test that the get/setrlimit functions
+        # properly convert the number to a C long long and that the
+        # conversion doesn't raise an error.
+        p = psutil.Process()
+        soft, hard = p.rlimit(psutil.RLIMIT_FSIZE)
+        assert hard == psutil.RLIM_INFINITY
+        p.rlimit(psutil.RLIMIT_FSIZE, (soft, hard))
+
+    def test_num_threads(self):
+        # on certain platforms such as Linux we might test for exact
+        # thread number, since we always have with 1 thread per process,
+        # but this does not apply across all platforms (MACOS, Windows)
+        p = psutil.Process()
+        if OPENBSD:
+            try:
+                step1 = p.num_threads()
+            except psutil.AccessDenied:
+                raise pytest.skip("on OpenBSD this requires root access")
+        else:
+            step1 = p.num_threads()
+
+        with ThreadTask():
+            step2 = p.num_threads()
+            assert step2 == step1 + 1
+
+    @pytest.mark.skipif(not WINDOWS, reason="WINDOWS only")
+    def test_num_handles(self):
+        # a better test is done later into test/_windows.py
+        p = psutil.Process()
+        assert p.num_handles() > 0
+
+    @pytest.mark.skipif(not HAS_THREADS, reason="not supported")
+    def test_threads(self):
+        p = psutil.Process()
+        if OPENBSD:
+            try:
+                step1 = p.threads()
+            except psutil.AccessDenied:
+                raise pytest.skip("on OpenBSD this requires root access")
+        else:
+            step1 = p.threads()
+
+        with ThreadTask():
+            step2 = p.threads()
+            assert len(step2) == len(step1) + 1
+            athread = step2[0]
+            # test named tuple
+            assert athread.id == athread[0]
+            assert athread.user_time == athread[1]
+            assert athread.system_time == athread[2]
+
+    @retry_on_failure()
+    @skip_on_access_denied(only_if=MACOS)
+    @pytest.mark.skipif(not HAS_THREADS, reason="not supported")
+    def test_threads_2(self):
+        p = self.spawn_psproc()
+        if OPENBSD:
+            try:
+                p.threads()
+            except psutil.AccessDenied:
+                raise pytest.skip("on OpenBSD this requires root access")
+        assert (
+            abs(p.cpu_times().user - sum(x.user_time for x in p.threads()))
+            < 0.1
+        )
+        assert (
+            abs(p.cpu_times().system - sum(x.system_time for x in p.threads()))
+            < 0.1
+        )
+
+    @retry_on_failure()
+    def test_memory_info(self):
+        p = psutil.Process()
+
+        # step 1 - get a base value to compare our results
+        rss1, vms1 = p.memory_info()[:2]
+        percent1 = p.memory_percent()
+        assert rss1 > 0
+        assert vms1 > 0
+
+        # step 2 - allocate some memory
+        memarr = [None] * 1500000
+
+        rss2, vms2 = p.memory_info()[:2]
+        percent2 = p.memory_percent()
+
+        # step 3 - make sure that the memory usage bumped up
+        assert rss2 > rss1
+        assert vms2 >= vms1  # vms might be equal
+        assert percent2 > percent1
+        del memarr
+
+        if WINDOWS:
+            mem = p.memory_info()
+            assert mem.rss == mem.wset
+            assert mem.vms == mem.pagefile
+
+        mem = p.memory_info()
+        for name in mem._fields:
+            assert getattr(mem, name) >= 0
+
+    def test_memory_full_info(self):
+        p = psutil.Process()
+        total = psutil.virtual_memory().total
+        mem = p.memory_full_info()
+        for name in mem._fields:
+            value = getattr(mem, name)
+            assert value >= 0
+            if (name == "vms" and OSX) or LINUX:
+                continue
+            assert value <= total
+        if LINUX or WINDOWS or MACOS:
+            assert mem.uss >= 0
+        if LINUX:
+            assert mem.pss >= 0
+            assert mem.swap >= 0
+
+    @pytest.mark.skipif(not HAS_MEMORY_MAPS, reason="not supported")
+    def test_memory_maps(self):
+        p = psutil.Process()
+        maps = p.memory_maps()
+        assert len(maps) == len(set(maps))
+        ext_maps = p.memory_maps(grouped=False)
+
+        for nt in maps:
+            if nt.path.startswith('['):
+                continue
+            if BSD and nt.path == "pvclock":
+                continue
+            assert os.path.isabs(nt.path), nt.path
+
+            if POSIX:
+                try:
+                    assert os.path.exists(nt.path) or os.path.islink(
+                        nt.path
+                    ), nt.path
+                except AssertionError:
+                    if not LINUX:
+                        raise
+                    # https://github.com/giampaolo/psutil/issues/759
+                    with open_text('/proc/self/smaps') as f:
+                        data = f.read()
+                    if f"{nt.path} (deleted)" not in data:
+                        raise
+            elif '64' not in os.path.basename(nt.path):
+                # XXX - On Windows we have this strange behavior with
+                # 64 bit dlls: they are visible via explorer but cannot
+                # be accessed via os.stat() (wtf?).
+                try:
+                    st = os.stat(nt.path)
+                except FileNotFoundError:
+                    pass
+                else:
+                    assert stat.S_ISREG(st.st_mode), nt.path
+
+        for nt in ext_maps:
+            for fname in nt._fields:
+                value = getattr(nt, fname)
+                if fname == 'path':
+                    continue
+                if fname in {'addr', 'perms'}:
+                    assert value, value
+                else:
+                    assert isinstance(value, int)
+                    assert value >= 0, value
+
+    @pytest.mark.skipif(not HAS_MEMORY_MAPS, reason="not supported")
+    def test_memory_maps_lists_lib(self):
+        # Make sure a newly loaded shared lib is listed.
+        p = psutil.Process()
+        with copyload_shared_lib() as path:
+
+            def normpath(p):
+                return os.path.realpath(os.path.normcase(p))
+
+            libpaths = [normpath(x.path) for x in p.memory_maps()]
+            assert normpath(path) in libpaths
+
+    def test_memory_percent(self):
+        p = psutil.Process()
+        p.memory_percent()
+        with pytest.raises(ValueError):
+            p.memory_percent(memtype="?!?")
+        if LINUX or MACOS or WINDOWS:
+            p.memory_percent(memtype='uss')
+
+    def test_is_running(self):
+        p = self.spawn_psproc()
+        assert p.is_running()
+        assert p.is_running()
+        p.kill()
+        p.wait()
+        assert not p.is_running()
+        assert not p.is_running()
+
+    def test_exe(self):
+        p = self.spawn_psproc()
+        exe = p.exe()
+        try:
+            assert exe == PYTHON_EXE
+        except AssertionError:
+            if WINDOWS and len(exe) == len(PYTHON_EXE):
+                # on Windows we don't care about case sensitivity
+                normcase = os.path.normcase
+                assert normcase(exe) == normcase(PYTHON_EXE)
+            else:
+                # certain platforms such as BSD are more accurate returning:
+                # "/usr/local/bin/python3.7"
+                # ...instead of:
+                # "/usr/local/bin/python"
+                # We do not want to consider this difference in accuracy
+                # an error.
+                ver = f"{sys.version_info[0]}.{sys.version_info[1]}"
+                try:
+                    assert exe.replace(ver, '') == PYTHON_EXE.replace(ver, '')
+                except AssertionError:
+                    # Typically MACOS. Really not sure what to do here.
+                    pass
+
+        out = sh([exe, "-c", "import os; print('hey')"])
+        assert out == 'hey'
+
+    def test_cmdline(self):
+        cmdline = [
+            PYTHON_EXE,
+            "-c",
+            "import time; [time.sleep(0.1) for x in range(100)]",
+        ]
+        p = self.spawn_psproc(cmdline)
+
+        if NETBSD and p.cmdline() == []:
+            # https://github.com/giampaolo/psutil/issues/2250
+            raise pytest.skip("OPENBSD: returned EBUSY")
+
+        # XXX - most of the times the underlying sysctl() call on Net
+        # and Open BSD returns a truncated string.
+        # Also /proc/pid/cmdline behaves the same so it looks
+        # like this is a kernel bug.
+        # XXX - AIX truncates long arguments in /proc/pid/cmdline
+        if NETBSD or OPENBSD or AIX:
+            assert p.cmdline()[0] == PYTHON_EXE
+        else:
+            if MACOS and CI_TESTING:
+                pyexe = p.cmdline()[0]
+                if pyexe != PYTHON_EXE:
+                    assert ' '.join(p.cmdline()[1:]) == ' '.join(cmdline[1:])
+                    return
+            assert ' '.join(p.cmdline()) == ' '.join(cmdline)
+
+    @pytest.mark.skipif(PYPY, reason="broken on PYPY")
+    def test_long_cmdline(self):
+        cmdline = [PYTHON_EXE]
+        cmdline.extend(["-v"] * 50)
+        cmdline.extend(
+            ["-c", "import time; [time.sleep(0.1) for x in range(100)]"]
+        )
+        p = self.spawn_psproc(cmdline)
+        if OPENBSD:
+            # XXX: for some reason the test process may turn into a
+            # zombie (don't know why).
+            try:
+                assert p.cmdline() == cmdline
+            except psutil.ZombieProcess:
+                raise pytest.skip("OPENBSD: process turned into zombie")
+        else:
+            ret = p.cmdline()
+            if NETBSD and ret == []:
+                # https://github.com/giampaolo/psutil/issues/2250
+                raise pytest.skip("OPENBSD: returned EBUSY")
+            assert ret == cmdline
+
+    def test_name(self):
+        p = self.spawn_psproc()
+        name = p.name().lower()
+        pyexe = os.path.basename(os.path.realpath(sys.executable)).lower()
+        assert pyexe.startswith(name), (pyexe, name)
+
+    @pytest.mark.skipif(PYPY, reason="unreliable on PYPY")
+    def test_long_name(self):
+        pyexe = create_py_exe(self.get_testfn(suffix=string.digits * 2))
+        cmdline = [
+            pyexe,
+            "-c",
+            "import time; [time.sleep(0.1) for x in range(100)]",
+        ]
+        p = self.spawn_psproc(cmdline)
+        if OPENBSD:
+            # XXX: for some reason the test process may turn into a
+            # zombie (don't know why). Because the name() is long, all
+            # UNIX kernels truncate it to 15 chars, so internally psutil
+            # tries to guess the full name() from the cmdline(). But the
+            # cmdline() of a zombie on OpenBSD fails (internally), so we
+            # just compare the first 15 chars. Full explanation:
+            # https://github.com/giampaolo/psutil/issues/2239
+            try:
+                assert p.name() == os.path.basename(pyexe)
+            except AssertionError:
+                if p.status() == psutil.STATUS_ZOMBIE:
+                    assert os.path.basename(pyexe).startswith(p.name())
+                else:
+                    raise
+        else:
+            assert p.name() == os.path.basename(pyexe)
+
+    # XXX: fails too often
+    # @pytest.mark.skipif(SUNOS, reason="broken on SUNOS")
+    # @pytest.mark.skipif(AIX, reason="broken on AIX")
+    # @pytest.mark.skipif(PYPY, reason="broken on PYPY")
+    # def test_prog_w_funky_name(self):
+    #     # Test that name(), exe() and cmdline() correctly handle programs
+    #     # with funky chars such as spaces and ")", see:
+    #     # https://github.com/giampaolo/psutil/issues/628
+    #     pyexe = create_py_exe(self.get_testfn(suffix='foo bar )'))
+    #     cmdline = [
+    #         pyexe,
+    #         "-c",
+    #         "import time; [time.sleep(0.1) for x in range(100)]",
+    #     ]
+    #     p = self.spawn_psproc(cmdline)
+    #     assert p.cmdline() == cmdline
+    #     assert p.name() == os.path.basename(pyexe)
+    #     assert os.path.normcase(p.exe()) == os.path.normcase(pyexe)
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_uids(self):
+        p = psutil.Process()
+        real, effective, _saved = p.uids()
+        # os.getuid() refers to "real" uid
+        assert real == os.getuid()
+        # os.geteuid() refers to "effective" uid
+        assert effective == os.geteuid()
+        # No such thing as os.getsuid() ("saved" uid), but we have
+        # os.getresuid() which returns all of them.
+        if hasattr(os, "getresuid"):
+            assert os.getresuid() == p.uids()
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_gids(self):
+        p = psutil.Process()
+        real, effective, _saved = p.gids()
+        # os.getuid() refers to "real" uid
+        assert real == os.getgid()
+        # os.geteuid() refers to "effective" uid
+        assert effective == os.getegid()
+        # No such thing as os.getsgid() ("saved" gid), but we have
+        # os.getresgid() which returns all of them.
+        if hasattr(os, "getresuid"):
+            assert os.getresgid() == p.gids()
+
+    def test_nice(self):
+        def cleanup(init):
+            try:
+                p.nice(init)
+            except psutil.AccessDenied:
+                pass
+
+        p = psutil.Process()
+        with pytest.raises(TypeError):
+            p.nice("str")
+        init = p.nice()
+        self.addCleanup(cleanup, init)
+
+        if WINDOWS:
+            highest_prio = None
+            for prio in [
+                psutil.IDLE_PRIORITY_CLASS,
+                psutil.BELOW_NORMAL_PRIORITY_CLASS,
+                psutil.NORMAL_PRIORITY_CLASS,
+                psutil.ABOVE_NORMAL_PRIORITY_CLASS,
+                psutil.HIGH_PRIORITY_CLASS,
+                psutil.REALTIME_PRIORITY_CLASS,
+            ]:
+                with self.subTest(prio=prio):
+                    try:
+                        p.nice(prio)
+                    except psutil.AccessDenied:
+                        pass
+                    else:
+                        new_prio = p.nice()
+                        # The OS may limit our maximum priority,
+                        # even if the function succeeds. For higher
+                        # priorities, we match either the expected
+                        # value or the highest so far.
+                        if prio in {
+                            psutil.ABOVE_NORMAL_PRIORITY_CLASS,
+                            psutil.HIGH_PRIORITY_CLASS,
+                            psutil.REALTIME_PRIORITY_CLASS,
+                        }:
+                            if new_prio == prio or highest_prio is None:
+                                highest_prio = prio
+                                assert new_prio == highest_prio
+                        else:
+                            assert new_prio == prio
+        else:
+            try:
+                if hasattr(os, "getpriority"):
+                    assert (
+                        os.getpriority(os.PRIO_PROCESS, os.getpid())
+                        == p.nice()
+                    )
+                p.nice(1)
+                assert p.nice() == 1
+                if hasattr(os, "getpriority"):
+                    assert (
+                        os.getpriority(os.PRIO_PROCESS, os.getpid())
+                        == p.nice()
+                    )
+                # XXX - going back to previous nice value raises
+                # AccessDenied on MACOS
+                if not MACOS:
+                    p.nice(0)
+                    assert p.nice() == 0
+            except psutil.AccessDenied:
+                pass
+
+    def test_status(self):
+        p = psutil.Process()
+        assert p.status() == psutil.STATUS_RUNNING
+
+    def test_username(self):
+        p = self.spawn_psproc()
+        username = p.username()
+        if WINDOWS:
+            domain, username = username.split('\\')
+            getpass_user = getpass.getuser()
+            if getpass_user.endswith('$'):
+                # When running as a service account (most likely to be
+                # NetworkService), these user name calculations don't produce
+                # the same result, causing the test to fail.
+                raise pytest.skip('running as service account')
+            assert username == getpass_user
+            if 'USERDOMAIN' in os.environ:
+                assert domain == os.environ['USERDOMAIN']
+        else:
+            assert username == getpass.getuser()
+
+    def test_cwd(self):
+        p = self.spawn_psproc()
+        assert p.cwd() == os.getcwd()
+
+    def test_cwd_2(self):
+        cmd = [
+            PYTHON_EXE,
+            "-c",
+            (
+                "import os, time; os.chdir('..'); [time.sleep(0.1) for x in"
+                " range(100)]"
+            ),
+        ]
+        p = self.spawn_psproc(cmd)
+        call_until(lambda: p.cwd() == os.path.dirname(os.getcwd()))
+
+    @pytest.mark.skipif(not HAS_CPU_AFFINITY, reason="not supported")
+    def test_cpu_affinity(self):
+        p = psutil.Process()
+        initial = p.cpu_affinity()
+        assert initial, initial
+        self.addCleanup(p.cpu_affinity, initial)
+
+        if hasattr(os, "sched_getaffinity"):
+            assert initial == list(os.sched_getaffinity(p.pid))
+        assert len(initial) == len(set(initial))
+
+        all_cpus = list(range(len(psutil.cpu_percent(percpu=True))))
+        for n in all_cpus:
+            p.cpu_affinity([n])
+            assert p.cpu_affinity() == [n]
+            if hasattr(os, "sched_getaffinity"):
+                assert p.cpu_affinity() == list(os.sched_getaffinity(p.pid))
+            # also test num_cpu()
+            if hasattr(p, "num_cpu"):
+                assert p.cpu_affinity()[0] == p.num_cpu()
+
+        # [] is an alias for "all eligible CPUs"; on Linux this may
+        # not be equal to all available CPUs, see:
+        # https://github.com/giampaolo/psutil/issues/956
+        p.cpu_affinity([])
+        if LINUX:
+            assert p.cpu_affinity() == p._proc._get_eligible_cpus()
+        else:
+            assert p.cpu_affinity() == all_cpus
+        if hasattr(os, "sched_getaffinity"):
+            assert p.cpu_affinity() == list(os.sched_getaffinity(p.pid))
+
+        with pytest.raises(TypeError):
+            p.cpu_affinity(1)
+        p.cpu_affinity(initial)
+        # it should work with all iterables, not only lists
+        p.cpu_affinity(set(all_cpus))
+        p.cpu_affinity(tuple(all_cpus))
+
+    @pytest.mark.skipif(not HAS_CPU_AFFINITY, reason="not supported")
+    def test_cpu_affinity_errs(self):
+        p = self.spawn_psproc()
+        invalid_cpu = [len(psutil.cpu_times(percpu=True)) + 10]
+        with pytest.raises(ValueError):
+            p.cpu_affinity(invalid_cpu)
+        with pytest.raises(ValueError):
+            p.cpu_affinity(range(10000, 11000))
+        with pytest.raises((TypeError, ValueError)):
+            p.cpu_affinity([0, "1"])
+        with pytest.raises(ValueError):
+            p.cpu_affinity([0, -1])
+
+    @pytest.mark.skipif(not HAS_CPU_AFFINITY, reason="not supported")
+    def test_cpu_affinity_all_combinations(self):
+        p = psutil.Process()
+        initial = p.cpu_affinity()
+        assert initial, initial
+        self.addCleanup(p.cpu_affinity, initial)
+
+        # All possible CPU set combinations.
+        if len(initial) > 12:
+            initial = initial[:12]  # ...otherwise it will take forever
+        combos = []
+        for i in range(len(initial) + 1):
+            combos.extend(
+                list(subset)
+                for subset in itertools.combinations(initial, i)
+                if subset
+            )
+
+        for combo in combos:
+            p.cpu_affinity(combo)
+            assert sorted(p.cpu_affinity()) == sorted(combo)
+
+    # TODO: #595
+    @pytest.mark.skipif(BSD, reason="broken on BSD")
+    def test_open_files(self):
+        p = psutil.Process()
+        testfn = self.get_testfn()
+        files = p.open_files()
+        assert testfn not in files
+        with open(testfn, 'wb') as f:
+            f.write(b'x' * 1024)
+            f.flush()
+            # give the kernel some time to see the new file
+            call_until(lambda: len(p.open_files()) != len(files))
+            files = p.open_files()
+            filenames = [os.path.normcase(x.path) for x in files]
+            assert os.path.normcase(testfn) in filenames
+            if LINUX:
+                for file in files:
+                    if file.path == testfn:
+                        assert file.position == 1024
+        for file in files:
+            assert os.path.isfile(file.path), file
+
+        # another process
+        cmdline = (
+            f"import time; f = open(r'{testfn}', 'r'); [time.sleep(0.1) for x"
+            " in range(100)];"
+        )
+        p = self.spawn_psproc([PYTHON_EXE, "-c", cmdline])
+
+        for x in range(100):
+            filenames = [os.path.normcase(x.path) for x in p.open_files()]
+            if testfn in filenames:
+                break
+            time.sleep(0.01)
+        else:
+            assert os.path.normcase(testfn) in filenames
+        for file in filenames:
+            assert os.path.isfile(file), file
+
+    # TODO: #595
+    @pytest.mark.skipif(BSD, reason="broken on BSD")
+    def test_open_files_2(self):
+        # test fd and path fields
+        p = psutil.Process()
+        normcase = os.path.normcase
+        testfn = self.get_testfn()
+        with open(testfn, 'w') as fileobj:
+            for file in p.open_files():
+                if (
+                    normcase(file.path) == normcase(fileobj.name)
+                    or file.fd == fileobj.fileno()
+                ):
+                    break
+            else:
+                raise self.fail(f"no file found; files={p.open_files()!r}")
+            assert normcase(file.path) == normcase(fileobj.name)
+            if WINDOWS:
+                assert file.fd == -1
+            else:
+                assert file.fd == fileobj.fileno()
+            # test positions
+            ntuple = p.open_files()[0]
+            assert ntuple[0] == ntuple.path
+            assert ntuple[1] == ntuple.fd
+            # test file is gone
+            assert fileobj.name not in p.open_files()
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_num_fds(self):
+        p = psutil.Process()
+        testfn = self.get_testfn()
+        start = p.num_fds()
+        file = open(testfn, 'w')  # noqa: SIM115
+        self.addCleanup(file.close)
+        assert p.num_fds() == start + 1
+        sock = socket.socket()
+        self.addCleanup(sock.close)
+        assert p.num_fds() == start + 2
+        file.close()
+        sock.close()
+        assert p.num_fds() == start
+
+    @skip_on_not_implemented(only_if=LINUX)
+    @pytest.mark.skipif(
+        OPENBSD or NETBSD, reason="not reliable on OPENBSD & NETBSD"
+    )
+    def test_num_ctx_switches(self):
+        p = psutil.Process()
+        before = sum(p.num_ctx_switches())
+        for _ in range(2):
+            time.sleep(0.05)  # this shall ensure a context switch happens
+            after = sum(p.num_ctx_switches())
+            if after > before:
+                return
+        raise self.fail("num ctx switches still the same after 2 iterations")
+
+    def test_ppid(self):
+        p = psutil.Process()
+        if hasattr(os, 'getppid'):
+            assert p.ppid() == os.getppid()
+        p = self.spawn_psproc()
+        assert p.ppid() == os.getpid()
+
+    def test_parent(self):
+        p = self.spawn_psproc()
+        assert p.parent().pid == os.getpid()
+
+        lowest_pid = psutil.pids()[0]
+        assert psutil.Process(lowest_pid).parent() is None
+
+    def test_parent_multi(self):
+        parent = psutil.Process()
+        child, grandchild = self.spawn_children_pair()
+        assert grandchild.parent() == child
+        assert child.parent() == parent
+
+    @retry_on_failure()
+    def test_parents(self):
+        parent = psutil.Process()
+        assert parent.parents()
+        child, grandchild = self.spawn_children_pair()
+        assert child.parents()[0] == parent
+        assert grandchild.parents()[0] == child
+        assert grandchild.parents()[1] == parent
+
+    def test_children(self):
+        parent = psutil.Process()
+        assert not parent.children()
+        assert not parent.children(recursive=True)
+        # On Windows we set the flag to 0 in order to cancel out the
+        # CREATE_NO_WINDOW flag (enabled by default) which creates
+        # an extra "conhost.exe" child.
+        child = self.spawn_psproc(creationflags=0)
+        children1 = parent.children()
+        children2 = parent.children(recursive=True)
+        for children in (children1, children2):
+            assert len(children) == 1
+            assert children[0].pid == child.pid
+            assert children[0].ppid() == parent.pid
+
+    def test_children_recursive(self):
+        # Test children() against two sub processes, p1 and p2, where
+        # p1 (our child) spawned p2 (our grandchild).
+        parent = psutil.Process()
+        child, grandchild = self.spawn_children_pair()
+        assert parent.children() == [child]
+        assert parent.children(recursive=True) == [child, grandchild]
+        # If the intermediate process is gone there's no way for
+        # children() to recursively find it.
+        child.terminate()
+        child.wait()
+        assert not parent.children(recursive=True)
+
+    def test_children_duplicates(self):
+        # find the process which has the highest number of children
+        table = collections.defaultdict(int)
+        for p in psutil.process_iter():
+            try:
+                table[p.ppid()] += 1
+            except psutil.Error:
+                pass
+        # this is the one, now let's make sure there are no duplicates
+        pid = max(table.items(), key=lambda x: x[1])[0]
+        if LINUX and pid == 0:
+            raise pytest.skip("PID 0")
+        p = psutil.Process(pid)
+        try:
+            c = p.children(recursive=True)
+        except psutil.AccessDenied:  # windows
+            pass
+        else:
+            assert len(c) == len(set(c))
+
+    def test_parents_and_children(self):
+        parent = psutil.Process()
+        child, grandchild = self.spawn_children_pair()
+        # forward
+        children = parent.children(recursive=True)
+        assert len(children) == 2
+        assert children[0] == child
+        assert children[1] == grandchild
+        # backward
+        parents = grandchild.parents()
+        assert parents[0] == child
+        assert parents[1] == parent
+
+    def test_suspend_resume(self):
+        p = self.spawn_psproc()
+        p.suspend()
+        for _ in range(100):
+            if p.status() == psutil.STATUS_STOPPED:
+                break
+            time.sleep(0.01)
+        p.resume()
+        assert p.status() != psutil.STATUS_STOPPED
+
+    def test_invalid_pid(self):
+        with pytest.raises(TypeError):
+            psutil.Process("1")
+        with pytest.raises(ValueError):
+            psutil.Process(-1)
+
+    def test_as_dict(self):
+        p = psutil.Process()
+        d = p.as_dict(attrs=['exe', 'name'])
+        assert sorted(d.keys()) == ['exe', 'name']
+
+        p = psutil.Process(min(psutil.pids()))
+        d = p.as_dict(attrs=['net_connections'], ad_value='foo')
+        if not isinstance(d['net_connections'], list):
+            assert d['net_connections'] == 'foo'
+
+        # Test ad_value is set on AccessDenied.
+        with mock.patch(
+            'psutil.Process.nice', create=True, side_effect=psutil.AccessDenied
+        ):
+            assert p.as_dict(attrs=["nice"], ad_value=1) == {"nice": 1}
+
+        # Test that NoSuchProcess bubbles up.
+        with mock.patch(
+            'psutil.Process.nice',
+            create=True,
+            side_effect=psutil.NoSuchProcess(p.pid, "name"),
+        ):
+            with pytest.raises(psutil.NoSuchProcess):
+                p.as_dict(attrs=["nice"])
+
+        # Test that ZombieProcess is swallowed.
+        with mock.patch(
+            'psutil.Process.nice',
+            create=True,
+            side_effect=psutil.ZombieProcess(p.pid, "name"),
+        ):
+            assert p.as_dict(attrs=["nice"], ad_value="foo") == {"nice": "foo"}
+
+        # By default APIs raising NotImplementedError are
+        # supposed to be skipped.
+        with mock.patch(
+            'psutil.Process.nice', create=True, side_effect=NotImplementedError
+        ):
+            d = p.as_dict()
+            assert 'nice' not in list(d.keys())
+            # ...unless the user explicitly asked for some attr.
+            with pytest.raises(NotImplementedError):
+                p.as_dict(attrs=["nice"])
+
+        # errors
+        with pytest.raises(TypeError):
+            p.as_dict('name')
+        with pytest.raises(ValueError):
+            p.as_dict(['foo'])
+        with pytest.raises(ValueError):
+            p.as_dict(['foo', 'bar'])
+
+    def test_oneshot(self):
+        p = psutil.Process()
+        with mock.patch("psutil._psplatform.Process.cpu_times") as m:
+            with p.oneshot():
+                p.cpu_times()
+                p.cpu_times()
+            assert m.call_count == 1
+
+        with mock.patch("psutil._psplatform.Process.cpu_times") as m:
+            p.cpu_times()
+            p.cpu_times()
+        assert m.call_count == 2
+
+    def test_oneshot_twice(self):
+        # Test the case where the ctx manager is __enter__ed twice.
+        # The second __enter__ is supposed to resut in a NOOP.
+        p = psutil.Process()
+        with mock.patch("psutil._psplatform.Process.cpu_times") as m1:
+            with mock.patch("psutil._psplatform.Process.oneshot_enter") as m2:
+                with p.oneshot():
+                    p.cpu_times()
+                    p.cpu_times()
+                    with p.oneshot():
+                        p.cpu_times()
+                        p.cpu_times()
+                assert m1.call_count == 1
+                assert m2.call_count == 1
+
+        with mock.patch("psutil._psplatform.Process.cpu_times") as m:
+            p.cpu_times()
+            p.cpu_times()
+        assert m.call_count == 2
+
+    def test_oneshot_cache(self):
+        # Make sure oneshot() cache is nonglobal. Instead it's
+        # supposed to be bound to the Process instance, see:
+        # https://github.com/giampaolo/psutil/issues/1373
+        p1, p2 = self.spawn_children_pair()
+        p1_ppid = p1.ppid()
+        p2_ppid = p2.ppid()
+        assert p1_ppid != p2_ppid
+        with p1.oneshot():
+            assert p1.ppid() == p1_ppid
+            assert p2.ppid() == p2_ppid
+        with p2.oneshot():
+            assert p1.ppid() == p1_ppid
+            assert p2.ppid() == p2_ppid
+
+    def test_halfway_terminated_process(self):
+        # Test that NoSuchProcess exception gets raised in case the
+        # process dies after we create the Process object.
+        # Example:
+        # >>> proc = Process(1234)
+        # >>> time.sleep(2)  # time-consuming task, process dies in meantime
+        # >>> proc.name()
+        # Refers to Issue #15
+        def assert_raises_nsp(fun, fun_name):
+            try:
+                ret = fun()
+            except psutil.ZombieProcess:  # differentiate from NSP
+                raise
+            except psutil.NoSuchProcess:
+                pass
+            except psutil.AccessDenied:
+                if OPENBSD and fun_name in {'threads', 'num_threads'}:
+                    return
+                raise
+            else:
+                # NtQuerySystemInformation succeeds even if process is gone.
+                if WINDOWS and fun_name in {'exe', 'name'}:
+                    return
+                raise self.fail(
+                    f"{fun!r} didn't raise NSP and returned {ret!r} instead"
+                )
+
+        p = self.spawn_psproc()
+        p.terminate()
+        p.wait()
+        if WINDOWS:  # XXX
+            call_until(lambda: p.pid not in psutil.pids())
+        self.assertProcessGone(p)
+
+        ns = process_namespace(p)
+        for fun, name in ns.iter(ns.all):
+            assert_raises_nsp(fun, name)
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_zombie_process(self):
+        _parent, zombie = self.spawn_zombie()
+        self.assertProcessZombie(zombie)
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_zombie_process_is_running_w_exc(self):
+        # Emulate a case where internally is_running() raises
+        # ZombieProcess.
+        p = psutil.Process()
+        with mock.patch(
+            "psutil.Process", side_effect=psutil.ZombieProcess(0)
+        ) as m:
+            assert p.is_running()
+            assert m.called
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_zombie_process_status_w_exc(self):
+        # Emulate a case where internally status() raises
+        # ZombieProcess.
+        p = psutil.Process()
+        with mock.patch(
+            "psutil._psplatform.Process.status",
+            side_effect=psutil.ZombieProcess(0),
+        ) as m:
+            assert p.status() == psutil.STATUS_ZOMBIE
+            assert m.called
+
+    def test_reused_pid(self):
+        # Emulate a case where PID has been reused by another process.
+        subp = self.spawn_testproc()
+        p = psutil.Process(subp.pid)
+        p._ident = (p.pid, p.create_time() + 100)
+
+        list(psutil.process_iter())
+        assert p.pid in psutil._pmap
+        assert not p.is_running()
+
+        # make sure is_running() removed PID from process_iter()
+        # internal cache
+        with mock.patch.object(psutil._common, "PSUTIL_DEBUG", True):
+            with contextlib.redirect_stderr(io.StringIO()) as f:
+                list(psutil.process_iter())
+        assert (
+            f"refreshing Process instance for reused PID {p.pid}"
+            in f.getvalue()
+        )
+        assert p.pid not in psutil._pmap
+
+        assert p != psutil.Process(subp.pid)
+        msg = "process no longer exists and its PID has been reused"
+        ns = process_namespace(p)
+        for fun, name in ns.iter(ns.setters + ns.killers, clear_cache=False):
+            with self.subTest(name=name):
+                with pytest.raises(psutil.NoSuchProcess, match=msg):
+                    fun()
+
+        assert "terminated + PID reused" in str(p)
+        assert "terminated + PID reused" in repr(p)
+
+        with pytest.raises(psutil.NoSuchProcess, match=msg):
+            p.ppid()
+        with pytest.raises(psutil.NoSuchProcess, match=msg):
+            p.parent()
+        with pytest.raises(psutil.NoSuchProcess, match=msg):
+            p.parents()
+        with pytest.raises(psutil.NoSuchProcess, match=msg):
+            p.children()
+
+    def test_pid_0(self):
+        # Process(0) is supposed to work on all platforms except Linux
+        if 0 not in psutil.pids():
+            with pytest.raises(psutil.NoSuchProcess):
+                psutil.Process(0)
+            # These 2 are a contradiction, but "ps" says PID 1's parent
+            # is PID 0.
+            assert not psutil.pid_exists(0)
+            assert psutil.Process(1).ppid() == 0
+            return
+
+        p = psutil.Process(0)
+        exc = psutil.AccessDenied if WINDOWS else ValueError
+        with pytest.raises(exc):
+            p.wait()
+        with pytest.raises(exc):
+            p.terminate()
+        with pytest.raises(exc):
+            p.suspend()
+        with pytest.raises(exc):
+            p.resume()
+        with pytest.raises(exc):
+            p.kill()
+        with pytest.raises(exc):
+            p.send_signal(signal.SIGTERM)
+
+        # test all methods
+        ns = process_namespace(p)
+        for fun, name in ns.iter(ns.getters + ns.setters):
+            try:
+                ret = fun()
+            except psutil.AccessDenied:
+                pass
+            else:
+                if name in {"uids", "gids"}:
+                    assert ret.real == 0
+                elif name == "username":
+                    user = 'NT AUTHORITY\\SYSTEM' if WINDOWS else 'root'
+                    assert p.username() == user
+                elif name == "name":
+                    assert name, name
+
+        if not OPENBSD:
+            assert 0 in psutil.pids()
+            assert psutil.pid_exists(0)
+
+    @pytest.mark.skipif(not HAS_ENVIRON, reason="not supported")
+    def test_environ(self):
+        def clean_dict(d):
+            exclude = ["PLAT", "HOME", "PYTEST_CURRENT_TEST", "PYTEST_VERSION"]
+            if MACOS:
+                exclude.extend([
+                    "__CF_USER_TEXT_ENCODING",
+                    "VERSIONER_PYTHON_PREFER_32_BIT",
+                    "VERSIONER_PYTHON_VERSION",
+                    "VERSIONER_PYTHON_VERSION",
+                ])
+            for name in exclude:
+                d.pop(name, None)
+            return {
+                k.replace("\r", "").replace("\n", ""): v.replace(
+                    "\r", ""
+                ).replace("\n", "")
+                for k, v in d.items()
+            }
+
+        self.maxDiff = None
+        p = psutil.Process()
+        d1 = clean_dict(p.environ())
+        d2 = clean_dict(os.environ.copy())
+        if not OSX and GITHUB_ACTIONS:
+            assert d1 == d2
+
+    @pytest.mark.skipif(not HAS_ENVIRON, reason="not supported")
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    @pytest.mark.skipif(
+        MACOS_11PLUS,
+        reason="macOS 11+ can't get another process environment, issue #2084",
+    )
+    @pytest.mark.skipif(
+        NETBSD, reason="sometimes fails on `assert is_running()`"
+    )
+    def test_weird_environ(self):
+        # environment variables can contain values without an equals sign
+        code = textwrap.dedent("""
+            #include <unistd.h>
+            #include <fcntl.h>
+
+            char * const argv[] = {"cat", 0};
+            char * const envp[] = {"A=1", "X", "C=3", 0};
+
+            int main(void) {
+                // Close stderr on exec so parent can wait for the
+                // execve to finish.
+                if (fcntl(2, F_SETFD, FD_CLOEXEC) != 0)
+                    return 0;
+                return execve("/bin/cat", argv, envp);
+            }
+            """)
+        cexe = create_c_exe(self.get_testfn(), c_code=code)
+        sproc = self.spawn_testproc(
+            [cexe], stdin=subprocess.PIPE, stderr=subprocess.PIPE
+        )
+        p = psutil.Process(sproc.pid)
+        wait_for_pid(p.pid)
+        assert p.is_running()
+        # Wait for process to exec or exit.
+        assert sproc.stderr.read() == b""
+        if MACOS and CI_TESTING:
+            try:
+                env = p.environ()
+            except psutil.AccessDenied:
+                # XXX: fails sometimes with:
+                # PermissionError from 'sysctl(KERN_PROCARGS2) -> EIO'
+                return
+        else:
+            env = p.environ()
+        assert env == {"A": "1", "C": "3"}
+        sproc.communicate()
+        assert sproc.returncode == 0
+
+
+# ===================================================================
+# --- psutil.Popen tests
+# ===================================================================
+
+
+class TestPopen(PsutilTestCase):
+    """Tests for psutil.Popen class."""
+
+    @classmethod
+    def tearDownClass(cls):
+        reap_children()
+
+    def test_misc(self):
+        # XXX this test causes a ResourceWarning because
+        # psutil.__subproc instance doesn't get properly freed.
+        # Not sure what to do though.
+        cmd = [
+            PYTHON_EXE,
+            "-c",
+            "import time; [time.sleep(0.1) for x in range(100)];",
+        ]
+        with psutil.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            env=PYTHON_EXE_ENV,
+        ) as proc:
+            proc.name()
+            proc.cpu_times()
+            proc.stdin  # noqa: B018
+            assert dir(proc)
+            with pytest.raises(AttributeError):
+                proc.foo  # noqa: B018
+            proc.terminate()
+        if POSIX:
+            assert proc.wait(5) == -signal.SIGTERM
+        else:
+            assert proc.wait(5) == signal.SIGTERM
+
+    def test_ctx_manager(self):
+        with psutil.Popen(
+            [PYTHON_EXE, "-V"],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            stdin=subprocess.PIPE,
+            env=PYTHON_EXE_ENV,
+        ) as proc:
+            proc.communicate()
+        assert proc.stdout.closed
+        assert proc.stderr.closed
+        assert proc.stdin.closed
+        assert proc.returncode == 0
+
+    def test_kill_terminate(self):
+        # subprocess.Popen()'s terminate(), kill() and send_signal() do
+        # not raise exception after the process is gone. psutil.Popen
+        # diverges from that.
+        cmd = [
+            PYTHON_EXE,
+            "-c",
+            "import time; [time.sleep(0.1) for x in range(100)];",
+        ]
+        with psutil.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            env=PYTHON_EXE_ENV,
+        ) as proc:
+            proc.terminate()
+            proc.wait()
+            with pytest.raises(psutil.NoSuchProcess):
+                proc.terminate()
+            with pytest.raises(psutil.NoSuchProcess):
+                proc.kill()
+            with pytest.raises(psutil.NoSuchProcess):
+                proc.send_signal(signal.SIGTERM)
+            if WINDOWS:
+                with pytest.raises(psutil.NoSuchProcess):
+                    proc.send_signal(signal.CTRL_C_EVENT)
+                with pytest.raises(psutil.NoSuchProcess):
+                    proc.send_signal(signal.CTRL_BREAK_EVENT)
+
+    def test__getattribute__(self):
+        cmd = [
+            PYTHON_EXE,
+            "-c",
+            "import time; [time.sleep(0.1) for x in range(100)];",
+        ]
+        with psutil.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            env=PYTHON_EXE_ENV,
+        ) as proc:
+            proc.terminate()
+            proc.wait()
+            with pytest.raises(AttributeError):
+                proc.foo  # noqa: B018
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_process_all.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_process_all.py
new file mode 100644
index 0000000000000000000000000000000000000000..aaa3fa01dfd774f163ce0cbc9d358fef2b3b3bd1
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_process_all.py
@@ -0,0 +1,535 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Iterate over all process PIDs and for each one of them invoke and
+test all psutil.Process() methods.
+"""
+
+import enum
+import errno
+import multiprocessing
+import os
+import stat
+import time
+import traceback
+
+import psutil
+from psutil import AIX
+from psutil import BSD
+from psutil import FREEBSD
+from psutil import LINUX
+from psutil import MACOS
+from psutil import NETBSD
+from psutil import OPENBSD
+from psutil import OSX
+from psutil import POSIX
+from psutil import WINDOWS
+from psutil.tests import CI_TESTING
+from psutil.tests import PYTEST_PARALLEL
+from psutil.tests import VALID_PROC_STATUSES
+from psutil.tests import PsutilTestCase
+from psutil.tests import check_connection_ntuple
+from psutil.tests import create_sockets
+from psutil.tests import is_namedtuple
+from psutil.tests import is_win_secure_system_proc
+from psutil.tests import process_namespace
+from psutil.tests import pytest
+
+
+# Cuts the time in half, but (e.g.) on macOS the process pool stays
+# alive after join() (multiprocessing bug?), messing up other tests.
+USE_PROC_POOL = LINUX and not CI_TESTING and not PYTEST_PARALLEL
+
+
+def proc_info(pid):
+    tcase = PsutilTestCase()
+
+    def check_exception(exc, proc, name, ppid):
+        tcase.assertEqual(exc.pid, pid)
+        if exc.name is not None:
+            tcase.assertEqual(exc.name, name)
+        if isinstance(exc, psutil.ZombieProcess):
+            tcase.assertProcessZombie(proc)
+            if exc.ppid is not None:
+                tcase.assertGreaterEqual(exc.ppid, 0)
+                tcase.assertEqual(exc.ppid, ppid)
+        elif isinstance(exc, psutil.NoSuchProcess):
+            tcase.assertProcessGone(proc)
+        str(exc)
+        repr(exc)
+
+    def do_wait():
+        if pid != 0:
+            try:
+                proc.wait(0)
+            except psutil.Error as exc:
+                check_exception(exc, proc, name, ppid)
+
+    try:
+        proc = psutil.Process(pid)
+    except psutil.NoSuchProcess:
+        tcase.assertPidGone(pid)
+        return {}
+    try:
+        d = proc.as_dict(['ppid', 'name'])
+    except psutil.NoSuchProcess:
+        tcase.assertProcessGone(proc)
+    else:
+        name, ppid = d['name'], d['ppid']
+        info = {'pid': proc.pid}
+        ns = process_namespace(proc)
+        # We don't use oneshot() because in order not to fool
+        # check_exception() in case of NSP.
+        for fun, fun_name in ns.iter(ns.getters, clear_cache=False):
+            try:
+                info[fun_name] = fun()
+            except psutil.Error as exc:
+                check_exception(exc, proc, name, ppid)
+                continue
+        do_wait()
+        return info
+
+
+class TestFetchAllProcesses(PsutilTestCase):
+    """Test which iterates over all running processes and performs
+    some sanity checks against Process API's returned values.
+    Uses a process pool to get info about all processes.
+    """
+
+    def setUp(self):
+        psutil._set_debug(False)
+        # Using a pool in a CI env may result in deadlock, see:
+        # https://github.com/giampaolo/psutil/issues/2104
+        if USE_PROC_POOL:
+            self.pool = multiprocessing.Pool()
+
+    def tearDown(self):
+        psutil._set_debug(True)
+        if USE_PROC_POOL:
+            self.pool.terminate()
+            self.pool.join()
+
+    def iter_proc_info(self):
+        # Fixes "can't pickle <function proc_info>: it's not the
+        # same object as test_process_all.proc_info".
+        from psutil.tests.test_process_all import proc_info
+
+        if USE_PROC_POOL:
+            return self.pool.imap_unordered(proc_info, psutil.pids())
+        else:
+            ls = [proc_info(pid) for pid in psutil.pids()]
+            return ls
+
+    def test_all(self):
+        failures = []
+        for info in self.iter_proc_info():
+            for name, value in info.items():
+                meth = getattr(self, name)
+                try:
+                    meth(value, info)
+                except Exception:  # noqa: BLE001
+                    s = '\n' + '=' * 70 + '\n'
+                    s += (
+                        "FAIL: name=test_{}, pid={}, ret={}\ninfo={}\n".format(
+                            name,
+                            info['pid'],
+                            repr(value),
+                            info,
+                        )
+                    )
+                    s += '-' * 70
+                    s += f"\n{traceback.format_exc()}"
+                    s = "\n".join((" " * 4) + i for i in s.splitlines()) + "\n"
+                    failures.append(s)
+                else:
+                    if value not in (0, 0.0, [], None, '', {}):
+                        assert value, value
+        if failures:
+            raise self.fail(''.join(failures))
+
+    def cmdline(self, ret, info):
+        assert isinstance(ret, list)
+        for part in ret:
+            assert isinstance(part, str)
+
+    def exe(self, ret, info):
+        assert isinstance(ret, str)
+        assert ret.strip() == ret
+        if ret:
+            if WINDOWS and not ret.endswith('.exe'):
+                return  # May be "Registry", "MemCompression", ...
+            assert os.path.isabs(ret), ret
+            # Note: os.stat() may return False even if the file is there
+            # hence we skip the test, see:
+            # http://stackoverflow.com/questions/3112546/os-path-exists-lies
+            if POSIX and os.path.isfile(ret):
+                if hasattr(os, 'access') and hasattr(os, "X_OK"):
+                    # XXX: may fail on MACOS
+                    try:
+                        assert os.access(ret, os.X_OK)
+                    except AssertionError:
+                        if os.path.exists(ret) and not CI_TESTING:
+                            raise
+
+    def pid(self, ret, info):
+        assert isinstance(ret, int)
+        assert ret >= 0
+
+    def ppid(self, ret, info):
+        assert isinstance(ret, int)
+        assert ret >= 0
+        proc_info(ret)
+
+    def name(self, ret, info):
+        assert isinstance(ret, str)
+        if WINDOWS and not ret and is_win_secure_system_proc(info['pid']):
+            # https://github.com/giampaolo/psutil/issues/2338
+            return
+        # on AIX, "<exiting>" processes don't have names
+        if not AIX:
+            assert ret, repr(ret)
+
+    def create_time(self, ret, info):
+        assert isinstance(ret, float)
+        try:
+            assert ret >= 0
+        except AssertionError:
+            # XXX
+            if OPENBSD and info['status'] == psutil.STATUS_ZOMBIE:
+                pass
+            else:
+                raise
+        # this can't be taken for granted on all platforms
+        # self.assertGreaterEqual(ret, psutil.boot_time())
+        # make sure returned value can be pretty printed
+        # with strftime
+        time.strftime("%Y %m %d %H:%M:%S", time.localtime(ret))
+
+    def uids(self, ret, info):
+        assert is_namedtuple(ret)
+        for uid in ret:
+            assert isinstance(uid, int)
+            assert uid >= 0
+
+    def gids(self, ret, info):
+        assert is_namedtuple(ret)
+        # note: testing all gids as above seems not to be reliable for
+        # gid == 30 (nodoby); not sure why.
+        for gid in ret:
+            assert isinstance(gid, int)
+            if not MACOS and not NETBSD:
+                assert gid >= 0
+
+    def username(self, ret, info):
+        assert isinstance(ret, str)
+        assert ret.strip() == ret
+        assert ret.strip()
+
+    def status(self, ret, info):
+        assert isinstance(ret, str)
+        assert ret, ret
+        assert ret != '?'  # XXX
+        assert ret in VALID_PROC_STATUSES
+
+    def io_counters(self, ret, info):
+        assert is_namedtuple(ret)
+        for field in ret:
+            assert isinstance(field, int)
+            if field != -1:
+                assert field >= 0
+
+    def ionice(self, ret, info):
+        if LINUX:
+            assert isinstance(ret.ioclass, int)
+            assert isinstance(ret.value, int)
+            assert ret.ioclass >= 0
+            assert ret.value >= 0
+        else:  # Windows, Cygwin
+            choices = [
+                psutil.IOPRIO_VERYLOW,
+                psutil.IOPRIO_LOW,
+                psutil.IOPRIO_NORMAL,
+                psutil.IOPRIO_HIGH,
+            ]
+            assert isinstance(ret, int)
+            assert ret >= 0
+            assert ret in choices
+
+    def num_threads(self, ret, info):
+        assert isinstance(ret, int)
+        if WINDOWS and ret == 0 and is_win_secure_system_proc(info['pid']):
+            # https://github.com/giampaolo/psutil/issues/2338
+            return
+        assert ret >= 1
+
+    def threads(self, ret, info):
+        assert isinstance(ret, list)
+        for t in ret:
+            assert is_namedtuple(t)
+            assert t.id >= 0
+            assert t.user_time >= 0
+            assert t.system_time >= 0
+            for field in t:
+                assert isinstance(field, (int, float))
+
+    def cpu_times(self, ret, info):
+        assert is_namedtuple(ret)
+        for n in ret:
+            assert isinstance(n, float)
+            assert n >= 0
+        # TODO: check ntuple fields
+
+    def cpu_percent(self, ret, info):
+        assert isinstance(ret, float)
+        assert 0.0 <= ret <= 100.0, ret
+
+    def cpu_num(self, ret, info):
+        assert isinstance(ret, int)
+        if FREEBSD and ret == -1:
+            return
+        assert ret >= 0
+        if psutil.cpu_count() == 1:
+            assert ret == 0
+        assert ret in list(range(psutil.cpu_count()))
+
+    def memory_info(self, ret, info):
+        assert is_namedtuple(ret)
+        for value in ret:
+            assert isinstance(value, int)
+            assert value >= 0
+        if WINDOWS:
+            assert ret.peak_wset >= ret.wset
+            assert ret.peak_paged_pool >= ret.paged_pool
+            assert ret.peak_nonpaged_pool >= ret.nonpaged_pool
+            assert ret.peak_pagefile >= ret.pagefile
+
+    def memory_full_info(self, ret, info):
+        assert is_namedtuple(ret)
+        total = psutil.virtual_memory().total
+        for name in ret._fields:
+            value = getattr(ret, name)
+            assert isinstance(value, int)
+            assert value >= 0
+            if LINUX or (OSX and name in {'vms', 'data'}):
+                # On Linux there are processes (e.g. 'goa-daemon') whose
+                # VMS is incredibly high for some reason.
+                continue
+            assert value <= total, name
+
+        if LINUX:
+            assert ret.pss >= ret.uss
+
+    def open_files(self, ret, info):
+        assert isinstance(ret, list)
+        for f in ret:
+            assert isinstance(f.fd, int)
+            assert isinstance(f.path, str)
+            assert f.path.strip() == f.path
+            if WINDOWS:
+                assert f.fd == -1
+            elif LINUX:
+                assert isinstance(f.position, int)
+                assert isinstance(f.mode, str)
+                assert isinstance(f.flags, int)
+                assert f.position >= 0
+                assert f.mode in {'r', 'w', 'a', 'r+', 'a+'}
+                assert f.flags > 0
+            elif BSD and not f.path:
+                # XXX see: https://github.com/giampaolo/psutil/issues/595
+                continue
+            assert os.path.isabs(f.path), f
+            try:
+                st = os.stat(f.path)
+            except FileNotFoundError:
+                pass
+            else:
+                assert stat.S_ISREG(st.st_mode), f
+
+    def num_fds(self, ret, info):
+        assert isinstance(ret, int)
+        assert ret >= 0
+
+    def net_connections(self, ret, info):
+        with create_sockets():
+            assert len(ret) == len(set(ret))
+            for conn in ret:
+                assert is_namedtuple(conn)
+                check_connection_ntuple(conn)
+
+    def cwd(self, ret, info):
+        assert isinstance(ret, str)
+        assert ret.strip() == ret
+        if ret:
+            assert os.path.isabs(ret), ret
+            try:
+                st = os.stat(ret)
+            except OSError as err:
+                if WINDOWS and psutil._psplatform.is_permission_err(err):
+                    pass
+                # directory has been removed in mean time
+                elif err.errno != errno.ENOENT:
+                    raise
+            else:
+                assert stat.S_ISDIR(st.st_mode)
+
+    def memory_percent(self, ret, info):
+        assert isinstance(ret, float)
+        assert 0 <= ret <= 100, ret
+
+    def is_running(self, ret, info):
+        assert isinstance(ret, bool)
+
+    def cpu_affinity(self, ret, info):
+        assert isinstance(ret, list)
+        assert ret != []
+        cpus = list(range(psutil.cpu_count()))
+        for n in ret:
+            assert isinstance(n, int)
+            assert n in cpus
+
+    def terminal(self, ret, info):
+        assert isinstance(ret, (str, type(None)))
+        if ret is not None:
+            assert os.path.isabs(ret), ret
+            assert os.path.exists(ret), ret
+
+    def memory_maps(self, ret, info):
+        for nt in ret:
+            assert isinstance(nt.addr, str)
+            assert isinstance(nt.perms, str)
+            assert isinstance(nt.path, str)
+            for fname in nt._fields:
+                value = getattr(nt, fname)
+                if fname == 'path':
+                    if value.startswith(("[", "anon_inode:")):  # linux
+                        continue
+                    if BSD and value == "pvclock":  # seen on FreeBSD
+                        continue
+                    assert os.path.isabs(nt.path), nt.path
+                    # commented as on Linux we might get
+                    # '/foo/bar (deleted)'
+                    # assert os.path.exists(nt.path), nt.path
+                elif fname == 'addr':
+                    assert value, repr(value)
+                elif fname == 'perms':
+                    if not WINDOWS:
+                        assert value, repr(value)
+                else:
+                    assert isinstance(value, int)
+                    assert value >= 0
+
+    def num_handles(self, ret, info):
+        assert isinstance(ret, int)
+        assert ret >= 0
+
+    def nice(self, ret, info):
+        assert isinstance(ret, int)
+        if POSIX:
+            assert -20 <= ret <= 20, ret
+        else:
+            priorities = [
+                getattr(psutil, x)
+                for x in dir(psutil)
+                if x.endswith('_PRIORITY_CLASS')
+            ]
+            assert ret in priorities
+            assert isinstance(ret, enum.IntEnum)
+
+    def num_ctx_switches(self, ret, info):
+        assert is_namedtuple(ret)
+        for value in ret:
+            assert isinstance(value, int)
+            assert value >= 0
+
+    def rlimit(self, ret, info):
+        assert isinstance(ret, tuple)
+        assert len(ret) == 2
+        assert ret[0] >= -1
+        assert ret[1] >= -1
+
+    def environ(self, ret, info):
+        assert isinstance(ret, dict)
+        for k, v in ret.items():
+            assert isinstance(k, str)
+            assert isinstance(v, str)
+
+
+class TestPidsRange(PsutilTestCase):
+    """Given pid_exists() return value for a range of PIDs which may or
+    may not exist, make sure that psutil.Process() and psutil.pids()
+    agree with pid_exists(). This guarantees that the 3 APIs are all
+    consistent with each other. See:
+    https://github.com/giampaolo/psutil/issues/2359
+
+    XXX - Note about Windows: it turns out there are some "hidden" PIDs
+    which are not returned by psutil.pids() and are also not revealed
+    by taskmgr.exe and ProcessHacker, still they can be instantiated by
+    psutil.Process() and queried. One of such PIDs is "conhost.exe".
+    Running as_dict() for it reveals that some Process() APIs
+    erroneously raise NoSuchProcess, so we know we have problem there.
+    Let's ignore this for now, since it's quite a corner case (who even
+    imagined hidden PIDs existed on Windows?).
+    """
+
+    def setUp(self):
+        psutil._set_debug(False)
+
+    def tearDown(self):
+        psutil._set_debug(True)
+
+    def test_it(self):
+        def is_linux_tid(pid):
+            try:
+                f = open(f"/proc/{pid}/status", "rb")  # noqa: SIM115
+            except FileNotFoundError:
+                return False
+            else:
+                with f:
+                    for line in f:
+                        if line.startswith(b"Tgid:"):
+                            tgid = int(line.split()[1])
+                            # If tgid and pid are different then we're
+                            # dealing with a process TID.
+                            return tgid != pid
+                    raise ValueError("'Tgid' line not found")
+
+        def check(pid):
+            # In case of failure retry up to 3 times in order to avoid
+            # race conditions, especially when running in a CI
+            # environment where PIDs may appear and disappear at any
+            # time.
+            x = 3
+            while True:
+                exists = psutil.pid_exists(pid)
+                try:
+                    if exists:
+                        psutil.Process(pid)
+                        if not WINDOWS:  # see docstring
+                            assert pid in psutil.pids()
+                    else:
+                        # On OpenBSD thread IDs can be instantiated,
+                        # and oneshot() succeeds, but other APIs fail
+                        # with EINVAL.
+                        if not OPENBSD:
+                            with pytest.raises(psutil.NoSuchProcess):
+                                psutil.Process(pid)
+                        if not WINDOWS:  # see docstring
+                            assert pid not in psutil.pids()
+                except (psutil.Error, AssertionError):
+                    x -= 1
+                    if x == 0:
+                        raise
+                else:
+                    return
+
+        for pid in range(1, 3000):
+            if LINUX and is_linux_tid(pid):
+                # On Linux a TID (thread ID) can be passed to the
+                # Process class and is querable like a PID (process
+                # ID). Skip it.
+                continue
+            with self.subTest(pid=pid):
+                check(pid)
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_scripts.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..de0ad2af747cd1daec63f4cc5e1320e855cfcfad
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_scripts.py
@@ -0,0 +1,240 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Test various scripts."""
+
+import ast
+import os
+import shutil
+import stat
+import subprocess
+
+import pytest
+
+from psutil import POSIX
+from psutil import WINDOWS
+from psutil.tests import CI_TESTING
+from psutil.tests import HAS_BATTERY
+from psutil.tests import HAS_MEMORY_MAPS
+from psutil.tests import HAS_SENSORS_BATTERY
+from psutil.tests import HAS_SENSORS_FANS
+from psutil.tests import HAS_SENSORS_TEMPERATURES
+from psutil.tests import PYTHON_EXE
+from psutil.tests import PYTHON_EXE_ENV
+from psutil.tests import ROOT_DIR
+from psutil.tests import SCRIPTS_DIR
+from psutil.tests import PsutilTestCase
+from psutil.tests import import_module_by_path
+from psutil.tests import psutil
+from psutil.tests import sh
+
+
+INTERNAL_SCRIPTS_DIR = os.path.join(SCRIPTS_DIR, "internal")
+SETUP_PY = os.path.join(ROOT_DIR, 'setup.py')
+
+
+# ===================================================================
+# --- Tests scripts in scripts/ directory
+# ===================================================================
+
+
+@pytest.mark.skipif(
+    CI_TESTING and not os.path.exists(SCRIPTS_DIR),
+    reason="can't find scripts/ directory",
+)
+class TestExampleScripts(PsutilTestCase):
+    @staticmethod
+    def assert_stdout(exe, *args, **kwargs):
+        kwargs.setdefault("env", PYTHON_EXE_ENV)
+        exe = os.path.join(SCRIPTS_DIR, exe)
+        cmd = [PYTHON_EXE, exe]
+        for arg in args:
+            cmd.append(arg)
+        try:
+            out = sh(cmd, **kwargs).strip()
+        except RuntimeError as err:
+            if 'AccessDenied' in str(err):
+                return str(err)
+            else:
+                raise
+        assert out, out
+        return out
+
+    @staticmethod
+    def assert_syntax(exe):
+        exe = os.path.join(SCRIPTS_DIR, exe)
+        with open(exe, encoding="utf8") as f:
+            src = f.read()
+        ast.parse(src)
+
+    def test_coverage(self):
+        # make sure all example scripts have a test method defined
+        meths = dir(self)
+        for name in os.listdir(SCRIPTS_DIR):
+            if name.endswith('.py'):
+                if 'test_' + os.path.splitext(name)[0] not in meths:
+                    # self.assert_stdout(name)
+                    raise self.fail(
+                        "no test defined for"
+                        f" {os.path.join(SCRIPTS_DIR, name)!r} script"
+                    )
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_executable(self):
+        for root, dirs, files in os.walk(SCRIPTS_DIR):
+            for file in files:
+                if file.endswith('.py'):
+                    path = os.path.join(root, file)
+                    if not stat.S_IXUSR & os.stat(path)[stat.ST_MODE]:
+                        raise self.fail(f"{path!r} is not executable")
+
+    def test_disk_usage(self):
+        self.assert_stdout('disk_usage.py')
+
+    def test_free(self):
+        self.assert_stdout('free.py')
+
+    def test_meminfo(self):
+        self.assert_stdout('meminfo.py')
+
+    def test_procinfo(self):
+        self.assert_stdout('procinfo.py', str(os.getpid()))
+
+    @pytest.mark.skipif(CI_TESTING and not psutil.users(), reason="no users")
+    def test_who(self):
+        self.assert_stdout('who.py')
+
+    def test_ps(self):
+        self.assert_stdout('ps.py')
+
+    def test_pstree(self):
+        self.assert_stdout('pstree.py')
+
+    def test_netstat(self):
+        self.assert_stdout('netstat.py')
+
+    def test_ifconfig(self):
+        self.assert_stdout('ifconfig.py')
+
+    @pytest.mark.skipif(not HAS_MEMORY_MAPS, reason="not supported")
+    def test_pmap(self):
+        self.assert_stdout('pmap.py', str(os.getpid()))
+
+    def test_procsmem(self):
+        if 'uss' not in psutil.Process().memory_full_info()._fields:
+            raise pytest.skip("not supported")
+        self.assert_stdout('procsmem.py')
+
+    def test_killall(self):
+        self.assert_syntax('killall.py')
+
+    def test_nettop(self):
+        self.assert_syntax('nettop.py')
+
+    def test_top(self):
+        self.assert_syntax('top.py')
+
+    def test_iotop(self):
+        self.assert_syntax('iotop.py')
+
+    def test_pidof(self):
+        output = self.assert_stdout('pidof.py', psutil.Process().name())
+        assert str(os.getpid()) in output
+
+    @pytest.mark.skipif(not WINDOWS, reason="WINDOWS only")
+    def test_winservices(self):
+        self.assert_stdout('winservices.py')
+
+    def test_cpu_distribution(self):
+        self.assert_syntax('cpu_distribution.py')
+
+    @pytest.mark.skipif(not HAS_SENSORS_TEMPERATURES, reason="not supported")
+    def test_temperatures(self):
+        if not psutil.sensors_temperatures():
+            raise pytest.skip("no temperatures")
+        self.assert_stdout('temperatures.py')
+
+    @pytest.mark.skipif(not HAS_SENSORS_FANS, reason="not supported")
+    def test_fans(self):
+        if not psutil.sensors_fans():
+            raise pytest.skip("no fans")
+        self.assert_stdout('fans.py')
+
+    @pytest.mark.skipif(not HAS_SENSORS_BATTERY, reason="not supported")
+    @pytest.mark.skipif(not HAS_BATTERY, reason="no battery")
+    def test_battery(self):
+        self.assert_stdout('battery.py')
+
+    @pytest.mark.skipif(not HAS_SENSORS_BATTERY, reason="not supported")
+    @pytest.mark.skipif(not HAS_BATTERY, reason="no battery")
+    def test_sensors(self):
+        self.assert_stdout('sensors.py')
+
+
+# ===================================================================
+# --- Tests scripts in scripts/internal/ directory
+# ===================================================================
+
+
+@pytest.mark.skipif(
+    CI_TESTING and not os.path.exists(INTERNAL_SCRIPTS_DIR),
+    reason="can't find scripts/internal/ directory",
+)
+class TestInternalScripts(PsutilTestCase):
+    @staticmethod
+    def ls():
+        for name in os.listdir(INTERNAL_SCRIPTS_DIR):
+            if name.endswith(".py"):
+                yield os.path.join(INTERNAL_SCRIPTS_DIR, name)
+
+    def test_syntax_all(self):
+        for path in self.ls():
+            with open(path, encoding="utf8") as f:
+                data = f.read()
+            ast.parse(data)
+
+    @pytest.mark.skipif(CI_TESTING, reason="not on CI")
+    def test_import_all(self):
+        for path in self.ls():
+            try:
+                import_module_by_path(path)
+            except SystemExit:
+                pass
+
+
+# ===================================================================
+# --- Tests for setup.py script
+# ===================================================================
+
+
+@pytest.mark.skipif(
+    CI_TESTING and not os.path.exists(SETUP_PY), reason="can't find setup.py"
+)
+class TestSetupScript(PsutilTestCase):
+    def test_invocation(self):
+        module = import_module_by_path(SETUP_PY)
+        with pytest.raises(SystemExit):
+            module.setup()
+        assert module.get_version() == psutil.__version__
+
+    @pytest.mark.skipif(
+        not shutil.which("python2.7"), reason="python2.7 not installed"
+    )
+    def test_python2(self):
+        # There's a duplicate of this test in scripts/internal
+        # directory, which is only executed by CI. We replicate it here
+        # to run it when developing locally.
+        p = subprocess.Popen(
+            [shutil.which("python2.7"), SETUP_PY],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            universal_newlines=True,
+        )
+        stdout, stderr = p.communicate()
+        assert p.wait() == 1
+        assert not stdout
+        assert "psutil no longer supports Python 2.7" in stderr
+        assert "Latest version supporting Python 2.7 is" in stderr
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_sunos.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_sunos.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5d9d353b94e60f537b967b7a9fbcfc13cf5bf65
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_sunos.py
@@ -0,0 +1,39 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Sun OS specific tests."""
+
+import os
+
+import psutil
+from psutil import SUNOS
+from psutil.tests import PsutilTestCase
+from psutil.tests import pytest
+from psutil.tests import sh
+
+
+@pytest.mark.skipif(not SUNOS, reason="SUNOS only")
+class SunOSSpecificTestCase(PsutilTestCase):
+    def test_swap_memory(self):
+        out = sh(f"env PATH=/usr/sbin:/sbin:{os.environ['PATH']} swap -l")
+        lines = out.strip().split('\n')[1:]
+        if not lines:
+            raise ValueError('no swap device(s) configured')
+        total = free = 0
+        for line in lines:
+            fields = line.split()
+            total = int(fields[3]) * 512
+            free = int(fields[4]) * 512
+        used = total - free
+
+        psutil_swap = psutil.swap_memory()
+        assert psutil_swap.total == total
+        assert psutil_swap.used == used
+        assert psutil_swap.free == free
+
+    def test_cpu_count(self):
+        out = sh("/usr/sbin/psrinfo")
+        assert psutil.cpu_count() == len(out.split('\n'))
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_system.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_system.py
new file mode 100644
index 0000000000000000000000000000000000000000..b961e1f89178932ce99ea3a6cc1acfa745bfa280
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_system.py
@@ -0,0 +1,979 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Tests for system APIS."""
+
+import datetime
+import enum
+import errno
+import os
+import platform
+import pprint
+import shutil
+import signal
+import socket
+import sys
+import time
+from unittest import mock
+
+import psutil
+from psutil import AIX
+from psutil import BSD
+from psutil import FREEBSD
+from psutil import LINUX
+from psutil import MACOS
+from psutil import NETBSD
+from psutil import OPENBSD
+from psutil import POSIX
+from psutil import SUNOS
+from psutil import WINDOWS
+from psutil._common import broadcast_addr
+from psutil.tests import AARCH64
+from psutil.tests import ASCII_FS
+from psutil.tests import CI_TESTING
+from psutil.tests import GITHUB_ACTIONS
+from psutil.tests import GLOBAL_TIMEOUT
+from psutil.tests import HAS_BATTERY
+from psutil.tests import HAS_CPU_FREQ
+from psutil.tests import HAS_GETLOADAVG
+from psutil.tests import HAS_NET_IO_COUNTERS
+from psutil.tests import HAS_SENSORS_BATTERY
+from psutil.tests import HAS_SENSORS_FANS
+from psutil.tests import HAS_SENSORS_TEMPERATURES
+from psutil.tests import IS_64BIT
+from psutil.tests import MACOS_12PLUS
+from psutil.tests import PYPY
+from psutil.tests import UNICODE_SUFFIX
+from psutil.tests import PsutilTestCase
+from psutil.tests import check_net_address
+from psutil.tests import pytest
+from psutil.tests import retry_on_failure
+
+
+# ===================================================================
+# --- System-related API tests
+# ===================================================================
+
+
+class TestProcessIter(PsutilTestCase):
+    def test_pid_presence(self):
+        assert os.getpid() in [x.pid for x in psutil.process_iter()]
+        sproc = self.spawn_testproc()
+        assert sproc.pid in [x.pid for x in psutil.process_iter()]
+        p = psutil.Process(sproc.pid)
+        p.kill()
+        p.wait()
+        assert sproc.pid not in [x.pid for x in psutil.process_iter()]
+
+    def test_no_duplicates(self):
+        ls = list(psutil.process_iter())
+        assert sorted(ls, key=lambda x: x.pid) == sorted(
+            set(ls), key=lambda x: x.pid
+        )
+
+    def test_emulate_nsp(self):
+        list(psutil.process_iter())  # populate cache
+        for x in range(2):
+            with mock.patch(
+                'psutil.Process.as_dict',
+                side_effect=psutil.NoSuchProcess(os.getpid()),
+            ):
+                assert not list(psutil.process_iter(attrs=["cpu_times"]))
+            psutil.process_iter.cache_clear()  # repeat test without cache
+
+    def test_emulate_access_denied(self):
+        list(psutil.process_iter())  # populate cache
+        for x in range(2):
+            with mock.patch(
+                'psutil.Process.as_dict',
+                side_effect=psutil.AccessDenied(os.getpid()),
+            ):
+                with pytest.raises(psutil.AccessDenied):
+                    list(psutil.process_iter(attrs=["cpu_times"]))
+            psutil.process_iter.cache_clear()  # repeat test without cache
+
+    def test_attrs(self):
+        for p in psutil.process_iter(attrs=['pid']):
+            assert list(p.info.keys()) == ['pid']
+        # yield again
+        for p in psutil.process_iter(attrs=['pid']):
+            assert list(p.info.keys()) == ['pid']
+        with pytest.raises(ValueError):
+            list(psutil.process_iter(attrs=['foo']))
+        with mock.patch(
+            "psutil._psplatform.Process.cpu_times",
+            side_effect=psutil.AccessDenied(0, ""),
+        ) as m:
+            for p in psutil.process_iter(attrs=["pid", "cpu_times"]):
+                assert p.info['cpu_times'] is None
+                assert p.info['pid'] >= 0
+            assert m.called
+        with mock.patch(
+            "psutil._psplatform.Process.cpu_times",
+            side_effect=psutil.AccessDenied(0, ""),
+        ) as m:
+            flag = object()
+            for p in psutil.process_iter(
+                attrs=["pid", "cpu_times"], ad_value=flag
+            ):
+                assert p.info['cpu_times'] is flag
+                assert p.info['pid'] >= 0
+            assert m.called
+
+    def test_cache_clear(self):
+        list(psutil.process_iter())  # populate cache
+        assert psutil._pmap
+        psutil.process_iter.cache_clear()
+        assert not psutil._pmap
+
+
+class TestProcessAPIs(PsutilTestCase):
+    @pytest.mark.skipif(
+        PYPY and WINDOWS,
+        reason="spawn_testproc() unreliable on PYPY + WINDOWS",
+    )
+    def test_wait_procs(self):
+        def callback(p):
+            pids.append(p.pid)
+
+        pids = []
+        sproc1 = self.spawn_testproc()
+        sproc2 = self.spawn_testproc()
+        sproc3 = self.spawn_testproc()
+        procs = [psutil.Process(x.pid) for x in (sproc1, sproc2, sproc3)]
+        with pytest.raises(ValueError):
+            psutil.wait_procs(procs, timeout=-1)
+        with pytest.raises(TypeError):
+            psutil.wait_procs(procs, callback=1)
+        t = time.time()
+        gone, alive = psutil.wait_procs(procs, timeout=0.01, callback=callback)
+
+        assert time.time() - t < 0.5
+        assert not gone
+        assert len(alive) == 3
+        assert not pids
+        for p in alive:
+            assert not hasattr(p, 'returncode')
+
+        @retry_on_failure(30)
+        def test_1(procs, callback):
+            gone, alive = psutil.wait_procs(
+                procs, timeout=0.03, callback=callback
+            )
+            assert len(gone) == 1
+            assert len(alive) == 2
+            return gone, alive
+
+        sproc3.terminate()
+        gone, alive = test_1(procs, callback)
+        assert sproc3.pid in [x.pid for x in gone]
+        if POSIX:
+            assert gone.pop().returncode == -signal.SIGTERM
+        else:
+            assert gone.pop().returncode == 1
+        assert pids == [sproc3.pid]
+        for p in alive:
+            assert not hasattr(p, 'returncode')
+
+        @retry_on_failure(30)
+        def test_2(procs, callback):
+            gone, alive = psutil.wait_procs(
+                procs, timeout=0.03, callback=callback
+            )
+            assert len(gone) == 3
+            assert len(alive) == 0
+            return gone, alive
+
+        sproc1.terminate()
+        sproc2.terminate()
+        gone, alive = test_2(procs, callback)
+        assert set(pids) == {sproc1.pid, sproc2.pid, sproc3.pid}
+        for p in gone:
+            assert hasattr(p, 'returncode')
+
+    @pytest.mark.skipif(
+        PYPY and WINDOWS,
+        reason="spawn_testproc() unreliable on PYPY + WINDOWS",
+    )
+    def test_wait_procs_no_timeout(self):
+        sproc1 = self.spawn_testproc()
+        sproc2 = self.spawn_testproc()
+        sproc3 = self.spawn_testproc()
+        procs = [psutil.Process(x.pid) for x in (sproc1, sproc2, sproc3)]
+        for p in procs:
+            p.terminate()
+        psutil.wait_procs(procs)
+
+    def test_pid_exists(self):
+        sproc = self.spawn_testproc()
+        assert psutil.pid_exists(sproc.pid)
+        p = psutil.Process(sproc.pid)
+        p.kill()
+        p.wait()
+        assert not psutil.pid_exists(sproc.pid)
+        assert not psutil.pid_exists(-1)
+        assert psutil.pid_exists(0) == (0 in psutil.pids())
+
+    def test_pid_exists_2(self):
+        pids = psutil.pids()
+        for pid in pids:
+            try:
+                assert psutil.pid_exists(pid)
+            except AssertionError:
+                # in case the process disappeared in meantime fail only
+                # if it is no longer in psutil.pids()
+                time.sleep(0.1)
+                assert pid not in psutil.pids()
+        pids = range(max(pids) + 15000, max(pids) + 16000)
+        for pid in pids:
+            assert not psutil.pid_exists(pid)
+
+
+class TestMiscAPIs(PsutilTestCase):
+    def test_boot_time(self):
+        bt = psutil.boot_time()
+        assert isinstance(bt, float)
+        assert bt > 0
+        assert bt < time.time()
+
+    @pytest.mark.skipif(
+        CI_TESTING and not psutil.users(), reason="unreliable on CI"
+    )
+    def test_users(self):
+        users = psutil.users()
+        assert users
+        for user in users:
+            with self.subTest(user=user):
+                assert user.name
+                assert isinstance(user.name, str)
+                assert isinstance(user.terminal, (str, type(None)))
+                if user.host is not None:
+                    assert isinstance(user.host, (str, type(None)))
+                user.terminal  # noqa: B018
+                user.host  # noqa: B018
+                assert user.started > 0.0
+                datetime.datetime.fromtimestamp(user.started)
+                if WINDOWS or OPENBSD:
+                    assert user.pid is None
+                else:
+                    psutil.Process(user.pid)
+
+    def test_os_constants(self):
+        names = [
+            "POSIX",
+            "WINDOWS",
+            "LINUX",
+            "MACOS",
+            "FREEBSD",
+            "OPENBSD",
+            "NETBSD",
+            "BSD",
+            "SUNOS",
+        ]
+        for name in names:
+            assert isinstance(getattr(psutil, name), bool), name
+
+        if os.name == 'posix':
+            assert psutil.POSIX
+            assert not psutil.WINDOWS
+            names.remove("POSIX")
+            if "linux" in sys.platform.lower():
+                assert psutil.LINUX
+                names.remove("LINUX")
+            elif "bsd" in sys.platform.lower():
+                assert psutil.BSD
+                assert [psutil.FREEBSD, psutil.OPENBSD, psutil.NETBSD].count(
+                    True
+                ) == 1
+                names.remove("BSD")
+                names.remove("FREEBSD")
+                names.remove("OPENBSD")
+                names.remove("NETBSD")
+            elif (
+                "sunos" in sys.platform.lower()
+                or "solaris" in sys.platform.lower()
+            ):
+                assert psutil.SUNOS
+                names.remove("SUNOS")
+            elif "darwin" in sys.platform.lower():
+                assert psutil.MACOS
+                names.remove("MACOS")
+        else:
+            assert psutil.WINDOWS
+            assert not psutil.POSIX
+            names.remove("WINDOWS")
+
+        # assert all other constants are set to False
+        for name in names:
+            assert not getattr(psutil, name), name
+
+
+class TestMemoryAPIs(PsutilTestCase):
+    def test_virtual_memory(self):
+        mem = psutil.virtual_memory()
+        assert mem.total > 0, mem
+        assert mem.available > 0, mem
+        assert 0 <= mem.percent <= 100, mem
+        assert mem.used > 0, mem
+        assert mem.free >= 0, mem
+        for name in mem._fields:
+            value = getattr(mem, name)
+            if name != 'percent':
+                assert isinstance(value, int)
+            if name != 'total':
+                if not value >= 0:
+                    raise self.fail(f"{name!r} < 0 ({value})")
+                if value > mem.total:
+                    raise self.fail(
+                        f"{name!r} > total (total={mem.total}, {name}={value})"
+                    )
+
+    def test_swap_memory(self):
+        mem = psutil.swap_memory()
+        assert mem._fields == (
+            'total',
+            'used',
+            'free',
+            'percent',
+            'sin',
+            'sout',
+        )
+
+        assert mem.total >= 0, mem
+        assert mem.used >= 0, mem
+        if mem.total > 0:
+            # likely a system with no swap partition
+            assert mem.free > 0, mem
+        else:
+            assert mem.free == 0, mem
+        assert 0 <= mem.percent <= 100, mem
+        assert mem.sin >= 0, mem
+        assert mem.sout >= 0, mem
+
+
+class TestCpuAPIs(PsutilTestCase):
+    def test_cpu_count_logical(self):
+        logical = psutil.cpu_count()
+        assert logical is not None
+        assert logical == len(psutil.cpu_times(percpu=True))
+        assert logical >= 1
+
+        if os.path.exists("/proc/cpuinfo"):
+            with open("/proc/cpuinfo") as fd:
+                cpuinfo_data = fd.read()
+            if "physical id" not in cpuinfo_data:
+                raise pytest.skip("cpuinfo doesn't include physical id")
+
+    def test_cpu_count_cores(self):
+        logical = psutil.cpu_count()
+        cores = psutil.cpu_count(logical=False)
+        if cores is None:
+            raise pytest.skip("cpu_count_cores() is None")
+        if WINDOWS and sys.getwindowsversion()[:2] <= (6, 1):  # <= Vista
+            assert cores is None
+        else:
+            assert cores >= 1
+            assert logical >= cores
+
+    def test_cpu_count_none(self):
+        # https://github.com/giampaolo/psutil/issues/1085
+        for val in (-1, 0, None):
+            with mock.patch(
+                'psutil._psplatform.cpu_count_logical', return_value=val
+            ) as m:
+                assert psutil.cpu_count() is None
+                assert m.called
+            with mock.patch(
+                'psutil._psplatform.cpu_count_cores', return_value=val
+            ) as m:
+                assert psutil.cpu_count(logical=False) is None
+                assert m.called
+
+    def test_cpu_times(self):
+        # Check type, value >= 0, str().
+        total = 0
+        times = psutil.cpu_times()
+        sum(times)
+        for cp_time in times:
+            assert isinstance(cp_time, float)
+            assert cp_time >= 0.0
+            total += cp_time
+        assert round(abs(total - sum(times)), 6) == 0
+        str(times)
+        # CPU times are always supposed to increase over time
+        # or at least remain the same and that's because time
+        # cannot go backwards.
+        # Surprisingly sometimes this might not be the case (at
+        # least on Windows and Linux), see:
+        # https://github.com/giampaolo/psutil/issues/392
+        # https://github.com/giampaolo/psutil/issues/645
+        # if not WINDOWS:
+        #     last = psutil.cpu_times()
+        #     for x in range(100):
+        #         new = psutil.cpu_times()
+        #         for field in new._fields:
+        #             new_t = getattr(new, field)
+        #             last_t = getattr(last, field)
+        #             self.assertGreaterEqual(
+        #                 new_t, last_t,
+        #                 msg="{} {}".format(new_t, last_t))
+        #         last = new
+
+    def test_cpu_times_time_increases(self):
+        # Make sure time increases between calls.
+        t1 = sum(psutil.cpu_times())
+        stop_at = time.time() + GLOBAL_TIMEOUT
+        while time.time() < stop_at:
+            t2 = sum(psutil.cpu_times())
+            if t2 > t1:
+                return
+        raise self.fail("time remained the same")
+
+    def test_per_cpu_times(self):
+        # Check type, value >= 0, str().
+        for times in psutil.cpu_times(percpu=True):
+            total = 0
+            sum(times)
+            for cp_time in times:
+                assert isinstance(cp_time, float)
+                assert cp_time >= 0.0
+                total += cp_time
+            assert round(abs(total - sum(times)), 6) == 0
+            str(times)
+        assert len(psutil.cpu_times(percpu=True)[0]) == len(
+            psutil.cpu_times(percpu=False)
+        )
+
+        # Note: in theory CPU times are always supposed to increase over
+        # time or remain the same but never go backwards. In practice
+        # sometimes this is not the case.
+        # This issue seemd to be afflict Windows:
+        # https://github.com/giampaolo/psutil/issues/392
+        # ...but it turns out also Linux (rarely) behaves the same.
+        # last = psutil.cpu_times(percpu=True)
+        # for x in range(100):
+        #     new = psutil.cpu_times(percpu=True)
+        #     for index in range(len(new)):
+        #         newcpu = new[index]
+        #         lastcpu = last[index]
+        #         for field in newcpu._fields:
+        #             new_t = getattr(newcpu, field)
+        #             last_t = getattr(lastcpu, field)
+        #             self.assertGreaterEqual(
+        #                 new_t, last_t, msg="{} {}".format(lastcpu, newcpu))
+        #     last = new
+
+    def test_per_cpu_times_2(self):
+        # Simulate some work load then make sure time have increased
+        # between calls.
+        tot1 = psutil.cpu_times(percpu=True)
+        giveup_at = time.time() + GLOBAL_TIMEOUT
+        while True:
+            if time.time() >= giveup_at:
+                return self.fail("timeout")
+            tot2 = psutil.cpu_times(percpu=True)
+            for t1, t2 in zip(tot1, tot2):
+                t1, t2 = psutil._cpu_busy_time(t1), psutil._cpu_busy_time(t2)
+                difference = t2 - t1
+                if difference >= 0.05:
+                    return None
+
+    @pytest.mark.skipif(
+        CI_TESTING and OPENBSD, reason="unreliable on OPENBSD + CI"
+    )
+    @retry_on_failure(30)
+    def test_cpu_times_comparison(self):
+        # Make sure the sum of all per cpu times is almost equal to
+        # base "one cpu" times. On OpenBSD the sum of per-CPUs is
+        # higher for some reason.
+        base = psutil.cpu_times()
+        per_cpu = psutil.cpu_times(percpu=True)
+        summed_values = base._make([sum(num) for num in zip(*per_cpu)])
+        for field in base._fields:
+            with self.subTest(field=field, base=base, per_cpu=per_cpu):
+                assert (
+                    abs(getattr(base, field) - getattr(summed_values, field))
+                    < 2
+                )
+
+    def _test_cpu_percent(self, percent, last_ret, new_ret):
+        try:
+            assert isinstance(percent, float)
+            assert percent >= 0.0
+            assert percent <= 100.0 * psutil.cpu_count()
+        except AssertionError as err:
+            raise AssertionError(
+                "\n{}\nlast={}\nnew={}".format(
+                    err, pprint.pformat(last_ret), pprint.pformat(new_ret)
+                )
+            )
+
+    def test_cpu_percent(self):
+        last = psutil.cpu_percent(interval=0.001)
+        for _ in range(100):
+            new = psutil.cpu_percent(interval=None)
+            self._test_cpu_percent(new, last, new)
+            last = new
+        with pytest.raises(ValueError):
+            psutil.cpu_percent(interval=-1)
+
+    def test_per_cpu_percent(self):
+        last = psutil.cpu_percent(interval=0.001, percpu=True)
+        assert len(last) == psutil.cpu_count()
+        for _ in range(100):
+            new = psutil.cpu_percent(interval=None, percpu=True)
+            for percent in new:
+                self._test_cpu_percent(percent, last, new)
+            last = new
+        with pytest.raises(ValueError):
+            psutil.cpu_percent(interval=-1, percpu=True)
+
+    def test_cpu_times_percent(self):
+        last = psutil.cpu_times_percent(interval=0.001)
+        for _ in range(100):
+            new = psutil.cpu_times_percent(interval=None)
+            for percent in new:
+                self._test_cpu_percent(percent, last, new)
+            self._test_cpu_percent(sum(new), last, new)
+            last = new
+        with pytest.raises(ValueError):
+            psutil.cpu_times_percent(interval=-1)
+
+    def test_per_cpu_times_percent(self):
+        last = psutil.cpu_times_percent(interval=0.001, percpu=True)
+        assert len(last) == psutil.cpu_count()
+        for _ in range(100):
+            new = psutil.cpu_times_percent(interval=None, percpu=True)
+            for cpu in new:
+                for percent in cpu:
+                    self._test_cpu_percent(percent, last, new)
+                self._test_cpu_percent(sum(cpu), last, new)
+            last = new
+
+    def test_per_cpu_times_percent_negative(self):
+        # see: https://github.com/giampaolo/psutil/issues/645
+        psutil.cpu_times_percent(percpu=True)
+        zero_times = [
+            x._make([0 for x in range(len(x._fields))])
+            for x in psutil.cpu_times(percpu=True)
+        ]
+        with mock.patch('psutil.cpu_times', return_value=zero_times):
+            for cpu in psutil.cpu_times_percent(percpu=True):
+                for percent in cpu:
+                    self._test_cpu_percent(percent, None, None)
+
+    def test_cpu_stats(self):
+        # Tested more extensively in per-platform test modules.
+        infos = psutil.cpu_stats()
+        assert infos._fields == (
+            'ctx_switches',
+            'interrupts',
+            'soft_interrupts',
+            'syscalls',
+        )
+        for name in infos._fields:
+            value = getattr(infos, name)
+            assert value >= 0
+            # on AIX, ctx_switches is always 0
+            if not AIX and name in {'ctx_switches', 'interrupts'}:
+                assert value > 0
+
+    # TODO: remove this once 1892 is fixed
+    @pytest.mark.skipif(
+        MACOS and platform.machine() == 'arm64', reason="skipped due to #1892"
+    )
+    @pytest.mark.skipif(not HAS_CPU_FREQ, reason="not supported")
+    def test_cpu_freq(self):
+        def check_ls(ls):
+            for nt in ls:
+                assert nt._fields == ('current', 'min', 'max')
+                if nt.max != 0.0:
+                    assert nt.current <= nt.max
+                for name in nt._fields:
+                    value = getattr(nt, name)
+                    assert isinstance(value, (int, float))
+                    assert value >= 0
+
+        ls = psutil.cpu_freq(percpu=True)
+        if (FREEBSD or AARCH64) and not ls:
+            raise pytest.skip(
+                "returns empty list on FreeBSD and Linux aarch64"
+            )
+
+        assert ls, ls
+        check_ls([psutil.cpu_freq(percpu=False)])
+
+        if LINUX:
+            assert len(ls) == psutil.cpu_count()
+
+    @pytest.mark.skipif(not HAS_GETLOADAVG, reason="not supported")
+    def test_getloadavg(self):
+        loadavg = psutil.getloadavg()
+        assert len(loadavg) == 3
+        for load in loadavg:
+            assert isinstance(load, float)
+            assert load >= 0.0
+
+
+class TestDiskAPIs(PsutilTestCase):
+    @pytest.mark.skipif(
+        PYPY and not IS_64BIT, reason="unreliable on PYPY32 + 32BIT"
+    )
+    def test_disk_usage(self):
+        usage = psutil.disk_usage(os.getcwd())
+        assert usage._fields == ('total', 'used', 'free', 'percent')
+
+        assert usage.total > 0, usage
+        assert usage.used > 0, usage
+        assert usage.free > 0, usage
+        assert usage.total > usage.used, usage
+        assert usage.total > usage.free, usage
+        assert 0 <= usage.percent <= 100, usage.percent
+        if hasattr(shutil, 'disk_usage'):
+            # py >= 3.3, see: http://bugs.python.org/issue12442
+            shutil_usage = shutil.disk_usage(os.getcwd())
+            tolerance = 5 * 1024 * 1024  # 5MB
+            assert usage.total == shutil_usage.total
+            assert abs(usage.free - shutil_usage.free) < tolerance
+            if not MACOS_12PLUS:
+                # see https://github.com/giampaolo/psutil/issues/2147
+                assert abs(usage.used - shutil_usage.used) < tolerance
+
+        # if path does not exist OSError ENOENT is expected across
+        # all platforms
+        fname = self.get_testfn()
+        with pytest.raises(FileNotFoundError):
+            psutil.disk_usage(fname)
+
+    @pytest.mark.skipif(not ASCII_FS, reason="not an ASCII fs")
+    def test_disk_usage_unicode(self):
+        # See: https://github.com/giampaolo/psutil/issues/416
+        with pytest.raises(UnicodeEncodeError):
+            psutil.disk_usage(UNICODE_SUFFIX)
+
+    def test_disk_usage_bytes(self):
+        psutil.disk_usage(b'.')
+
+    def test_disk_partitions(self):
+        def check_ntuple(nt):
+            assert isinstance(nt.device, str)
+            assert isinstance(nt.mountpoint, str)
+            assert isinstance(nt.fstype, str)
+            assert isinstance(nt.opts, str)
+
+        # all = False
+        ls = psutil.disk_partitions(all=False)
+        assert ls
+        for disk in ls:
+            check_ntuple(disk)
+            if WINDOWS and 'cdrom' in disk.opts:
+                continue
+            if not POSIX:
+                assert os.path.exists(disk.device), disk
+            else:
+                # we cannot make any assumption about this, see:
+                # http://goo.gl/p9c43
+                disk.device  # noqa: B018
+            # on modern systems mount points can also be files
+            assert os.path.exists(disk.mountpoint), disk
+            assert disk.fstype, disk
+
+        # all = True
+        ls = psutil.disk_partitions(all=True)
+        assert ls
+        for disk in psutil.disk_partitions(all=True):
+            check_ntuple(disk)
+            if not WINDOWS and disk.mountpoint:
+                try:
+                    os.stat(disk.mountpoint)
+                except OSError as err:
+                    if GITHUB_ACTIONS and MACOS and err.errno == errno.EIO:
+                        continue
+                    # http://mail.python.org/pipermail/python-dev/
+                    #     2012-June/120787.html
+                    if err.errno not in {errno.EPERM, errno.EACCES}:
+                        raise
+                else:
+                    assert os.path.exists(disk.mountpoint), disk
+
+        # ---
+
+        def find_mount_point(path):
+            path = os.path.abspath(path)
+            while not os.path.ismount(path):
+                path = os.path.dirname(path)
+            return path.lower()
+
+        mount = find_mount_point(__file__)
+        mounts = [
+            x.mountpoint.lower()
+            for x in psutil.disk_partitions(all=True)
+            if x.mountpoint
+        ]
+        assert mount in mounts
+
+    @pytest.mark.skipif(
+        LINUX and not os.path.exists('/proc/diskstats'),
+        reason="/proc/diskstats not available on this linux version",
+    )
+    @pytest.mark.skipif(
+        CI_TESTING and not psutil.disk_io_counters(), reason="unreliable on CI"
+    )  # no visible disks
+    def test_disk_io_counters(self):
+        def check_ntuple(nt):
+            assert nt[0] == nt.read_count
+            assert nt[1] == nt.write_count
+            assert nt[2] == nt.read_bytes
+            assert nt[3] == nt.write_bytes
+            if not (OPENBSD or NETBSD):
+                assert nt[4] == nt.read_time
+                assert nt[5] == nt.write_time
+                if LINUX:
+                    assert nt[6] == nt.read_merged_count
+                    assert nt[7] == nt.write_merged_count
+                    assert nt[8] == nt.busy_time
+                elif FREEBSD:
+                    assert nt[6] == nt.busy_time
+            for name in nt._fields:
+                assert getattr(nt, name) >= 0, nt
+
+        ret = psutil.disk_io_counters(perdisk=False)
+        assert ret is not None, "no disks on this system?"
+        check_ntuple(ret)
+        ret = psutil.disk_io_counters(perdisk=True)
+        # make sure there are no duplicates
+        assert len(ret) == len(set(ret))
+        for key in ret:
+            assert key, key
+            check_ntuple(ret[key])
+
+    def test_disk_io_counters_no_disks(self):
+        # Emulate a case where no disks are installed, see:
+        # https://github.com/giampaolo/psutil/issues/1062
+        with mock.patch(
+            'psutil._psplatform.disk_io_counters', return_value={}
+        ) as m:
+            assert psutil.disk_io_counters(perdisk=False) is None
+            assert psutil.disk_io_counters(perdisk=True) == {}
+            assert m.called
+
+
+class TestNetAPIs(PsutilTestCase):
+    @pytest.mark.skipif(not HAS_NET_IO_COUNTERS, reason="not supported")
+    def test_net_io_counters(self):
+        def check_ntuple(nt):
+            assert nt[0] == nt.bytes_sent
+            assert nt[1] == nt.bytes_recv
+            assert nt[2] == nt.packets_sent
+            assert nt[3] == nt.packets_recv
+            assert nt[4] == nt.errin
+            assert nt[5] == nt.errout
+            assert nt[6] == nt.dropin
+            assert nt[7] == nt.dropout
+            assert nt.bytes_sent >= 0, nt
+            assert nt.bytes_recv >= 0, nt
+            assert nt.packets_sent >= 0, nt
+            assert nt.packets_recv >= 0, nt
+            assert nt.errin >= 0, nt
+            assert nt.errout >= 0, nt
+            assert nt.dropin >= 0, nt
+            assert nt.dropout >= 0, nt
+
+        ret = psutil.net_io_counters(pernic=False)
+        check_ntuple(ret)
+        ret = psutil.net_io_counters(pernic=True)
+        assert ret != []
+        for key in ret:
+            assert key
+            assert isinstance(key, str)
+            check_ntuple(ret[key])
+
+    @pytest.mark.skipif(not HAS_NET_IO_COUNTERS, reason="not supported")
+    def test_net_io_counters_no_nics(self):
+        # Emulate a case where no NICs are installed, see:
+        # https://github.com/giampaolo/psutil/issues/1062
+        with mock.patch(
+            'psutil._psplatform.net_io_counters', return_value={}
+        ) as m:
+            assert psutil.net_io_counters(pernic=False) is None
+            assert psutil.net_io_counters(pernic=True) == {}
+            assert m.called
+
+    def test_net_if_addrs(self):
+        nics = psutil.net_if_addrs()
+        assert nics, nics
+
+        nic_stats = psutil.net_if_stats()
+
+        # Not reliable on all platforms (net_if_addrs() reports more
+        # interfaces).
+        # self.assertEqual(sorted(nics.keys()),
+        #                  sorted(psutil.net_io_counters(pernic=True).keys()))
+
+        families = {socket.AF_INET, socket.AF_INET6, psutil.AF_LINK}
+        for nic, addrs in nics.items():
+            assert isinstance(nic, str)
+            assert len(set(addrs)) == len(addrs)
+            for addr in addrs:
+                assert isinstance(addr.family, int)
+                assert isinstance(addr.address, str)
+                assert isinstance(addr.netmask, (str, type(None)))
+                assert isinstance(addr.broadcast, (str, type(None)))
+                assert addr.family in families
+                assert isinstance(addr.family, enum.IntEnum)
+                if nic_stats[nic].isup:
+                    # Do not test binding to addresses of interfaces
+                    # that are down
+                    if addr.family == socket.AF_INET:
+                        with socket.socket(addr.family) as s:
+                            s.bind((addr.address, 0))
+                    elif addr.family == socket.AF_INET6:
+                        info = socket.getaddrinfo(
+                            addr.address,
+                            0,
+                            socket.AF_INET6,
+                            socket.SOCK_STREAM,
+                            0,
+                            socket.AI_PASSIVE,
+                        )[0]
+                        af, socktype, proto, _canonname, sa = info
+                        with socket.socket(af, socktype, proto) as s:
+                            s.bind(sa)
+                for ip in (
+                    addr.address,
+                    addr.netmask,
+                    addr.broadcast,
+                    addr.ptp,
+                ):
+                    if ip is not None:
+                        # TODO: skip AF_INET6 for now because I get:
+                        # AddressValueError: Only hex digits permitted in
+                        # u'c6f3%lxcbr0' in u'fe80::c8e0:fff:fe54:c6f3%lxcbr0'
+                        if addr.family != socket.AF_INET6:
+                            check_net_address(ip, addr.family)
+                # broadcast and ptp addresses are mutually exclusive
+                if addr.broadcast:
+                    assert addr.ptp is None
+                elif addr.ptp:
+                    assert addr.broadcast is None
+
+                # check broadcast address
+                if (
+                    addr.broadcast
+                    and addr.netmask
+                    and addr.family in {socket.AF_INET, socket.AF_INET6}
+                ):
+                    assert addr.broadcast == broadcast_addr(addr)
+
+        if BSD or MACOS or SUNOS:
+            if hasattr(socket, "AF_LINK"):
+                assert psutil.AF_LINK == socket.AF_LINK
+        elif LINUX:
+            assert psutil.AF_LINK == socket.AF_PACKET
+        elif WINDOWS:
+            assert psutil.AF_LINK == -1
+
+    def test_net_if_addrs_mac_null_bytes(self):
+        # Simulate that the underlying C function returns an incomplete
+        # MAC address. psutil is supposed to fill it with null bytes.
+        # https://github.com/giampaolo/psutil/issues/786
+        if POSIX:
+            ret = [('em1', psutil.AF_LINK, '06:3d:29', None, None, None)]
+        else:
+            ret = [('em1', -1, '06-3d-29', None, None, None)]
+        with mock.patch(
+            'psutil._psplatform.net_if_addrs', return_value=ret
+        ) as m:
+            addr = psutil.net_if_addrs()['em1'][0]
+            assert m.called
+            if POSIX:
+                assert addr.address == '06:3d:29:00:00:00'
+            else:
+                assert addr.address == '06-3d-29-00-00-00'
+
+    def test_net_if_stats(self):
+        nics = psutil.net_if_stats()
+        assert nics, nics
+        all_duplexes = (
+            psutil.NIC_DUPLEX_FULL,
+            psutil.NIC_DUPLEX_HALF,
+            psutil.NIC_DUPLEX_UNKNOWN,
+        )
+        for name, stats in nics.items():
+            assert isinstance(name, str)
+            isup, duplex, speed, mtu, flags = stats
+            assert isinstance(isup, bool)
+            assert duplex in all_duplexes
+            assert duplex in all_duplexes
+            assert speed >= 0
+            assert mtu >= 0
+            assert isinstance(flags, str)
+
+    @pytest.mark.skipif(
+        not (LINUX or BSD or MACOS), reason="LINUX or BSD or MACOS specific"
+    )
+    def test_net_if_stats_enodev(self):
+        # See: https://github.com/giampaolo/psutil/issues/1279
+        with mock.patch(
+            'psutil._psutil_posix.net_if_mtu',
+            side_effect=OSError(errno.ENODEV, ""),
+        ) as m:
+            ret = psutil.net_if_stats()
+            assert ret == {}
+            assert m.called
+
+
+class TestSensorsAPIs(PsutilTestCase):
+    @pytest.mark.skipif(not HAS_SENSORS_TEMPERATURES, reason="not supported")
+    def test_sensors_temperatures(self):
+        temps = psutil.sensors_temperatures()
+        for name, entries in temps.items():
+            assert isinstance(name, str)
+            for entry in entries:
+                assert isinstance(entry.label, str)
+                if entry.current is not None:
+                    assert entry.current >= 0
+                if entry.high is not None:
+                    assert entry.high >= 0
+                if entry.critical is not None:
+                    assert entry.critical >= 0
+
+    @pytest.mark.skipif(not HAS_SENSORS_TEMPERATURES, reason="not supported")
+    def test_sensors_temperatures_fahreneit(self):
+        d = {'coretemp': [('label', 50.0, 60.0, 70.0)]}
+        with mock.patch(
+            "psutil._psplatform.sensors_temperatures", return_value=d
+        ) as m:
+            temps = psutil.sensors_temperatures(fahrenheit=True)['coretemp'][0]
+            assert m.called
+            assert temps.current == 122.0
+            assert temps.high == 140.0
+            assert temps.critical == 158.0
+
+    @pytest.mark.skipif(not HAS_SENSORS_BATTERY, reason="not supported")
+    @pytest.mark.skipif(not HAS_BATTERY, reason="no battery")
+    def test_sensors_battery(self):
+        ret = psutil.sensors_battery()
+        assert ret.percent >= 0
+        assert ret.percent <= 100
+        if ret.secsleft not in {
+            psutil.POWER_TIME_UNKNOWN,
+            psutil.POWER_TIME_UNLIMITED,
+        }:
+            assert ret.secsleft >= 0
+        elif ret.secsleft == psutil.POWER_TIME_UNLIMITED:
+            assert ret.power_plugged
+        assert isinstance(ret.power_plugged, bool)
+
+    @pytest.mark.skipif(not HAS_SENSORS_FANS, reason="not supported")
+    def test_sensors_fans(self):
+        fans = psutil.sensors_fans()
+        for name, entries in fans.items():
+            assert isinstance(name, str)
+            for entry in entries:
+                assert isinstance(entry.label, str)
+                assert isinstance(entry.current, int)
+                assert entry.current >= 0
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_testutils.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_testutils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6db66e50eb54c8b77222785fb537923ba05f46d3
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_testutils.py
@@ -0,0 +1,577 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Tests for testing utils (psutil.tests namespace)."""
+
+import collections
+import errno
+import os
+import socket
+import stat
+import subprocess
+import textwrap
+import unittest
+import warnings
+from unittest import mock
+
+import psutil
+import psutil.tests
+from psutil import FREEBSD
+from psutil import NETBSD
+from psutil import POSIX
+from psutil._common import open_binary
+from psutil._common import open_text
+from psutil._common import supports_ipv6
+from psutil.tests import CI_TESTING
+from psutil.tests import COVERAGE
+from psutil.tests import HAS_NET_CONNECTIONS_UNIX
+from psutil.tests import HERE
+from psutil.tests import PYTHON_EXE
+from psutil.tests import PYTHON_EXE_ENV
+from psutil.tests import PsutilTestCase
+from psutil.tests import TestMemoryLeak
+from psutil.tests import bind_socket
+from psutil.tests import bind_unix_socket
+from psutil.tests import call_until
+from psutil.tests import chdir
+from psutil.tests import create_sockets
+from psutil.tests import fake_pytest
+from psutil.tests import filter_proc_net_connections
+from psutil.tests import get_free_port
+from psutil.tests import is_namedtuple
+from psutil.tests import process_namespace
+from psutil.tests import pytest
+from psutil.tests import reap_children
+from psutil.tests import retry
+from psutil.tests import retry_on_failure
+from psutil.tests import safe_mkdir
+from psutil.tests import safe_rmpath
+from psutil.tests import system_namespace
+from psutil.tests import tcp_socketpair
+from psutil.tests import terminate
+from psutil.tests import unix_socketpair
+from psutil.tests import wait_for_file
+from psutil.tests import wait_for_pid
+
+
+# ===================================================================
+# --- Unit tests for test utilities.
+# ===================================================================
+
+
+class TestRetryDecorator(PsutilTestCase):
+    @mock.patch('time.sleep')
+    def test_retry_success(self, sleep):
+        # Fail 3 times out of 5; make sure the decorated fun returns.
+
+        @retry(retries=5, interval=1, logfun=None)
+        def foo():
+            while queue:
+                queue.pop()
+                1 / 0  # noqa: B018
+            return 1
+
+        queue = list(range(3))
+        assert foo() == 1
+        assert sleep.call_count == 3
+
+    @mock.patch('time.sleep')
+    def test_retry_failure(self, sleep):
+        # Fail 6 times out of 5; th function is supposed to raise exc.
+        @retry(retries=5, interval=1, logfun=None)
+        def foo():
+            while queue:
+                queue.pop()
+                1 / 0  # noqa: B018
+            return 1
+
+        queue = list(range(6))
+        with pytest.raises(ZeroDivisionError):
+            foo()
+        assert sleep.call_count == 5
+
+    @mock.patch('time.sleep')
+    def test_exception_arg(self, sleep):
+        @retry(exception=ValueError, interval=1)
+        def foo():
+            raise TypeError
+
+        with pytest.raises(TypeError):
+            foo()
+        assert sleep.call_count == 0
+
+    @mock.patch('time.sleep')
+    def test_no_interval_arg(self, sleep):
+        # if interval is not specified sleep is not supposed to be called
+
+        @retry(retries=5, interval=None, logfun=None)
+        def foo():
+            1 / 0  # noqa: B018
+
+        with pytest.raises(ZeroDivisionError):
+            foo()
+        assert sleep.call_count == 0
+
+    @mock.patch('time.sleep')
+    def test_retries_arg(self, sleep):
+        @retry(retries=5, interval=1, logfun=None)
+        def foo():
+            1 / 0  # noqa: B018
+
+        with pytest.raises(ZeroDivisionError):
+            foo()
+        assert sleep.call_count == 5
+
+    @mock.patch('time.sleep')
+    def test_retries_and_timeout_args(self, sleep):
+        with pytest.raises(ValueError):
+            retry(retries=5, timeout=1)
+
+
+class TestSyncTestUtils(PsutilTestCase):
+    def test_wait_for_pid(self):
+        wait_for_pid(os.getpid())
+        nopid = max(psutil.pids()) + 99999
+        with mock.patch('psutil.tests.retry.__iter__', return_value=iter([0])):
+            with pytest.raises(psutil.NoSuchProcess):
+                wait_for_pid(nopid)
+
+    def test_wait_for_file(self):
+        testfn = self.get_testfn()
+        with open(testfn, 'w') as f:
+            f.write('foo')
+        wait_for_file(testfn)
+        assert not os.path.exists(testfn)
+
+    def test_wait_for_file_empty(self):
+        testfn = self.get_testfn()
+        with open(testfn, 'w'):
+            pass
+        wait_for_file(testfn, empty=True)
+        assert not os.path.exists(testfn)
+
+    def test_wait_for_file_no_file(self):
+        testfn = self.get_testfn()
+        with mock.patch('psutil.tests.retry.__iter__', return_value=iter([0])):
+            with pytest.raises(OSError):
+                wait_for_file(testfn)
+
+    def test_wait_for_file_no_delete(self):
+        testfn = self.get_testfn()
+        with open(testfn, 'w') as f:
+            f.write('foo')
+        wait_for_file(testfn, delete=False)
+        assert os.path.exists(testfn)
+
+    def test_call_until(self):
+        call_until(lambda: 1)
+        # TODO: test for timeout
+
+
+class TestFSTestUtils(PsutilTestCase):
+    def test_open_text(self):
+        with open_text(__file__) as f:
+            assert f.mode == 'r'
+
+    def test_open_binary(self):
+        with open_binary(__file__) as f:
+            assert f.mode == 'rb'
+
+    def test_safe_mkdir(self):
+        testfn = self.get_testfn()
+        safe_mkdir(testfn)
+        assert os.path.isdir(testfn)
+        safe_mkdir(testfn)
+        assert os.path.isdir(testfn)
+
+    def test_safe_rmpath(self):
+        # test file is removed
+        testfn = self.get_testfn()
+        open(testfn, 'w').close()
+        safe_rmpath(testfn)
+        assert not os.path.exists(testfn)
+        # test no exception if path does not exist
+        safe_rmpath(testfn)
+        # test dir is removed
+        os.mkdir(testfn)
+        safe_rmpath(testfn)
+        assert not os.path.exists(testfn)
+        # test other exceptions are raised
+        with mock.patch(
+            'psutil.tests.os.stat', side_effect=OSError(errno.EINVAL, "")
+        ) as m:
+            with pytest.raises(OSError):
+                safe_rmpath(testfn)
+            assert m.called
+
+    def test_chdir(self):
+        testfn = self.get_testfn()
+        base = os.getcwd()
+        os.mkdir(testfn)
+        with chdir(testfn):
+            assert os.getcwd() == os.path.join(base, testfn)
+        assert os.getcwd() == base
+
+
+class TestProcessUtils(PsutilTestCase):
+    def test_reap_children(self):
+        subp = self.spawn_testproc()
+        p = psutil.Process(subp.pid)
+        assert p.is_running()
+        reap_children()
+        assert not p.is_running()
+        assert not psutil.tests._pids_started
+        assert not psutil.tests._subprocesses_started
+
+    def test_spawn_children_pair(self):
+        child, grandchild = self.spawn_children_pair()
+        assert child.pid != grandchild.pid
+        assert child.is_running()
+        assert grandchild.is_running()
+        children = psutil.Process().children()
+        assert children == [child]
+        children = psutil.Process().children(recursive=True)
+        assert len(children) == 2
+        assert child in children
+        assert grandchild in children
+        assert child.ppid() == os.getpid()
+        assert grandchild.ppid() == child.pid
+
+        terminate(child)
+        assert not child.is_running()
+        assert grandchild.is_running()
+
+        terminate(grandchild)
+        assert not grandchild.is_running()
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_spawn_zombie(self):
+        _parent, zombie = self.spawn_zombie()
+        assert zombie.status() == psutil.STATUS_ZOMBIE
+
+    def test_terminate(self):
+        # by subprocess.Popen
+        p = self.spawn_testproc()
+        terminate(p)
+        self.assertPidGone(p.pid)
+        terminate(p)
+        # by psutil.Process
+        p = psutil.Process(self.spawn_testproc().pid)
+        terminate(p)
+        self.assertPidGone(p.pid)
+        terminate(p)
+        # by psutil.Popen
+        cmd = [
+            PYTHON_EXE,
+            "-c",
+            "import time; [time.sleep(0.1) for x in range(100)];",
+        ]
+        p = psutil.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            env=PYTHON_EXE_ENV,
+        )
+        terminate(p)
+        self.assertPidGone(p.pid)
+        terminate(p)
+        # by PID
+        pid = self.spawn_testproc().pid
+        terminate(pid)
+        self.assertPidGone(p.pid)
+        terminate(pid)
+        # zombie
+        if POSIX:
+            parent, zombie = self.spawn_zombie()
+            terminate(parent)
+            terminate(zombie)
+            self.assertPidGone(parent.pid)
+            self.assertPidGone(zombie.pid)
+
+
+class TestNetUtils(PsutilTestCase):
+    def bind_socket(self):
+        port = get_free_port()
+        with bind_socket(addr=('', port)) as s:
+            assert s.getsockname()[1] == port
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_bind_unix_socket(self):
+        name = self.get_testfn()
+        with bind_unix_socket(name) as sock:
+            assert sock.family == socket.AF_UNIX
+            assert sock.type == socket.SOCK_STREAM
+            assert sock.getsockname() == name
+            assert os.path.exists(name)
+            assert stat.S_ISSOCK(os.stat(name).st_mode)
+        # UDP
+        name = self.get_testfn()
+        with bind_unix_socket(name, type=socket.SOCK_DGRAM) as sock:
+            assert sock.type == socket.SOCK_DGRAM
+
+    def test_tcp_socketpair(self):
+        addr = ("127.0.0.1", get_free_port())
+        server, client = tcp_socketpair(socket.AF_INET, addr=addr)
+        with server, client:
+            # Ensure they are connected and the positions are correct.
+            assert server.getsockname() == addr
+            assert client.getpeername() == addr
+            assert client.getsockname() != addr
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    @pytest.mark.skipif(
+        NETBSD or FREEBSD, reason="/var/run/log UNIX socket opened by default"
+    )
+    def test_unix_socketpair(self):
+        p = psutil.Process()
+        num_fds = p.num_fds()
+        assert not filter_proc_net_connections(p.net_connections(kind='unix'))
+        name = self.get_testfn()
+        server, client = unix_socketpair(name)
+        try:
+            assert os.path.exists(name)
+            assert stat.S_ISSOCK(os.stat(name).st_mode)
+            assert p.num_fds() - num_fds == 2
+            assert (
+                len(
+                    filter_proc_net_connections(p.net_connections(kind='unix'))
+                )
+                == 2
+            )
+            assert server.getsockname() == name
+            assert client.getpeername() == name
+        finally:
+            client.close()
+            server.close()
+
+    def test_create_sockets(self):
+        with create_sockets() as socks:
+            fams = collections.defaultdict(int)
+            types = collections.defaultdict(int)
+            for s in socks:
+                fams[s.family] += 1
+                # work around http://bugs.python.org/issue30204
+                types[s.getsockopt(socket.SOL_SOCKET, socket.SO_TYPE)] += 1
+            assert fams[socket.AF_INET] >= 2
+            if supports_ipv6():
+                assert fams[socket.AF_INET6] >= 2
+            if POSIX and HAS_NET_CONNECTIONS_UNIX:
+                assert fams[socket.AF_UNIX] >= 2
+            assert types[socket.SOCK_STREAM] >= 2
+            assert types[socket.SOCK_DGRAM] >= 2
+
+
+@pytest.mark.xdist_group(name="serial")
+class TestMemLeakClass(TestMemoryLeak):
+    @retry_on_failure()
+    def test_times(self):
+        def fun():
+            cnt['cnt'] += 1
+
+        cnt = {'cnt': 0}
+        self.execute(fun, times=10, warmup_times=15)
+        assert cnt['cnt'] == 26
+
+    def test_param_err(self):
+        with pytest.raises(ValueError):
+            self.execute(lambda: 0, times=0)
+        with pytest.raises(ValueError):
+            self.execute(lambda: 0, times=-1)
+        with pytest.raises(ValueError):
+            self.execute(lambda: 0, warmup_times=-1)
+        with pytest.raises(ValueError):
+            self.execute(lambda: 0, tolerance=-1)
+        with pytest.raises(ValueError):
+            self.execute(lambda: 0, retries=-1)
+
+    @retry_on_failure()
+    @pytest.mark.skipif(CI_TESTING, reason="skipped on CI")
+    @pytest.mark.skipif(COVERAGE, reason="skipped during test coverage")
+    def test_leak_mem(self):
+        ls = []
+
+        def fun(ls=ls):
+            ls.append("x" * 248 * 1024)
+
+        try:
+            # will consume around 60M in total
+            with pytest.raises(AssertionError, match="extra-mem"):
+                self.execute(fun, times=100)
+        finally:
+            del ls
+
+    def test_unclosed_files(self):
+        def fun():
+            f = open(__file__)  # noqa: SIM115
+            self.addCleanup(f.close)
+            box.append(f)
+
+        box = []
+        kind = "fd" if POSIX else "handle"
+        with pytest.raises(AssertionError, match="unclosed " + kind):
+            self.execute(fun)
+
+    def test_tolerance(self):
+        def fun():
+            ls.append("x" * 24 * 1024)
+
+        ls = []
+        times = 100
+        self.execute(
+            fun, times=times, warmup_times=0, tolerance=200 * 1024 * 1024
+        )
+        assert len(ls) == times + 1
+
+    def test_execute_w_exc(self):
+        def fun_1():
+            1 / 0  # noqa: B018
+
+        self.execute_w_exc(ZeroDivisionError, fun_1)
+        with pytest.raises(ZeroDivisionError):
+            self.execute_w_exc(OSError, fun_1)
+
+        def fun_2():
+            pass
+
+        with pytest.raises(AssertionError):
+            self.execute_w_exc(ZeroDivisionError, fun_2)
+
+
+class TestFakePytest(PsutilTestCase):
+    def run_test_class(self, klass):
+        suite = unittest.TestSuite()
+        suite.addTest(klass)
+        runner = unittest.TextTestRunner()
+        result = runner.run(suite)
+        return result
+
+    def test_raises(self):
+        with fake_pytest.raises(ZeroDivisionError) as cm:
+            1 / 0  # noqa: B018
+        assert isinstance(cm.value, ZeroDivisionError)
+
+        with fake_pytest.raises(ValueError, match="foo") as cm:
+            raise ValueError("foo")
+
+        try:
+            with fake_pytest.raises(ValueError, match="foo") as cm:
+                raise ValueError("bar")
+        except AssertionError as err:
+            assert str(err) == '"foo" does not match "bar"'
+        else:
+            raise self.fail("exception not raised")
+
+    def test_mark(self):
+        @fake_pytest.mark.xdist_group(name="serial")
+        def foo():
+            return 1
+
+        assert foo() == 1
+
+        @fake_pytest.mark.xdist_group(name="serial")
+        class Foo:
+            def bar(self):
+                return 1
+
+        assert Foo().bar() == 1
+
+    def test_skipif(self):
+        class TestCase(unittest.TestCase):
+            @fake_pytest.mark.skipif(True, reason="reason")
+            def foo(self):
+                assert 1 == 1  # noqa: PLR0133
+
+        result = self.run_test_class(TestCase("foo"))
+        assert result.wasSuccessful()
+        assert len(result.skipped) == 1
+        assert result.skipped[0][1] == "reason"
+
+        class TestCase(unittest.TestCase):
+            @fake_pytest.mark.skipif(False, reason="reason")
+            def foo(self):
+                assert 1 == 1  # noqa: PLR0133
+
+        result = self.run_test_class(TestCase("foo"))
+        assert result.wasSuccessful()
+        assert len(result.skipped) == 0
+
+    def test_skip(self):
+        class TestCase(unittest.TestCase):
+            def foo(self):
+                fake_pytest.skip("reason")
+                assert 1 == 0  # noqa: PLR0133
+
+        result = self.run_test_class(TestCase("foo"))
+        assert result.wasSuccessful()
+        assert len(result.skipped) == 1
+        assert result.skipped[0][1] == "reason"
+
+    def test_main(self):
+        tmpdir = self.get_testfn(dir=HERE)
+        os.mkdir(tmpdir)
+        with open(os.path.join(tmpdir, "__init__.py"), "w"):
+            pass
+        with open(os.path.join(tmpdir, "test_file.py"), "w") as f:
+            f.write(textwrap.dedent("""\
+                import unittest
+
+                class TestCase(unittest.TestCase):
+                    def test_passed(self):
+                        pass
+                """).lstrip())
+        with mock.patch.object(psutil.tests, "HERE", tmpdir):
+            with self.assertWarnsRegex(
+                UserWarning, "Fake pytest module was used"
+            ):
+                suite = fake_pytest.main()
+                assert suite.countTestCases() == 1
+
+    def test_warns(self):
+        # success
+        with fake_pytest.warns(UserWarning):
+            warnings.warn("foo", UserWarning, stacklevel=1)
+
+        # failure
+        try:
+            with fake_pytest.warns(UserWarning):
+                warnings.warn("foo", DeprecationWarning, stacklevel=1)
+        except AssertionError:
+            pass
+        else:
+            raise self.fail("exception not raised")
+
+        # match success
+        with fake_pytest.warns(UserWarning, match="foo"):
+            warnings.warn("foo", UserWarning, stacklevel=1)
+
+        # match failure
+        try:
+            with fake_pytest.warns(UserWarning, match="foo"):
+                warnings.warn("bar", UserWarning, stacklevel=1)
+        except AssertionError:
+            pass
+        else:
+            raise self.fail("exception not raised")
+
+
+class TestTestingUtils(PsutilTestCase):
+    def test_process_namespace(self):
+        p = psutil.Process()
+        ns = process_namespace(p)
+        ns.test()
+        fun = next(x for x in ns.iter(ns.getters) if x[1] == 'ppid')[0]
+        assert fun() == p.ppid()
+
+    def test_system_namespace(self):
+        ns = system_namespace()
+        fun = next(x for x in ns.iter(ns.getters) if x[1] == 'net_if_addrs')[0]
+        assert fun() == psutil.net_if_addrs()
+
+
+class TestOtherUtils(PsutilTestCase):
+    def test_is_namedtuple(self):
+        assert is_namedtuple(collections.namedtuple('foo', 'a b c')(1, 2, 3))
+        assert not is_namedtuple(tuple())
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_unicode.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_unicode.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8a8c4bfc55e7f077a6f27ca2b42dab183ba88ed
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_unicode.py
@@ -0,0 +1,313 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Notes about unicode handling in psutil
+======================================.
+
+Starting from version 5.3.0 psutil adds unicode support, see:
+https://github.com/giampaolo/psutil/issues/1040
+The notes below apply to *any* API returning a string such as
+process exe(), cwd() or username():
+
+* all strings are encoded by using the OS filesystem encoding
+  (sys.getfilesystemencoding()) which varies depending on the platform
+  (e.g. "UTF-8" on macOS, "mbcs" on Win)
+* no API call is supposed to crash with UnicodeDecodeError
+* instead, in case of badly encoded data returned by the OS, the
+  following error handlers are used to replace the corrupted characters in
+  the string:
+    * sys.getfilesystemencodeerrors() or "surrogatescape" on POSIX and
+      "replace" on Windows.
+
+For a detailed explanation of how psutil handles unicode see #1040.
+
+Tests
+=====
+
+List of APIs returning or dealing with a string:
+('not tested' means they are not tested to deal with non-ASCII strings):
+
+* Process.cmdline()
+* Process.cwd()
+* Process.environ()
+* Process.exe()
+* Process.memory_maps()
+* Process.name()
+* Process.net_connections('unix')
+* Process.open_files()
+* Process.username()             (not tested)
+
+* disk_io_counters()             (not tested)
+* disk_partitions()              (not tested)
+* disk_usage(str)
+* net_connections('unix')
+* net_if_addrs()                 (not tested)
+* net_if_stats()                 (not tested)
+* net_io_counters()              (not tested)
+* sensors_fans()                 (not tested)
+* sensors_temperatures()         (not tested)
+* users()                        (not tested)
+
+* WindowsService.binpath()       (not tested)
+* WindowsService.description()   (not tested)
+* WindowsService.display_name()  (not tested)
+* WindowsService.name()          (not tested)
+* WindowsService.status()        (not tested)
+* WindowsService.username()      (not tested)
+
+In here we create a unicode path with a funky non-ASCII name and (where
+possible) make psutil return it back (e.g. on name(), exe(), open_files(),
+etc.) and make sure that:
+
+* psutil never crashes with UnicodeDecodeError
+* the returned path matches
+"""
+
+import os
+import shutil
+import warnings
+from contextlib import closing
+
+import psutil
+from psutil import BSD
+from psutil import POSIX
+from psutil import WINDOWS
+from psutil.tests import ASCII_FS
+from psutil.tests import CI_TESTING
+from psutil.tests import HAS_ENVIRON
+from psutil.tests import HAS_MEMORY_MAPS
+from psutil.tests import HAS_NET_CONNECTIONS_UNIX
+from psutil.tests import INVALID_UNICODE_SUFFIX
+from psutil.tests import PYPY
+from psutil.tests import TESTFN_PREFIX
+from psutil.tests import UNICODE_SUFFIX
+from psutil.tests import PsutilTestCase
+from psutil.tests import bind_unix_socket
+from psutil.tests import chdir
+from psutil.tests import copyload_shared_lib
+from psutil.tests import create_py_exe
+from psutil.tests import get_testfn
+from psutil.tests import pytest
+from psutil.tests import safe_mkdir
+from psutil.tests import safe_rmpath
+from psutil.tests import skip_on_access_denied
+from psutil.tests import spawn_testproc
+from psutil.tests import terminate
+
+
+def try_unicode(suffix):
+    """Return True if both the fs and the subprocess module can
+    deal with a unicode file name.
+    """
+    sproc = None
+    testfn = get_testfn(suffix=suffix)
+    try:
+        safe_rmpath(testfn)
+        create_py_exe(testfn)
+        sproc = spawn_testproc(cmd=[testfn])
+        shutil.copyfile(testfn, testfn + '-2')
+        safe_rmpath(testfn + '-2')
+    except (UnicodeEncodeError, OSError):
+        return False
+    else:
+        return True
+    finally:
+        if sproc is not None:
+            terminate(sproc)
+        safe_rmpath(testfn)
+
+
+# ===================================================================
+# FS APIs
+# ===================================================================
+
+
+class BaseUnicodeTest(PsutilTestCase):
+    funky_suffix = None
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.skip_tests = False
+        cls.funky_name = None
+        if cls.funky_suffix is not None:
+            if not try_unicode(cls.funky_suffix):
+                cls.skip_tests = True
+            else:
+                cls.funky_name = get_testfn(suffix=cls.funky_suffix)
+                create_py_exe(cls.funky_name)
+
+    def setUp(self):
+        super().setUp()
+        if self.skip_tests:
+            raise pytest.skip("can't handle unicode str")
+
+
+@pytest.mark.xdist_group(name="serial")
+@pytest.mark.skipif(ASCII_FS, reason="ASCII fs")
+class TestFSAPIs(BaseUnicodeTest):
+    """Test FS APIs with a funky, valid, UTF8 path name."""
+
+    funky_suffix = UNICODE_SUFFIX
+
+    def expect_exact_path_match(self):
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            return self.funky_name in os.listdir(".")
+
+    # ---
+
+    def test_proc_exe(self):
+        cmd = [
+            self.funky_name,
+            "-c",
+            "import time; [time.sleep(0.1) for x in range(100)]",
+        ]
+        subp = self.spawn_testproc(cmd)
+        p = psutil.Process(subp.pid)
+        exe = p.exe()
+        assert isinstance(exe, str)
+        if self.expect_exact_path_match():
+            assert os.path.normcase(exe) == os.path.normcase(self.funky_name)
+
+    def test_proc_name(self):
+        cmd = [
+            self.funky_name,
+            "-c",
+            "import time; [time.sleep(0.1) for x in range(100)]",
+        ]
+        subp = self.spawn_testproc(cmd)
+        name = psutil.Process(subp.pid).name()
+        assert isinstance(name, str)
+        if self.expect_exact_path_match():
+            assert name == os.path.basename(self.funky_name)
+
+    def test_proc_cmdline(self):
+        cmd = [
+            self.funky_name,
+            "-c",
+            "import time; [time.sleep(0.1) for x in range(100)]",
+        ]
+        subp = self.spawn_testproc(cmd)
+        p = psutil.Process(subp.pid)
+        cmdline = p.cmdline()
+        for part in cmdline:
+            assert isinstance(part, str)
+        if self.expect_exact_path_match():
+            assert cmdline == cmd
+
+    def test_proc_cwd(self):
+        dname = self.funky_name + "2"
+        self.addCleanup(safe_rmpath, dname)
+        safe_mkdir(dname)
+        with chdir(dname):
+            p = psutil.Process()
+            cwd = p.cwd()
+        assert isinstance(p.cwd(), str)
+        if self.expect_exact_path_match():
+            assert cwd == dname
+
+    @pytest.mark.skipif(PYPY and WINDOWS, reason="fails on PYPY + WINDOWS")
+    def test_proc_open_files(self):
+        p = psutil.Process()
+        start = set(p.open_files())
+        with open(self.funky_name, 'rb'):
+            new = set(p.open_files())
+        path = (new - start).pop().path
+        assert isinstance(path, str)
+        if BSD and not path:
+            # XXX - see https://github.com/giampaolo/psutil/issues/595
+            raise pytest.skip("open_files on BSD is broken")
+        if self.expect_exact_path_match():
+            assert os.path.normcase(path) == os.path.normcase(self.funky_name)
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    def test_proc_net_connections(self):
+        name = self.get_testfn(suffix=self.funky_suffix)
+        sock = bind_unix_socket(name)
+        with closing(sock):
+            conn = psutil.Process().net_connections('unix')[0]
+            assert isinstance(conn.laddr, str)
+            assert conn.laddr == name
+
+    @pytest.mark.skipif(not POSIX, reason="POSIX only")
+    @pytest.mark.skipif(
+        not HAS_NET_CONNECTIONS_UNIX, reason="can't list UNIX sockets"
+    )
+    @skip_on_access_denied()
+    def test_net_connections(self):
+        def find_sock(cons):
+            for conn in cons:
+                if os.path.basename(conn.laddr).startswith(TESTFN_PREFIX):
+                    return conn
+            raise ValueError("connection not found")
+
+        name = self.get_testfn(suffix=self.funky_suffix)
+        sock = bind_unix_socket(name)
+        with closing(sock):
+            cons = psutil.net_connections(kind='unix')
+            conn = find_sock(cons)
+            assert isinstance(conn.laddr, str)
+            assert conn.laddr == name
+
+    def test_disk_usage(self):
+        dname = self.funky_name + "2"
+        self.addCleanup(safe_rmpath, dname)
+        safe_mkdir(dname)
+        psutil.disk_usage(dname)
+
+    @pytest.mark.skipif(not HAS_MEMORY_MAPS, reason="not supported")
+    @pytest.mark.skipif(PYPY, reason="unstable on PYPY")
+    def test_memory_maps(self):
+        with copyload_shared_lib(suffix=self.funky_suffix) as funky_path:
+
+            def normpath(p):
+                return os.path.realpath(os.path.normcase(p))
+
+            libpaths = [
+                normpath(x.path) for x in psutil.Process().memory_maps()
+            ]
+            # ...just to have a clearer msg in case of failure
+            libpaths = [x for x in libpaths if TESTFN_PREFIX in x]
+            assert normpath(funky_path) in libpaths
+            for path in libpaths:
+                assert isinstance(path, str)
+
+
+@pytest.mark.skipif(CI_TESTING, reason="unreliable on CI")
+class TestFSAPIsWithInvalidPath(TestFSAPIs):
+    """Test FS APIs with a funky, invalid path name."""
+
+    funky_suffix = INVALID_UNICODE_SUFFIX
+
+    def expect_exact_path_match(self):
+        return True
+
+
+# ===================================================================
+# Non fs APIs
+# ===================================================================
+
+
+class TestNonFSAPIS(BaseUnicodeTest):
+    """Unicode tests for non fs-related APIs."""
+
+    funky_suffix = UNICODE_SUFFIX
+
+    @pytest.mark.skipif(not HAS_ENVIRON, reason="not supported")
+    @pytest.mark.skipif(PYPY and WINDOWS, reason="segfaults on PYPY + WINDOWS")
+    def test_proc_environ(self):
+        # Note: differently from others, this test does not deal
+        # with fs paths.
+        env = os.environ.copy()
+        env['FUNNY_ARG'] = self.funky_suffix
+        sproc = self.spawn_testproc(env=env)
+        p = psutil.Process(sproc.pid)
+        env = p.environ()
+        for k, v in env.items():
+            assert isinstance(k, str)
+            assert isinstance(v, str)
+        assert env['FUNNY_ARG'] == self.funky_suffix
diff --git a/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_windows.py b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_windows.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5c536b468d44dfbbe44f4d5cfa6f443913cca57
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/psutil/tests/test_windows.py
@@ -0,0 +1,914 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Windows specific tests."""
+
+import datetime
+import glob
+import os
+import platform
+import re
+import shutil
+import signal
+import subprocess
+import sys
+import time
+import warnings
+from unittest import mock
+
+import psutil
+from psutil import WINDOWS
+from psutil.tests import GITHUB_ACTIONS
+from psutil.tests import HAS_BATTERY
+from psutil.tests import IS_64BIT
+from psutil.tests import PYPY
+from psutil.tests import TOLERANCE_DISK_USAGE
+from psutil.tests import TOLERANCE_SYS_MEM
+from psutil.tests import PsutilTestCase
+from psutil.tests import pytest
+from psutil.tests import retry_on_failure
+from psutil.tests import sh
+from psutil.tests import spawn_testproc
+from psutil.tests import terminate
+
+
+if WINDOWS and not PYPY:
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        import win32api  # requires "pip install pywin32"
+        import win32con
+        import win32process
+        import wmi  # requires "pip install wmi" / "make install-pydeps-test"
+
+if WINDOWS:
+    from psutil._pswindows import convert_oserror
+
+
+cext = psutil._psplatform.cext
+
+
+@pytest.mark.skipif(not WINDOWS, reason="WINDOWS only")
+@pytest.mark.skipif(PYPY, reason="pywin32 not available on PYPY")
+class WindowsTestCase(PsutilTestCase):
+    pass
+
+
+def powershell(cmd):
+    """Currently not used, but available just in case. Usage:
+
+    >>> powershell(
+        "Get-CIMInstance Win32_PageFileUsage | Select AllocatedBaseSize")
+    """
+    if not shutil.which("powershell.exe"):
+        raise pytest.skip("powershell.exe not available")
+    cmdline = (
+        "powershell.exe -ExecutionPolicy Bypass -NoLogo -NonInteractive "
+        f"-NoProfile -WindowStyle Hidden -Command \"{cmd}\""  # noqa: Q003
+    )
+    return sh(cmdline)
+
+
+def wmic(path, what, converter=int):
+    """Currently not used, but available just in case. Usage:
+
+    >>> wmic("Win32_OperatingSystem", "FreePhysicalMemory")
+    2134124534
+    """
+    out = sh(f"wmic path {path} get {what}").strip()
+    data = "".join(out.splitlines()[1:]).strip()  # get rid of the header
+    if converter is not None:
+        if "," in what:
+            return tuple(converter(x) for x in data.split())
+        else:
+            return converter(data)
+    else:
+        return data
+
+
+# ===================================================================
+# System APIs
+# ===================================================================
+
+
+class TestCpuAPIs(WindowsTestCase):
+    @pytest.mark.skipif(
+        'NUMBER_OF_PROCESSORS' not in os.environ,
+        reason="NUMBER_OF_PROCESSORS env var is not available",
+    )
+    def test_cpu_count_vs_NUMBER_OF_PROCESSORS(self):
+        # Will likely fail on many-cores systems:
+        # https://stackoverflow.com/questions/31209256
+        num_cpus = int(os.environ['NUMBER_OF_PROCESSORS'])
+        assert num_cpus == psutil.cpu_count()
+
+    def test_cpu_count_vs_GetSystemInfo(self):
+        # Will likely fail on many-cores systems:
+        # https://stackoverflow.com/questions/31209256
+        sys_value = win32api.GetSystemInfo()[5]
+        psutil_value = psutil.cpu_count()
+        assert sys_value == psutil_value
+
+    def test_cpu_count_logical_vs_wmi(self):
+        w = wmi.WMI()
+        procs = sum(
+            proc.NumberOfLogicalProcessors for proc in w.Win32_Processor()
+        )
+        assert psutil.cpu_count() == procs
+
+    def test_cpu_count_cores_vs_wmi(self):
+        w = wmi.WMI()
+        cores = sum(proc.NumberOfCores for proc in w.Win32_Processor())
+        assert psutil.cpu_count(logical=False) == cores
+
+    def test_cpu_count_vs_cpu_times(self):
+        assert psutil.cpu_count() == len(psutil.cpu_times(percpu=True))
+
+    def test_cpu_freq(self):
+        w = wmi.WMI()
+        proc = w.Win32_Processor()[0]
+        assert proc.CurrentClockSpeed == psutil.cpu_freq().current
+        assert proc.MaxClockSpeed == psutil.cpu_freq().max
+
+
+class TestSystemAPIs(WindowsTestCase):
+    def test_nic_names(self):
+        out = sh('ipconfig /all')
+        nics = psutil.net_io_counters(pernic=True).keys()
+        for nic in nics:
+            if "pseudo-interface" in nic.replace(' ', '-').lower():
+                continue
+            if nic not in out:
+                raise self.fail(
+                    f"{nic!r} nic wasn't found in 'ipconfig /all' output"
+                )
+
+    def test_total_phymem(self):
+        w = wmi.WMI().Win32_ComputerSystem()[0]
+        assert int(w.TotalPhysicalMemory) == psutil.virtual_memory().total
+
+    def test_free_phymem(self):
+        w = wmi.WMI().Win32_PerfRawData_PerfOS_Memory()[0]
+        assert (
+            abs(int(w.AvailableBytes) - psutil.virtual_memory().free)
+            < TOLERANCE_SYS_MEM
+        )
+
+    def test_total_swapmem(self):
+        w = wmi.WMI().Win32_PerfRawData_PerfOS_Memory()[0]
+        assert (
+            int(w.CommitLimit) - psutil.virtual_memory().total
+            == psutil.swap_memory().total
+        )
+        if psutil.swap_memory().total == 0:
+            assert psutil.swap_memory().free == 0
+            assert psutil.swap_memory().used == 0
+
+    def test_percent_swapmem(self):
+        if psutil.swap_memory().total > 0:
+            w = wmi.WMI().Win32_PerfRawData_PerfOS_PagingFile(Name="_Total")[0]
+            # calculate swap usage to percent
+            percentSwap = int(w.PercentUsage) * 100 / int(w.PercentUsage_Base)
+            # exact percent may change but should be reasonable
+            # assert within +/- 5% and between 0 and 100%
+            assert psutil.swap_memory().percent >= 0
+            assert abs(psutil.swap_memory().percent - percentSwap) < 5
+            assert psutil.swap_memory().percent <= 100
+
+    # @pytest.mark.skipif(wmi is None, reason="wmi module is not installed")
+    # def test__UPTIME(self):
+    #     # _UPTIME constant is not public but it is used internally
+    #     # as value to return for pid 0 creation time.
+    #     # WMI behaves the same.
+    #     w = wmi.WMI().Win32_Process(ProcessId=self.pid)[0]
+    #     p = psutil.Process(0)
+    #     wmic_create = str(w.CreationDate.split('.')[0])
+    #     psutil_create = time.strftime("%Y%m%d%H%M%S",
+    #                                   time.localtime(p.create_time()))
+
+    # Note: this test is not very reliable
+    @retry_on_failure()
+    def test_pids(self):
+        # Note: this test might fail if the OS is starting/killing
+        # other processes in the meantime
+        w = wmi.WMI().Win32_Process()
+        wmi_pids = {x.ProcessId for x in w}
+        psutil_pids = set(psutil.pids())
+        assert wmi_pids == psutil_pids
+
+    @retry_on_failure()
+    def test_disks(self):
+        ps_parts = psutil.disk_partitions(all=True)
+        wmi_parts = wmi.WMI().Win32_LogicalDisk()
+        for ps_part in ps_parts:
+            for wmi_part in wmi_parts:
+                if ps_part.device.replace('\\', '') == wmi_part.DeviceID:
+                    if not ps_part.mountpoint:
+                        # this is usually a CD-ROM with no disk inserted
+                        break
+                    if 'cdrom' in ps_part.opts:
+                        break
+                    if ps_part.mountpoint.startswith('A:'):
+                        break  # floppy
+                    try:
+                        usage = psutil.disk_usage(ps_part.mountpoint)
+                    except FileNotFoundError:
+                        # usually this is the floppy
+                        break
+                    assert usage.total == int(wmi_part.Size)
+                    wmi_free = int(wmi_part.FreeSpace)
+                    assert usage.free == wmi_free
+                    # 10 MB tolerance
+                    if abs(usage.free - wmi_free) > 10 * 1024 * 1024:
+                        raise self.fail(f"psutil={usage.free}, wmi={wmi_free}")
+                    break
+            else:
+                raise self.fail(f"can't find partition {ps_part!r}")
+
+    @retry_on_failure()
+    def test_disk_usage(self):
+        for disk in psutil.disk_partitions():
+            if 'cdrom' in disk.opts:
+                continue
+            sys_value = win32api.GetDiskFreeSpaceEx(disk.mountpoint)
+            psutil_value = psutil.disk_usage(disk.mountpoint)
+            assert abs(sys_value[0] - psutil_value.free) < TOLERANCE_DISK_USAGE
+            assert (
+                abs(sys_value[1] - psutil_value.total) < TOLERANCE_DISK_USAGE
+            )
+            assert psutil_value.used == psutil_value.total - psutil_value.free
+
+    def test_disk_partitions(self):
+        sys_value = [
+            x + '\\'
+            for x in win32api.GetLogicalDriveStrings().split("\\\x00")
+            if x and not x.startswith('A:')
+        ]
+        psutil_value = [
+            x.mountpoint
+            for x in psutil.disk_partitions(all=True)
+            if not x.mountpoint.startswith('A:')
+        ]
+        assert sys_value == psutil_value
+
+    def test_net_if_stats(self):
+        ps_names = set(cext.net_if_stats())
+        wmi_adapters = wmi.WMI().Win32_NetworkAdapter()
+        wmi_names = set()
+        for wmi_adapter in wmi_adapters:
+            wmi_names.add(wmi_adapter.Name)
+            wmi_names.add(wmi_adapter.NetConnectionID)
+        assert (
+            ps_names & wmi_names
+        ), f"no common entries in {ps_names}, {wmi_names}"
+
+    def test_boot_time(self):
+        wmi_os = wmi.WMI().Win32_OperatingSystem()
+        wmi_btime_str = wmi_os[0].LastBootUpTime.split('.')[0]
+        wmi_btime_dt = datetime.datetime.strptime(
+            wmi_btime_str, "%Y%m%d%H%M%S"
+        )
+        psutil_dt = datetime.datetime.fromtimestamp(psutil.boot_time())
+        diff = abs((wmi_btime_dt - psutil_dt).total_seconds())
+        assert diff <= 5
+
+    def test_boot_time_fluctuation(self):
+        # https://github.com/giampaolo/psutil/issues/1007
+        with mock.patch('psutil._pswindows.cext.boot_time', return_value=5):
+            assert psutil.boot_time() == 5
+        with mock.patch('psutil._pswindows.cext.boot_time', return_value=4):
+            assert psutil.boot_time() == 5
+        with mock.patch('psutil._pswindows.cext.boot_time', return_value=6):
+            assert psutil.boot_time() == 5
+        with mock.patch('psutil._pswindows.cext.boot_time', return_value=333):
+            assert psutil.boot_time() == 333
+
+
+# ===================================================================
+# sensors_battery()
+# ===================================================================
+
+
+class TestSensorsBattery(WindowsTestCase):
+    def test_has_battery(self):
+        if win32api.GetPwrCapabilities()['SystemBatteriesPresent']:
+            assert psutil.sensors_battery() is not None
+        else:
+            assert psutil.sensors_battery() is None
+
+    @pytest.mark.skipif(not HAS_BATTERY, reason="no battery")
+    def test_percent(self):
+        w = wmi.WMI()
+        battery_wmi = w.query('select * from Win32_Battery')[0]
+        battery_psutil = psutil.sensors_battery()
+        assert (
+            abs(battery_psutil.percent - battery_wmi.EstimatedChargeRemaining)
+            < 1
+        )
+
+    @pytest.mark.skipif(not HAS_BATTERY, reason="no battery")
+    def test_power_plugged(self):
+        w = wmi.WMI()
+        battery_wmi = w.query('select * from Win32_Battery')[0]
+        battery_psutil = psutil.sensors_battery()
+        # Status codes:
+        # https://msdn.microsoft.com/en-us/library/aa394074(v=vs.85).aspx
+        assert battery_psutil.power_plugged == (battery_wmi.BatteryStatus == 2)
+
+    def test_emulate_no_battery(self):
+        with mock.patch(
+            "psutil._pswindows.cext.sensors_battery",
+            return_value=(0, 128, 0, 0),
+        ) as m:
+            assert psutil.sensors_battery() is None
+            assert m.called
+
+    def test_emulate_power_connected(self):
+        with mock.patch(
+            "psutil._pswindows.cext.sensors_battery", return_value=(1, 0, 0, 0)
+        ) as m:
+            assert (
+                psutil.sensors_battery().secsleft
+                == psutil.POWER_TIME_UNLIMITED
+            )
+            assert m.called
+
+    def test_emulate_power_charging(self):
+        with mock.patch(
+            "psutil._pswindows.cext.sensors_battery", return_value=(0, 8, 0, 0)
+        ) as m:
+            assert (
+                psutil.sensors_battery().secsleft
+                == psutil.POWER_TIME_UNLIMITED
+            )
+            assert m.called
+
+    def test_emulate_secs_left_unknown(self):
+        with mock.patch(
+            "psutil._pswindows.cext.sensors_battery",
+            return_value=(0, 0, 0, -1),
+        ) as m:
+            assert (
+                psutil.sensors_battery().secsleft == psutil.POWER_TIME_UNKNOWN
+            )
+            assert m.called
+
+
+# ===================================================================
+# Process APIs
+# ===================================================================
+
+
+class TestProcess(WindowsTestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.pid = spawn_testproc().pid
+
+    @classmethod
+    def tearDownClass(cls):
+        terminate(cls.pid)
+
+    def test_issue_24(self):
+        p = psutil.Process(0)
+        with pytest.raises(psutil.AccessDenied):
+            p.kill()
+
+    def test_special_pid(self):
+        p = psutil.Process(4)
+        assert p.name() == 'System'
+        # use __str__ to access all common Process properties to check
+        # that nothing strange happens
+        str(p)
+        p.username()
+        assert p.create_time() >= 0.0
+        try:
+            rss, _vms = p.memory_info()[:2]
+        except psutil.AccessDenied:
+            # expected on Windows Vista and Windows 7
+            if platform.uname()[1] not in {'vista', 'win-7', 'win7'}:
+                raise
+        else:
+            assert rss > 0
+
+    def test_send_signal(self):
+        p = psutil.Process(self.pid)
+        with pytest.raises(ValueError):
+            p.send_signal(signal.SIGINT)
+
+    def test_num_handles_increment(self):
+        p = psutil.Process(os.getpid())
+        before = p.num_handles()
+        handle = win32api.OpenProcess(
+            win32con.PROCESS_QUERY_INFORMATION, win32con.FALSE, os.getpid()
+        )
+        after = p.num_handles()
+        assert after == before + 1
+        win32api.CloseHandle(handle)
+        assert p.num_handles() == before
+
+    def test_ctrl_signals(self):
+        p = psutil.Process(self.spawn_testproc().pid)
+        p.send_signal(signal.CTRL_C_EVENT)
+        p.send_signal(signal.CTRL_BREAK_EVENT)
+        p.kill()
+        p.wait()
+        with pytest.raises(psutil.NoSuchProcess):
+            p.send_signal(signal.CTRL_C_EVENT)
+        with pytest.raises(psutil.NoSuchProcess):
+            p.send_signal(signal.CTRL_BREAK_EVENT)
+
+    def test_username(self):
+        name = win32api.GetUserNameEx(win32con.NameSamCompatible)
+        if name.endswith('$'):
+            # When running as a service account (most likely to be
+            # NetworkService), these user name calculations don't produce the
+            # same result, causing the test to fail.
+            raise pytest.skip('running as service account')
+        assert psutil.Process().username() == name
+
+    def test_cmdline(self):
+        sys_value = re.sub(r"[ ]+", " ", win32api.GetCommandLine()).strip()
+        psutil_value = ' '.join(psutil.Process().cmdline())
+        if sys_value[0] == '"' != psutil_value[0]:
+            # The PyWin32 command line may retain quotes around argv[0] if they
+            # were used unnecessarily, while psutil will omit them. So remove
+            # the first 2 quotes from sys_value if not in psutil_value.
+            # A path to an executable will not contain quotes, so this is safe.
+            sys_value = sys_value.replace('"', '', 2)
+        assert sys_value == psutil_value
+
+    # XXX - occasional failures
+
+    # def test_cpu_times(self):
+    #     handle = win32api.OpenProcess(win32con.PROCESS_QUERY_INFORMATION,
+    #                                   win32con.FALSE, os.getpid())
+    #     self.addCleanup(win32api.CloseHandle, handle)
+    #     sys_value = win32process.GetProcessTimes(handle)
+    #     psutil_value = psutil.Process().cpu_times()
+    #     self.assertAlmostEqual(
+    #         psutil_value.user, sys_value['UserTime'] / 10000000.0,
+    #         delta=0.2)
+    #     self.assertAlmostEqual(
+    #         psutil_value.user, sys_value['KernelTime'] / 10000000.0,
+    #         delta=0.2)
+
+    def test_nice(self):
+        handle = win32api.OpenProcess(
+            win32con.PROCESS_QUERY_INFORMATION, win32con.FALSE, os.getpid()
+        )
+        self.addCleanup(win32api.CloseHandle, handle)
+        sys_value = win32process.GetPriorityClass(handle)
+        psutil_value = psutil.Process().nice()
+        assert psutil_value == sys_value
+
+    def test_memory_info(self):
+        handle = win32api.OpenProcess(
+            win32con.PROCESS_QUERY_INFORMATION, win32con.FALSE, self.pid
+        )
+        self.addCleanup(win32api.CloseHandle, handle)
+        sys_value = win32process.GetProcessMemoryInfo(handle)
+        psutil_value = psutil.Process(self.pid).memory_info()
+        assert sys_value['PeakWorkingSetSize'] == psutil_value.peak_wset
+        assert sys_value['WorkingSetSize'] == psutil_value.wset
+        assert (
+            sys_value['QuotaPeakPagedPoolUsage']
+            == psutil_value.peak_paged_pool
+        )
+        assert sys_value['QuotaPagedPoolUsage'] == psutil_value.paged_pool
+        assert (
+            sys_value['QuotaPeakNonPagedPoolUsage']
+            == psutil_value.peak_nonpaged_pool
+        )
+        assert (
+            sys_value['QuotaNonPagedPoolUsage'] == psutil_value.nonpaged_pool
+        )
+        assert sys_value['PagefileUsage'] == psutil_value.pagefile
+        assert sys_value['PeakPagefileUsage'] == psutil_value.peak_pagefile
+
+        assert psutil_value.rss == psutil_value.wset
+        assert psutil_value.vms == psutil_value.pagefile
+
+    def test_wait(self):
+        handle = win32api.OpenProcess(
+            win32con.PROCESS_QUERY_INFORMATION, win32con.FALSE, self.pid
+        )
+        self.addCleanup(win32api.CloseHandle, handle)
+        p = psutil.Process(self.pid)
+        p.terminate()
+        psutil_value = p.wait()
+        sys_value = win32process.GetExitCodeProcess(handle)
+        assert psutil_value == sys_value
+
+    def test_cpu_affinity(self):
+        def from_bitmask(x):
+            return [i for i in range(64) if (1 << i) & x]
+
+        handle = win32api.OpenProcess(
+            win32con.PROCESS_QUERY_INFORMATION, win32con.FALSE, self.pid
+        )
+        self.addCleanup(win32api.CloseHandle, handle)
+        sys_value = from_bitmask(
+            win32process.GetProcessAffinityMask(handle)[0]
+        )
+        psutil_value = psutil.Process(self.pid).cpu_affinity()
+        assert psutil_value == sys_value
+
+    def test_io_counters(self):
+        handle = win32api.OpenProcess(
+            win32con.PROCESS_QUERY_INFORMATION, win32con.FALSE, os.getpid()
+        )
+        self.addCleanup(win32api.CloseHandle, handle)
+        sys_value = win32process.GetProcessIoCounters(handle)
+        psutil_value = psutil.Process().io_counters()
+        assert psutil_value.read_count == sys_value['ReadOperationCount']
+        assert psutil_value.write_count == sys_value['WriteOperationCount']
+        assert psutil_value.read_bytes == sys_value['ReadTransferCount']
+        assert psutil_value.write_bytes == sys_value['WriteTransferCount']
+        assert psutil_value.other_count == sys_value['OtherOperationCount']
+        assert psutil_value.other_bytes == sys_value['OtherTransferCount']
+
+    def test_num_handles(self):
+        import ctypes
+        import ctypes.wintypes
+
+        PROCESS_QUERY_INFORMATION = 0x400
+        handle = ctypes.windll.kernel32.OpenProcess(
+            PROCESS_QUERY_INFORMATION, 0, self.pid
+        )
+        self.addCleanup(ctypes.windll.kernel32.CloseHandle, handle)
+
+        hndcnt = ctypes.wintypes.DWORD()
+        ctypes.windll.kernel32.GetProcessHandleCount(
+            handle, ctypes.byref(hndcnt)
+        )
+        sys_value = hndcnt.value
+        psutil_value = psutil.Process(self.pid).num_handles()
+        assert psutil_value == sys_value
+
+    def test_error_partial_copy(self):
+        # https://github.com/giampaolo/psutil/issues/875
+        exc = OSError()
+        exc.winerror = 299
+        with mock.patch("psutil._psplatform.cext.proc_cwd", side_effect=exc):
+            with mock.patch("time.sleep") as m:
+                p = psutil.Process()
+                with pytest.raises(psutil.AccessDenied):
+                    p.cwd()
+        assert m.call_count >= 5
+
+    def test_exe(self):
+        # NtQuerySystemInformation succeeds if process is gone. Make sure
+        # it raises NSP for a non existent pid.
+        pid = psutil.pids()[-1] + 99999
+        proc = psutil._psplatform.Process(pid)
+        with pytest.raises(psutil.NoSuchProcess):
+            proc.exe()
+
+
+class TestProcessWMI(WindowsTestCase):
+    """Compare Process API results with WMI."""
+
+    @classmethod
+    def setUpClass(cls):
+        cls.pid = spawn_testproc().pid
+
+    @classmethod
+    def tearDownClass(cls):
+        terminate(cls.pid)
+
+    def test_name(self):
+        w = wmi.WMI().Win32_Process(ProcessId=self.pid)[0]
+        p = psutil.Process(self.pid)
+        assert p.name() == w.Caption
+
+    # This fail on github because using virtualenv for test environment
+    @pytest.mark.skipif(
+        GITHUB_ACTIONS, reason="unreliable path on GITHUB_ACTIONS"
+    )
+    def test_exe(self):
+        w = wmi.WMI().Win32_Process(ProcessId=self.pid)[0]
+        p = psutil.Process(self.pid)
+        # Note: wmi reports the exe as a lower case string.
+        # Being Windows paths case-insensitive we ignore that.
+        assert p.exe().lower() == w.ExecutablePath.lower()
+
+    def test_cmdline(self):
+        w = wmi.WMI().Win32_Process(ProcessId=self.pid)[0]
+        p = psutil.Process(self.pid)
+        assert ' '.join(p.cmdline()) == w.CommandLine.replace('"', '')
+
+    def test_username(self):
+        w = wmi.WMI().Win32_Process(ProcessId=self.pid)[0]
+        p = psutil.Process(self.pid)
+        domain, _, username = w.GetOwner()
+        username = f"{domain}\\{username}"
+        assert p.username() == username
+
+    @retry_on_failure()
+    def test_memory_rss(self):
+        w = wmi.WMI().Win32_Process(ProcessId=self.pid)[0]
+        p = psutil.Process(self.pid)
+        rss = p.memory_info().rss
+        assert rss == int(w.WorkingSetSize)
+
+    @retry_on_failure()
+    def test_memory_vms(self):
+        w = wmi.WMI().Win32_Process(ProcessId=self.pid)[0]
+        p = psutil.Process(self.pid)
+        vms = p.memory_info().vms
+        # http://msdn.microsoft.com/en-us/library/aa394372(VS.85).aspx
+        # ...claims that PageFileUsage is represented in Kilo
+        # bytes but funnily enough on certain platforms bytes are
+        # returned instead.
+        wmi_usage = int(w.PageFileUsage)
+        if vms not in {wmi_usage, wmi_usage * 1024}:
+            raise self.fail(f"wmi={wmi_usage}, psutil={vms}")
+
+    def test_create_time(self):
+        w = wmi.WMI().Win32_Process(ProcessId=self.pid)[0]
+        p = psutil.Process(self.pid)
+        wmic_create = str(w.CreationDate.split('.')[0])
+        psutil_create = time.strftime(
+            "%Y%m%d%H%M%S", time.localtime(p.create_time())
+        )
+        assert wmic_create == psutil_create
+
+
+# ---
+
+
+@pytest.mark.skipif(not WINDOWS, reason="WINDOWS only")
+class TestDualProcessImplementation(PsutilTestCase):
+    """Certain APIs on Windows have 2 internal implementations, one
+    based on documented Windows APIs, another one based
+    NtQuerySystemInformation() which gets called as fallback in
+    case the first fails because of limited permission error.
+    Here we test that the two methods return the exact same value,
+    see:
+    https://github.com/giampaolo/psutil/issues/304.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        cls.pid = spawn_testproc().pid
+
+    @classmethod
+    def tearDownClass(cls):
+        terminate(cls.pid)
+
+    def test_memory_info(self):
+        mem_1 = psutil.Process(self.pid).memory_info()
+        with mock.patch(
+            "psutil._psplatform.cext.proc_memory_info",
+            side_effect=PermissionError,
+        ) as fun:
+            mem_2 = psutil.Process(self.pid).memory_info()
+            assert len(mem_1) == len(mem_2)
+            for i in range(len(mem_1)):
+                assert mem_1[i] >= 0
+                assert mem_2[i] >= 0
+                assert abs(mem_1[i] - mem_2[i]) < 512
+            assert fun.called
+
+    def test_create_time(self):
+        ctime = psutil.Process(self.pid).create_time()
+        with mock.patch(
+            "psutil._psplatform.cext.proc_times",
+            side_effect=PermissionError,
+        ) as fun:
+            assert psutil.Process(self.pid).create_time() == ctime
+            assert fun.called
+
+    def test_cpu_times(self):
+        cpu_times_1 = psutil.Process(self.pid).cpu_times()
+        with mock.patch(
+            "psutil._psplatform.cext.proc_times",
+            side_effect=PermissionError,
+        ) as fun:
+            cpu_times_2 = psutil.Process(self.pid).cpu_times()
+            assert fun.called
+            assert abs(cpu_times_1.user - cpu_times_2.user) < 0.01
+            assert abs(cpu_times_1.system - cpu_times_2.system) < 0.01
+
+    def test_io_counters(self):
+        io_counters_1 = psutil.Process(self.pid).io_counters()
+        with mock.patch(
+            "psutil._psplatform.cext.proc_io_counters",
+            side_effect=PermissionError,
+        ) as fun:
+            io_counters_2 = psutil.Process(self.pid).io_counters()
+            for i in range(len(io_counters_1)):
+                assert abs(io_counters_1[i] - io_counters_2[i]) < 5
+            assert fun.called
+
+    def test_num_handles(self):
+        num_handles = psutil.Process(self.pid).num_handles()
+        with mock.patch(
+            "psutil._psplatform.cext.proc_num_handles",
+            side_effect=PermissionError,
+        ) as fun:
+            assert psutil.Process(self.pid).num_handles() == num_handles
+            assert fun.called
+
+    def test_cmdline(self):
+        for pid in psutil.pids():
+            try:
+                a = cext.proc_cmdline(pid, use_peb=True)
+                b = cext.proc_cmdline(pid, use_peb=False)
+            except OSError as err:
+                err = convert_oserror(err)
+                if not isinstance(
+                    err, (psutil.AccessDenied, psutil.NoSuchProcess)
+                ):
+                    raise
+            else:
+                assert a == b
+
+
+@pytest.mark.skipif(not WINDOWS, reason="WINDOWS only")
+class RemoteProcessTestCase(PsutilTestCase):
+    """Certain functions require calling ReadProcessMemory.
+    This trivially works when called on the current process.
+    Check that this works on other processes, especially when they
+    have a different bitness.
+    """
+
+    @staticmethod
+    def find_other_interpreter():
+        # find a python interpreter that is of the opposite bitness from us
+        code = "import sys; sys.stdout.write(str(sys.maxsize > 2**32))"
+
+        # XXX: a different and probably more stable approach might be to access
+        # the registry but accessing 64 bit paths from a 32 bit process
+        for filename in glob.glob(r"C:\Python*\python.exe"):
+            proc = subprocess.Popen(
+                args=[filename, "-c", code],
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+            )
+            output, _ = proc.communicate()
+            proc.wait()
+            if output == str(not IS_64BIT):
+                return filename
+
+    test_args = ["-c", "import sys; sys.stdin.read()"]
+
+    def setUp(self):
+        super().setUp()
+
+        other_python = self.find_other_interpreter()
+        if other_python is None:
+            raise pytest.skip(
+                "could not find interpreter with opposite bitness"
+            )
+        if IS_64BIT:
+            self.python64 = sys.executable
+            self.python32 = other_python
+        else:
+            self.python64 = other_python
+            self.python32 = sys.executable
+
+        env = os.environ.copy()
+        env["THINK_OF_A_NUMBER"] = str(os.getpid())
+        self.proc32 = self.spawn_testproc(
+            [self.python32] + self.test_args, env=env, stdin=subprocess.PIPE
+        )
+        self.proc64 = self.spawn_testproc(
+            [self.python64] + self.test_args, env=env, stdin=subprocess.PIPE
+        )
+
+    def tearDown(self):
+        super().tearDown()
+        self.proc32.communicate()
+        self.proc64.communicate()
+
+    def test_cmdline_32(self):
+        p = psutil.Process(self.proc32.pid)
+        assert len(p.cmdline()) == 3
+        assert p.cmdline()[1:] == self.test_args
+
+    def test_cmdline_64(self):
+        p = psutil.Process(self.proc64.pid)
+        assert len(p.cmdline()) == 3
+        assert p.cmdline()[1:] == self.test_args
+
+    def test_cwd_32(self):
+        p = psutil.Process(self.proc32.pid)
+        assert p.cwd() == os.getcwd()
+
+    def test_cwd_64(self):
+        p = psutil.Process(self.proc64.pid)
+        assert p.cwd() == os.getcwd()
+
+    def test_environ_32(self):
+        p = psutil.Process(self.proc32.pid)
+        e = p.environ()
+        assert "THINK_OF_A_NUMBER" in e
+        assert e["THINK_OF_A_NUMBER"] == str(os.getpid())
+
+    def test_environ_64(self):
+        p = psutil.Process(self.proc64.pid)
+        try:
+            p.environ()
+        except psutil.AccessDenied:
+            pass
+
+
+# ===================================================================
+# Windows services
+# ===================================================================
+
+
+@pytest.mark.skipif(not WINDOWS, reason="WINDOWS only")
+class TestServices(PsutilTestCase):
+    def test_win_service_iter(self):
+        valid_statuses = {
+            "running",
+            "paused",
+            "start",
+            "pause",
+            "continue",
+            "stop",
+            "stopped",
+        }
+        valid_start_types = {"automatic", "manual", "disabled"}
+        valid_statuses = {
+            "running",
+            "paused",
+            "start_pending",
+            "pause_pending",
+            "continue_pending",
+            "stop_pending",
+            "stopped",
+        }
+        for serv in psutil.win_service_iter():
+            data = serv.as_dict()
+            assert isinstance(data['name'], str)
+            assert data['name'].strip()
+            assert isinstance(data['display_name'], str)
+            assert isinstance(data['username'], str)
+            assert data['status'] in valid_statuses
+            if data['pid'] is not None:
+                psutil.Process(data['pid'])
+            assert isinstance(data['binpath'], str)
+            assert isinstance(data['username'], str)
+            assert isinstance(data['start_type'], str)
+            assert data['start_type'] in valid_start_types
+            assert data['status'] in valid_statuses
+            assert isinstance(data['description'], str)
+            pid = serv.pid()
+            if pid is not None:
+                p = psutil.Process(pid)
+                assert p.is_running()
+            # win_service_get
+            s = psutil.win_service_get(serv.name())
+            # test __eq__
+            assert serv == s
+
+    def test_win_service_get(self):
+        ERROR_SERVICE_DOES_NOT_EXIST = (
+            psutil._psplatform.cext.ERROR_SERVICE_DOES_NOT_EXIST
+        )
+        ERROR_ACCESS_DENIED = psutil._psplatform.cext.ERROR_ACCESS_DENIED
+
+        name = next(psutil.win_service_iter()).name()
+        with pytest.raises(psutil.NoSuchProcess) as cm:
+            psutil.win_service_get(name + '???')
+        assert cm.value.name == name + '???'
+
+        # test NoSuchProcess
+        service = psutil.win_service_get(name)
+        exc = OSError(0, "msg", 0)
+        exc.winerror = ERROR_SERVICE_DOES_NOT_EXIST
+        with mock.patch(
+            "psutil._psplatform.cext.winservice_query_status", side_effect=exc
+        ):
+            with pytest.raises(psutil.NoSuchProcess):
+                service.status()
+        with mock.patch(
+            "psutil._psplatform.cext.winservice_query_config", side_effect=exc
+        ):
+            with pytest.raises(psutil.NoSuchProcess):
+                service.username()
+
+        # test AccessDenied
+        exc = OSError(0, "msg", 0)
+        exc.winerror = ERROR_ACCESS_DENIED
+        with mock.patch(
+            "psutil._psplatform.cext.winservice_query_status", side_effect=exc
+        ):
+            with pytest.raises(psutil.AccessDenied):
+                service.status()
+        with mock.patch(
+            "psutil._psplatform.cext.winservice_query_config", side_effect=exc
+        ):
+            with pytest.raises(psutil.AccessDenied):
+                service.username()
+
+        # test __str__ and __repr__
+        assert service.name() in str(service)
+        assert service.display_name() in str(service)
+        assert service.name() in repr(service)
+        assert service.display_name() in repr(service)
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diff --git a/tool_server/.venv/lib/python3.12/site-packages/pygments-2.19.2.dist-info/licenses/AUTHORS b/tool_server/.venv/lib/python3.12/site-packages/pygments-2.19.2.dist-info/licenses/AUTHORS
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+++ b/tool_server/.venv/lib/python3.12/site-packages/pygments-2.19.2.dist-info/licenses/AUTHORS
@@ -0,0 +1,291 @@
+Pygments is written and maintained by Georg Brandl <georg@python.org>.
+
+Major developers are Tim Hatch <tim@timhatch.com> and Armin Ronacher
+<armin.ronacher@active-4.com>.
+
+Other contributors, listed alphabetically, are:
+
+* Sam Aaron -- Ioke lexer
+* Jean Abou Samra -- LilyPond lexer
+* João Abecasis -- JSLT lexer
+* Ali Afshar -- image formatter
+* Thomas Aglassinger -- Easytrieve, JCL, Rexx, Transact-SQL and VBScript
+  lexers
+* Maxence Ahlouche -- PostgreSQL Explain lexer
+* Muthiah Annamalai -- Ezhil lexer
+* Nikolay Antipov -- OpenSCAD lexer
+* Kumar Appaiah -- Debian control lexer
+* Andreas Amann -- AppleScript lexer
+* Timothy Armstrong -- Dart lexer fixes
+* Jeffrey Arnold -- R/S, Rd, BUGS, Jags, and Stan lexers
+* Eiríkr Åsheim -- Uxntal lexer
+* Jeremy Ashkenas -- CoffeeScript lexer
+* José Joaquín Atria -- Praat lexer
+* Stefan Matthias Aust -- Smalltalk lexer
+* Lucas Bajolet -- Nit lexer
+* Ben Bangert -- Mako lexers
+* Max Battcher -- Darcs patch lexer
+* Thomas Baruchel -- APL lexer
+* Tim Baumann -- (Literate) Agda lexer
+* Paul Baumgart, 280 North, Inc. -- Objective-J lexer
+* Michael Bayer -- Myghty lexers
+* Thomas Beale -- Archetype lexers
+* John Benediktsson -- Factor lexer
+* David Benjamin, Google LLC -- TLS lexer
+* Trevor Bergeron -- mIRC formatter
+* Vincent Bernat -- LessCSS lexer
+* Christopher Bertels -- Fancy lexer
+* Sébastien Bigaret -- QVT Operational lexer
+* Jarrett Billingsley -- MiniD lexer
+* Adam Blinkinsop -- Haskell, Redcode lexers
+* Stéphane Blondon -- Procfile, SGF and Sieve lexers
+* Frits van Bommel -- assembler lexers
+* Pierre Bourdon -- bugfixes
+* Martijn Braam -- Kernel log lexer, BARE lexer
+* JD Browne, Google LLC -- GoogleSQL lexer
+* Matthias Bussonnier -- ANSI style handling for terminal-256 formatter
+* chebee7i -- Python traceback lexer improvements
+* Hiram Chirino -- Scaml and Jade lexers
+* Mauricio Caceres -- SAS and Stata lexers.
+* Michael Camilleri, John Gabriele, sogaiu -- Janet lexer
+* Daren Chandisingh -- Gleam lexer
+* Ian Cooper -- VGL lexer
+* David Corbett -- Inform, Jasmin, JSGF, Snowball, and TADS 3 lexers
+* Leaf Corcoran -- MoonScript lexer
+* Fraser Cormack -- TableGen lexer
+* Gabriel Corona -- ASN.1 lexer
+* Christopher Creutzig -- MuPAD lexer
+* Daniël W. Crompton -- Pike lexer
+* Pete Curry -- bugfixes
+* Bryan Davis -- EBNF lexer
+* Bruno Deferrari -- Shen lexer
+* Walter Dörwald -- UL4 lexer
+* Luke Drummond -- Meson lexer
+* Giedrius Dubinskas -- HTML formatter improvements
+* Owen Durni -- Haxe lexer
+* Alexander Dutton, Oxford University Computing Services -- SPARQL lexer
+* James Edwards -- Terraform lexer
+* Nick Efford -- Python 3 lexer
+* Sven Efftinge -- Xtend lexer
+* Artem Egorkine -- terminal256 formatter
+* Matthew Fernandez -- CAmkES lexer
+* Paweł Fertyk -- GDScript lexer, HTML formatter improvements
+* Michael Ficarra -- CPSA lexer
+* James H. Fisher -- PostScript lexer
+* Amanda Fitch, Google LLC -- GoogleSQL lexer
+* William S. Fulton -- SWIG lexer
+* Carlos Galdino -- Elixir and Elixir Console lexers
+* Michael Galloy -- IDL lexer
+* Naveen Garg -- Autohotkey lexer
+* Simon Garnotel -- FreeFem++ lexer
+* Laurent Gautier -- R/S lexer
+* Alex Gaynor -- PyPy log lexer
+* Richard Gerkin -- Igor Pro lexer
+* Alain Gilbert -- TypeScript lexer
+* Alex Gilding -- BlitzBasic lexer
+* GitHub, Inc -- DASM16, Augeas, TOML, and Slash lexers
+* Bertrand Goetzmann -- Groovy lexer
+* Krzysiek Goj -- Scala lexer
+* Rostyslav Golda -- FloScript lexer
+* Andrey Golovizin -- BibTeX lexers
+* Matt Good -- Genshi, Cheetah lexers
+* Michał Górny -- vim modeline support
+* Alex Gosse -- TrafficScript lexer
+* Patrick Gotthardt -- PHP namespaces support
+* Hubert Gruniaux -- C and C++ lexer improvements
+* Olivier Guibe -- Asymptote lexer
+* Phil Hagelberg -- Fennel lexer
+* Florian Hahn -- Boogie lexer
+* Martin Harriman -- SNOBOL lexer
+* Matthew Harrison -- SVG formatter
+* Steven Hazel -- Tcl lexer
+* Dan Michael Heggø -- Turtle lexer
+* Aslak Hellesøy -- Gherkin lexer
+* Greg Hendershott -- Racket lexer
+* Justin Hendrick -- ParaSail lexer
+* Jordi Gutiérrez Hermoso -- Octave lexer
+* David Hess, Fish Software, Inc. -- Objective-J lexer
+* Ken Hilton -- Typographic Number Theory and Arrow lexers
+* Varun Hiremath -- Debian control lexer
+* Rob Hoelz -- Perl 6 lexer
+* Doug Hogan -- Mscgen lexer
+* Ben Hollis -- Mason lexer
+* Max Horn -- GAP lexer
+* Fred Hornsey -- OMG IDL Lexer
+* Alastair Houghton -- Lexer inheritance facility
+* Tim Howard -- BlitzMax lexer
+* Dustin Howett -- Logos lexer
+* Ivan Inozemtsev -- Fantom lexer
+* Hiroaki Itoh -- Shell console rewrite, Lexers for PowerShell session,
+  MSDOS session, BC, WDiff
+* Brian R. Jackson -- Tea lexer
+* Christian Jann -- ShellSession lexer
+* Jonas Camillus Jeppesen -- Line numbers and line highlighting for 
+  RTF-formatter
+* Dennis Kaarsemaker -- sources.list lexer
+* Dmitri Kabak -- Inferno Limbo lexer
+* Igor Kalnitsky -- vhdl lexer
+* Colin Kennedy - USD lexer
+* Alexander Kit -- MaskJS lexer
+* Pekka Klärck -- Robot Framework lexer
+* Gerwin Klein -- Isabelle lexer
+* Eric Knibbe -- Lasso lexer
+* Stepan Koltsov -- Clay lexer
+* Oliver Kopp - Friendly grayscale style
+* Adam Koprowski -- Opa lexer
+* Benjamin Kowarsch -- Modula-2 lexer
+* Domen Kožar -- Nix lexer
+* Oleh Krekel -- Emacs Lisp lexer
+* Alexander Kriegisch -- Kconfig and AspectJ lexers
+* Marek Kubica -- Scheme lexer
+* Jochen Kupperschmidt -- Markdown processor
+* Gerd Kurzbach -- Modelica lexer
+* Jon Larimer, Google Inc. -- Smali lexer
+* Olov Lassus -- Dart lexer
+* Matt Layman -- TAP lexer
+* Dan Lazin, Google LLC -- GoogleSQL lexer
+* Kristian Lyngstøl -- Varnish lexers
+* Sylvestre Ledru -- Scilab lexer
+* Chee Sing Lee -- Flatline lexer
+* Mark Lee -- Vala lexer
+* Thomas Linder Puls -- Visual Prolog lexer
+* Pete Lomax -- Phix lexer
+* Valentin Lorentz -- C++ lexer improvements
+* Ben Mabey -- Gherkin lexer
+* Angus MacArthur -- QML lexer
+* Louis Mandel -- X10 lexer
+* Louis Marchand -- Eiffel lexer
+* Simone Margaritelli -- Hybris lexer
+* Tim Martin - World of Warcraft TOC lexer
+* Kirk McDonald -- D lexer
+* Gordon McGregor -- SystemVerilog lexer
+* Stephen McKamey -- Duel/JBST lexer
+* Brian McKenna -- F# lexer
+* Charles McLaughlin -- Puppet lexer
+* Kurt McKee -- Tera Term macro lexer, PostgreSQL updates, MySQL overhaul, JSON lexer
+* Joe Eli McIlvain -- Savi lexer
+* Lukas Meuser -- BBCode formatter, Lua lexer
+* Cat Miller -- Pig lexer
+* Paul Miller -- LiveScript lexer
+* Hong Minhee -- HTTP lexer
+* Michael Mior -- Awk lexer
+* Bruce Mitchener -- Dylan lexer rewrite
+* Reuben Morais -- SourcePawn lexer
+* Jon Morton -- Rust lexer
+* Paulo Moura -- Logtalk lexer
+* Mher Movsisyan -- DTD lexer
+* Dejan Muhamedagic -- Crmsh lexer
+* Adrien Nayrat -- PostgreSQL Explain lexer
+* Ana Nelson -- Ragel, ANTLR, R console lexers
+* David Neto, Google LLC -- WebGPU Shading Language lexer
+* Kurt Neufeld -- Markdown lexer
+* Nam T. Nguyen -- Monokai style
+* Jesper Noehr -- HTML formatter "anchorlinenos"
+* Mike Nolta -- Julia lexer
+* Avery Nortonsmith -- Pointless lexer
+* Jonas Obrist -- BBCode lexer
+* Edward O'Callaghan -- Cryptol lexer
+* David Oliva -- Rebol lexer
+* Pat Pannuto -- nesC lexer
+* Jon Parise -- Protocol buffers and Thrift lexers
+* Benjamin Peterson -- Test suite refactoring
+* Ronny Pfannschmidt -- BBCode lexer
+* Dominik Picheta -- Nimrod lexer
+* Andrew Pinkham -- RTF Formatter Refactoring
+* Clément Prévost -- UrbiScript lexer
+* Tanner Prynn -- cmdline -x option and loading lexers from files
+* Oleh Prypin -- Crystal lexer (based on Ruby lexer)
+* Nick Psaris -- K and Q lexers
+* Xidorn Quan -- Web IDL lexer
+* Elias Rabel -- Fortran fixed form lexer
+* raichoo -- Idris lexer
+* Daniel Ramirez -- GDScript lexer
+* Kashif Rasul -- CUDA lexer
+* Nathan Reed -- HLSL lexer
+* Justin Reidy -- MXML lexer
+* Jonathon Reinhart, Google LLC -- Soong lexer
+* Norman Richards -- JSON lexer
+* Corey Richardson -- Rust lexer updates
+* Fabrizio Riguzzi -- cplint leder
+* Lubomir Rintel -- GoodData MAQL and CL lexers
+* Andre Roberge -- Tango style
+* Georg Rollinger -- HSAIL lexer
+* Michiel Roos -- TypoScript lexer
+* Konrad Rudolph -- LaTeX formatter enhancements
+* Mario Ruggier -- Evoque lexers
+* Miikka Salminen -- Lovelace style, Hexdump lexer, lexer enhancements
+* Stou Sandalski -- NumPy, FORTRAN, tcsh and XSLT lexers
+* Matteo Sasso -- Common Lisp lexer
+* Joe Schafer -- Ada lexer
+* Max Schillinger -- TiddlyWiki5 lexer
+* Andrew Schmidt -- X++ lexer
+* Ken Schutte -- Matlab lexers
+* René Schwaiger -- Rainbow Dash style
+* Sebastian Schweizer -- Whiley lexer
+* Tassilo Schweyer -- Io, MOOCode lexers
+* Pablo Seminario -- PromQL lexer
+* Ted Shaw -- AutoIt lexer
+* Joerg Sieker -- ABAP lexer
+* Robert Simmons -- Standard ML lexer
+* Kirill Simonov -- YAML lexer
+* Corbin Simpson -- Monte lexer
+* Ville Skyttä -- ASCII armored lexer
+* Alexander Smishlajev -- Visual FoxPro lexer
+* Steve Spigarelli -- XQuery lexer
+* Jerome St-Louis -- eC lexer
+* Camil Staps -- Clean and NuSMV lexers; Solarized style
+* James Strachan -- Kotlin lexer
+* Tom Stuart -- Treetop lexer
+* Colin Sullivan -- SuperCollider lexer
+* Ben Swift -- Extempore lexer
+* tatt61880 -- Kuin lexer
+* Edoardo Tenani -- Arduino lexer
+* Tiberius Teng -- default style overhaul
+* Jeremy Thurgood -- Erlang, Squid config lexers
+* Brian Tiffin -- OpenCOBOL lexer
+* Bob Tolbert -- Hy lexer
+* Doug Torrance -- Macaulay2 lexer
+* Matthias Trute -- Forth lexer
+* Tuoa Spi T4 -- Bdd lexer
+* Erick Tryzelaar -- Felix lexer
+* Alexander Udalov -- Kotlin lexer improvements
+* Thomas Van Doren -- Chapel lexer
+* Dave Van Ee -- Uxntal lexer updates
+* Daniele Varrazzo -- PostgreSQL lexers
+* Abe Voelker -- OpenEdge ABL lexer
+* Pepijn de Vos -- HTML formatter CTags support
+* Matthias Vallentin -- Bro lexer
+* Benoît Vinot -- AMPL lexer
+* Linh Vu Hong -- RSL lexer
+* Taavi Väänänen -- Debian control lexer
+* Immanuel Washington -- Smithy lexer
+* Nathan Weizenbaum -- Haml and Sass lexers
+* Nathan Whetsell -- Csound lexers
+* Dietmar Winkler -- Modelica lexer
+* Nils Winter -- Smalltalk lexer
+* Davy Wybiral -- Clojure lexer
+* Whitney Young -- ObjectiveC lexer
+* Diego Zamboni -- CFengine3 lexer
+* Enrique Zamudio -- Ceylon lexer
+* Alex Zimin -- Nemerle lexer
+* Rob Zimmerman -- Kal lexer
+* Evgenii Zheltonozhskii -- Maple lexer
+* Vincent Zurczak -- Roboconf lexer
+* Hubert Gruniaux -- C and C++ lexer improvements
+* Thomas Symalla -- AMDGPU Lexer
+* 15b3 -- Image Formatter improvements
+* Fabian Neumann -- CDDL lexer
+* Thomas Duboucher -- CDDL lexer
+* Philipp Imhof -- Pango Markup formatter
+* Thomas Voss -- Sed lexer
+* Martin Fischer -- WCAG contrast testing
+* Marc Auberer -- Spice lexer
+* Amr Hesham -- Carbon lexer
+* diskdance -- Wikitext lexer
+* vanillajonathan -- PRQL lexer
+* Nikolay Antipov -- OpenSCAD lexer
+* Markus Meyer, Nextron Systems -- YARA lexer
+* Hannes Römer -- Mojo lexer
+* Jan Frederik Schaefer -- PDDL lexer
+
+Many thanks for all contributions!
diff --git a/tool_server/.venv/lib/python3.12/site-packages/pygments-2.19.2.dist-info/licenses/LICENSE b/tool_server/.venv/lib/python3.12/site-packages/pygments-2.19.2.dist-info/licenses/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..446a1a805c8a949579fb8a9799f2bec7777349dc
--- /dev/null
+++ b/tool_server/.venv/lib/python3.12/site-packages/pygments-2.19.2.dist-info/licenses/LICENSE
@@ -0,0 +1,25 @@
+Copyright (c) 2006-2022 by the respective authors (see AUTHORS file).
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+* Redistributions of source code must retain the above copyright
+  notice, this list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright
+  notice, this list of conditions and the following disclaimer in the
+  documentation and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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